Abstract

A computer system for maintaining the security of a building is provided. The computer system includes a processor and a memory. The processor is configured to: (i) receive data from a plurality of sensors; (ii) apply the data to a trained security assessment model to generate at least one model output, the trained security assessment model configured to compare the data from the plurality of sensors to a plurality of danger profiles; (iii) determine an anomalous event is occurring and a security response to the anomalous event based on the at least one model output, the security response including a plurality of tasks; (iv) trigger a first task of the plurality of tasks; and/or (v) in response to determining a predetermined period of time has elapsed, trigger a second task of the plurality of tasks.

IPC Classes  ?

• G08B 13/00 - Burglar, theft or intruder alarms
• G06N 20/00 - Machine learning
• G08B 21/12 - Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
• G08B 31/00 - Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

This disclosure describes systems, devices, and techniques for determining a root cause of anomalous events in a networked computing environment. A node detects an alert corresponding to an anomalous event during a time period. The alert is correlated with previously detected alerts occurring within the time period and a causal relationship associated with nodes in the networked computing environment. The node may then recursively identify a root cause of the anomalous event detected in the networked computing environment based on a set of correlated alerts. An incident ticket may then be sent to the node identified as the root cause of the anomalous event, and the node may notify other nodes in the network having a causal relationship with the node of the anomalous event.

IPC Classes  ?

• H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
• G06N 20/00 - Machine learning
• H04L 41/12 - Discovery or management of network topologies
• H04L 41/5074 - Handling of user complaints or trouble tickets

Abstract

A system that includes a failed test detector and a task updater can automatically update tasks associated with consistent failures of software tests in a software development management platform. The failed test detector can use a set of evidence of test (EOT) files that indicate software testing results over a period of time to identify tests that are consistently failing when executed against versions of a software application. The task updater can automatically create tasks associated with such consistently-failing tests in the software development management platform. The task updater can also automatically close existing tasks associated with tests, in the software development management system, if the failed test detector determines that those tests are no longer failing consistently.

IPC Classes  ?

• G06F 11/3668 - Testing of software

Abstract

Systems and methods for determining the performance of a driver of a vehicle based on changes, over time, in the context and environment in which the vehicle operates, and any resultant driver behavior are disclosed. A set of driver response data is created from based on an analysis of time-series data indicative of the driver’s operation of the vehicle in conjunction with time-series data indicative of changes in the vehicle’s context/environment. The driver response data indicates the types and magnitudes of the driver’s responses to various changes in the vehicle’s operating context/environment and the driver’s time-to-respond for each of the responses. That is, the driver response data indicates how a driver compensated his or her behavior (if at all) in response to different changes in the vehicle’s context and/or environment. The driver response data may be compared to one or more thresholds to determine the driver’s performance.

IPC Classes  ?

• B60W 40/09 - Driving style or behaviour
• B60W 40/04 - Traffic conditions
• B60W 40/06 - Road conditions
• B60W 50/12 - Limiting control by the driver depending on vehicle state, e.g. interlocking means for the control input for preventing unsafe operation
• G07C 5/00 - Registering or indicating the working of vehicles
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• G08G 1/0967 - Systems involving transmission of highway information, e.g. weather, speed limits

Abstract

Methods and systems for alerting a user of accident-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors and from a user device associated with the user, location data of the user device; (2) loading, by the one or more processors, the accident density map surrounding a location of the user device based upon the location data, wherein the accident density map is generated by the chatbot, the chatbot being trained with a plurality of training accident density maps and historical or hypothetical accident data associated with the plurality of training accident density maps; (3) determining, by the one or more processors, based upon the accident density map, an accident-prone location within the accident density map; and (4) presenting, by the one or more processors via the user device, an indication of the accident-prone location.

IPC Classes  ?

• G08G 1/14 - Traffic control systems for road vehicles indicating individual free spaces in parking areas
• G01C 21/36 - Input/output arrangements for on-board computers
• G06F 40/40 - Processing or translation of natural language
• G06Q 40/08 - Insurance
• G08G 1/0967 - Systems involving transmission of highway information, e.g. weather, speed limits

Abstract

A computer system is provided and is programmed to: (1) receive an audible caller statement originating from a caller being routed to the representative; (2) parse the audible caller statement; (3) identify one or more key words from the parsed audible caller statement; (4) search an information database for one or more items of information based upon the one or more key words; (5) identify at least one of the one or more items of information to present to the representative; and (6) transmit the at least one identified items of information to be presented to the representative.

IPC Classes  ?

• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/06 - Creation of reference templatesTraining of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G10L 15/08 - Speech classification or search
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
• G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention

Abstract

A computer system is provided that detects anomalies, and is programmed to (i) access one or more artificial intelligence (AI) models trained to analyze data and identify data anomalies associated with the data, (ii) receive a plurality of initial user data, (iii) receive at least one new user data, (iv) input the at least one new user data into one or more AI models to detect one or more anomalies within the received new user data by comparison to the plurality of initial user data, and (v) in response to the one or more AI models detecting one or more data anomalies within the at least one new user data, transmit one or more notifications to a user computing device including a message identifying the detected data anomaly.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

Embodiments described herein receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by an owner of the vehicle during the period of time; analyze the telematics data to identify driving behavior(s) of the owner during the period of time; predict one or more user preference values of a vehicle-sharing platform profile of the owner based on the identified one or more driving behaviors, wherein the one or more user preference values define one or more criteria for vehicle renters with whom the first vehicle can be shared; apply the one or more criteria to a potential vehicle renter; and cause an indication of the first vehicle of the owner to be displayed via a mobile device of the potential renter only if the potential vehicle renter satisfies the one or more criteria.

IPC Classes  ?

• B60W 40/09 - Driving style or behaviour
• G06Q 30/0645 - Rental transactionsLeasing transactions
• G06Q 50/40 - Business processes related to the transportation industry
• G07C 5/00 - Registering or indicating the working of vehicles

Abstract

A computer-implemented auditory biometric method generates a playlist file including at least one audio file. The method includes receiving user biometric data and applying user biometric data to a trained auditory biometric model to generate a playlist file. The auditory biometric model may be trained using training data including a plurality of historic records associated with a plurality of historic users. The method may include transmitting a playlist message including the playlist file to a user computer device for execution by the user computer device.

IPC Classes  ?

• G06F 16/638 - Presentation of query results
• G06N 5/022 - Knowledge engineeringKnowledge acquisition

Abstract

The method comprises receiving a contact identifier and a time identifier, retrieving one or more contact logs corresponding to the contact identifier and the time identifier, segmenting the one or more contact logs into a plurality of contact log segments based on a contact flow identifier for each of the one or more contact logs, for each contact log segment, retrieving a plurality of contact flows based on the contact flow identifier of the contact log segment, partitioning the plurality of contact log segments based on a block identifier for each of the plurality of blocks, analyzing the one or more partitioned contact log segments based on the plurality of contact flows to determine a contact path, creating a visual representation of the contact path, and outputting the visual representation to a user interface of a user device.

IPC Classes  ?

• H04M 3/22 - Arrangements for supervision, monitoring or testing
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention

Abstract

Methods, systems, and computer readable media for predictively determining a risk of damage to a property are provided. To determine the risk, a high resolution virtual model of a region that includes the property is obtained. The virtual model is imported into a simulation environment. One or more of the simulation parameters are set based on historic weather data for the region. For example, each parameter may be associated with a probability distribution derived based on the historic weather data that is sampled prior to executing the simulation. One or more simulations are executed in accordance with the sampled inputs to simulate the likely weather patterns the property will experience. The result of the simulation is analyzed to determine the predicted risk of damage to the property.

IPC Classes  ?

• G06Q 40/08 - Insurance

Abstract

A computer system is provided that detects anomalies, and is programmed to: (i) access one or more artificial intelligence (AI) models trained to analyze an image and identify data anomalies associated with the image, (ii) receive at least one image for analysis, (iii) input the at least one received image into the one or more AI models to determine whether the at least one received image includes one or more data anomalies, and (iv) in response to the one or more AI models detecting one or more data anomalies within the at least one received image, transmit one or more notifications to a user computing device including a message identifying the detected data anomaly.

IPC Classes  ?

• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting

Abstract

A computer system is provided that detects anomalies, and is programmed to: (i) access one or more artificial intelligence (AI) models trained to analyze appraisals to identify anomalies associated with the appraisals, (ii) store a plurality of initial appraisals, (iii) receive at least one appraisal verification request including appraisal data, (iv) input the at least one appraisal verification request into the one or more AI models to determine whether the at least one appraisal verification request includes one or more data anomalies, and (v) in response to the one or more AI models detecting one or more data anomalies within the at least one appraisal verification request, transmit one or more notifications to a user computing device including a message identifying the detected data anomaly.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06Q 30/02 - MarketingPrice estimation or determinationFundraising

Abstract

Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models
• G01W 1/00 - Meteorology
• G06N 20/00 - Machine learning
• G06Q 40/08 - Insurance

Abstract

The present techniques relate to, inter alia, cryptographically-verifiable insurance credentials and cryptographically-verifiable property transfer. The novel methods and systems of decentralized identity discussed herein improve user experience (whether individual or organizational) by moving control over identity from the hands of centralized entities, back to where it belongs—i.e., to the hands of individual organizations and users. In one aspect, a method includes obtaining a scanned image; processing the scanned image; transmitting a claim request; and receiving and storing an attestation response, and a computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: receive a claim request; cryptographically verify the claim; and transmit an attestation response.

IPC Classes  ?

• G06Q 30/018 - Certifying business or products
• G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
• G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
• G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
• G06Q 40/08 - Insurance
• G06V 30/19 - Recognition using electronic means
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system

Abstract

Systems and methods for modifying operation of an autonomous vehicle in an emergency situation are disclosed. According to aspects, a computing device associated with the autonomous vehicle detects, based on sensor(s), an emergency event associated with the autonomous vehicle. In response to detecting the emergency event, the computing device determines location(s) of emergency vehicle(s) and determines an assistance location for the autonomous vehicle. The computing device determines which of the emergency vehicle(s) is a nearest emergency vehicle that is nearest to the assistance location, and transmits the assistance location to the nearest emergency vehicle. The computing device then causes the autonomous vehicle to travel to the assistance location.

IPC Classes  ?

• G08G 1/00 - Traffic control systems for road vehicles
• G08G 1/01 - Detecting movement of traffic to be counted or controlled
• G08G 1/0968 - Systems involving transmission of navigation instructions to the vehicle

Abstract

A computer system is provided and is programmed to: (1) receive sensor data associated with a primary vehicle; (2) determine a current condition of a driver of the primary vehicle based upon the sensor data of the primary vehicle; (3) determine a threat level for the primary vehicle based upon the current condition of the driver of the primary vehicle and the sensor data of the primary vehicle; (4) activate at least one action in the primary vehicle based upon the threat level for the primary vehicle; and/or (5) electronically transmit the threat level of the primary vehicle to one or more additional vehicles.

IPC Classes  ?

• B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
• B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• H04W 4/46 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]

Abstract

Techniques for implementing regression testing of a software application that interacts with external systems via structured data input/output files are described herein. The systems and techniques described herein allows for verification of differences between a verified and a development version of the software application in a continuous integration (CI) development environment. Additionally, the techniques described herein identify or shortlist code changes that may have caused the differences, and provide a notification to respective developers of the identified code changes. The notifications may indicate the code changes ordered by a likelihood of having caused the differences, and may additionally, provide automatically-generated textual descriptions to aid with the verification.

IPC Classes  ?

• G06F 11/3698 - Environments for analysis, debugging or testing of software
• G06F 11/3668 - Testing of software

Abstract

A roof assessment (RA) computing device may be programmed to store a roof assessment model within memory wherein the roof assessment model may be configured to determine a roof status of a selected roof after experiencing a weather event, output from the roof assessment model target weather event parameters for the selected roof wherein the target weather event parameters are weather event parameters that result in at least repairable damage to the selected roof, store the target weather event parameters in memory for the selected roof, determine that the selected roof has experienced a first weather event having weather event parameters that meet the target weather event parameters, and/or transmit a message to a user computing device associated with the selected roof advising that a claim associated with an insurance policy has been triggered as a result of the selected roof experiencing the first weather event.

IPC Classes  ?

• G06Q 40/08 - Insurance

Abstract

In a method for applying penalties or incentives to a driver of a rented vehicle, an indication that the driver has agreed to terms for renting the vehicle from the vehicle owner is received, with the terms including the potential application of penalties or incentives to the driver based on driving behavior. Telematics data, indicative of operation of the rented vehicle by the driver during a period of time, is also received. By analyzing the telematics data, one or more driving behaviors of the driver during the time period is/are identified. One or more characteristics of the rented vehicle are also determined. One or more penalties or incentives are caused to be applied to the driver, based on the driving behavior(s) and the one or more characteristics of the rented vehicle.

IPC Classes  ?

• B60W 40/09 - Driving style or behaviour
• G06Q 30/0645 - Rental transactionsLeasing transactions
• G06Q 50/40 - Business processes related to the transportation industry
• G07C 5/00 - Registering or indicating the working of vehicles

Abstract

An AI computer system for responding to an inbound message including a processor configured to: a) receive an inbound message from a requestor computing device, b) parse the inbound message to generate a query, c) determine one or more keywords contained within the query, d) identify, based in part on the keywords, a subject-matter category database, e) search the identified subject-matter category database to determine one or more relevant documents, f) input the determined one or more relevant documents into a generative AI model to generate one or more model outputs including a proposed response message responding to the inbound message and a relevancy score for each of the relevant documents, the relevancy score indicating a level of relevance and responsiveness of the document to the inbound message, and g) transmit a notification message to a representative computing device.

IPC Classes  ?

• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles

Abstract

Described herein are systems and techniques to facilitate rapid and effective replication of a data processing system using granular metadata, including metadata representing table-level permissions and data filters. Metadata is determined and stored in a metadata processing system that accounts for table-level and other low-level configurations. This data is then used to replicate some or all of a data processing system. Data stored by data assets may also be replicated, or the metadata may be used to replicate some or all of the functionality of the data processing system for processing other data.

IPC Classes  ?

• G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
• G06F 16/22 - IndexingData structures thereforStorage structures

Abstract

An AI-based computing system for responding in real-time to an inbound message including a processor that is programmed to cause to display, within a graphical user interface i) a proposed response message responsive to a query message, the proposed response message being generated by an AI response model, ii) a link to one or more relevant documents retrieved from a corresponding subject-category database, and iii) a feedback input enabling a representative to assign a response indicator. The processor is programmed to receive a response indicator for the proposed response message and create a historical record. The processor is programmed to generate a training dataset and re-train the AI response model using the training dataset. The processor is programmed to cause to display, within the graphical user interface, in real-time, a revised response message responsive to the current inbound message using the re-trained AI response model.

IPC Classes  ?

• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/248 - Presentation of query results

Abstract

An AI-based computing system for responding in real-time to an inbound message includes a processor configured to: a) transmit, to a representative computing device, an AI model generated proposed response message responsive to a query message derived from the inbound message, b) receive, from the representative computing device, feedback associated with the AI model generated proposed response message, the feedback provided by a representative, d) create a historical record including the AI model generated proposed response message and the feedback, e) generate a training dataset including at least the created historical record, and g) using machine learning and/or artificial intelligence techniques, re-train the AI model using the training dataset.

IPC Classes  ?

• G06N 5/022 - Knowledge engineeringKnowledge acquisition

Abstract

A computer system is provided and is programmed to: (1) receive a plurality of images; and/or (2) for each image of the plurality of images: (a) retrieve the image; (b) identify one or more items of text in the retrieved image; (c) analyze the one or more items of text to detect one or more personally identifiable items of text from the one or more items of text; (d) identify one or more personally identifiable items of text to obscure based upon one or more security settings; (e) update the retrieved image to obscure at least one of the one or more items of personally identifiable text in the image, wherein obscuring includes altering pixels within a predetermined number of pixels of the pixels identified as forming the at least one of the one or more items of personally identifiable text in the image; and/or (f) provide the updated image.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G06T 5/70 - DenoisingSmoothing
• G06V 30/14 - Image acquisition
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions

Abstract

This document describes techniques for performing dynamic transfer operations based on a blockchain ledger without needing to perform computationally expensive cryptographic hash reversal operations. In some cases, an example system stores a hashed identifier on a blockchain ledger, along with one or more ledger values like an account balance value and/or a payout amount value. The example system may store un-hashed sensitive backend data on a private backend database. When the example system receives an event notification, the system extracts an identifier from the notification, hashes the identifier, and uses the hashed identifier to retrieve the blockchain ledger. The system then determines whether the ledger value(s) satisfy a condition. If so, the system uses the hashed identifier stored on the blockchain ledger to query the backend database for an account identifier, maps the account identifier to a blockchain wallet, and executes a blockchain transfer operation to the blockchain wallet.

IPC Classes  ?

• G06Q 20/38 - Payment protocolsDetails thereof
• G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists

Abstract

Test cases written to test a software application can be dynamically distributed among a set of software application instances such that different sets of test cases can execute simultaneously in parallel, thereby speeding up testing relative to executing the test cases sequentially. To avoid database conflicts that may occur when different test cases are executed in parallel, each software application instance can be associated with a different database instance. Accordingly, a first test case executing in association with a first database instance can avoid interfering with a second test case executing in association with a second database instance.

IPC Classes  ?

• G06F 11/3668 - Testing of software
• G06F 9/52 - Program synchronisationMutual exclusion, e.g. by means of semaphores
• G06F 16/25 - Integrating or interfacing systems involving database management systems

Abstract

Systems and methods disclosed herein relate to fine-tuning machine learning (ML) chatbots for an enterprise. The systems and methods may use ML chatbots and/or generative ML to generate social media content for a user associated with an enterprise. The systems and methods may fine-tune a base ML model, and use the fine-tuned ML model for the ML chatbot. A user profile may indicate user attributes, and a fine-tuned ML model may be loaded for the ML chatbot based upon an identified user profile.

IPC Classes  ?

• G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06N 20/00 - Machine learning
• G06Q 10/40 -
• H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages

Abstract

Systems, devices, and methods for reconstruction of an event are disclosed. The timeline reconstruction may include gathering data associated with an event, analyzing the data, generating a 3D visualization of the event and surrounding environment, and generating a timeline for the event that allows a user to see probable start and end points of the event. The 3D timeline reconstruction may include tools to allow a user to vary parameters in the timeline reconstruction to see how the changed parameters vary start and end points.

IPC Classes  ?

• G06T 17/00 - 3D modelling for computer graphics

Abstract

Described herein are systems and techniques to facilitate the efficient generation and association of data based on communications across multiple communications channels. A unified communications system may process communications exchanges using multiple communications channels and generate data that may be associated with the interactions representing the communications exchanges. The system associates the interactions with a context. The system determines an interaction interface for subsequent communication based on the context and interaction data. The system further facilitates the switch of communications channels during a communications session, tracking, storing, and updating interaction and context data to maintain a consistent communications experience for a user.

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06Q 30/01 - Customer relationship services
• H04L 51/56 - Unified messaging, e.g. interactions between e-mail, instant messaging or converged IP messaging [CPM]
• H04M 3/42 - Systems providing special services or facilities to subscribers

Abstract

A method of administering dynamic user experience applications includes a user-initiated query wherein the user-initiated query includes one or more paths and/or one or more steps, and wherein the user-initiated query includes an operation. The method may include executing the operation with respect to the one or more paths and/ or the one or more steps, transmitting an output of executing the operation to the user, and displaying the output of executing the operation to the user.

IPC Classes  ?

• H04L 69/08 - Protocols for interworkingProtocol conversion
• G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• G06F 8/20 - Software design
• G06F 8/34 - Graphical or visual programming
• G06F 8/35 - Creation or generation of source code model driven
• G06F 8/36 - Software reuse
• G06F 8/60 - Software deployment
• G06F 8/70 - Software maintenance or management
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 11/3604 - Analysis of software for verifying properties of programs
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/9535 - Search customisation based on user profiles and personalisation
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06Q 10/067 - Enterprise or organisation modelling
• H04L 67/02 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
• H04L 67/10 - Protocols in which an application is distributed across nodes in the network
• H04L 67/125 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
• H04L 67/56 - Provisioning of proxy services
• G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• G06F 8/10 - Requirements analysisSpecification techniques
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06Q 10/0633 - Workflow analysis
• G06Q 40/12 - Accounting
• H04L 67/306 - User profiles
• H04L 67/50 - Network services

Abstract

Systems and methods disclosed herein relate generally to using augmented reality (AR) for receiving and displaying recommended devices proximate a structure. In some examples, underlay layer data may be received (e.g., from a camera of an AR viewer device); and overlay layer data may be received (e.g., from a different camera or other overlay layer device). An AR display may be created by correlating the underlay layer data with the overlay layer data. An improved home score indicia may be displayed based upon the recommended device.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06N 20/00 - Machine learning
• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06Q 10/20 - Administration of product repair or maintenance
• G06Q 30/0601 - Electronic shopping [e-shopping]
• G06T 19/00 - Manipulating 3D models or images for computer graphics
• G06Q 30/02 - MarketingPrice estimation or determinationFundraising
• G06Q 50/16 - Real estate
• G06Q 50/163 - Real estate management

Abstract

A computer system programmed to: (1) receive smart building analytics data associated with a first plurality of buildings, (2) receive claims data associated with a second plurality of buildings, (3) train one or more AI models using the smart building analytics data and the claims data to output a building plan for constructing an enhanced subdivision of a plurality of enhanced buildings at a select location, wherein each enhanced building includes materials and/or features customized for the select location that reduce an overall likelihood of loss and wherein the enhanced subdivision includes features that improve an overall energy efficiency of the plurality of enhanced buildings, (4) input construction data into the one or more AI models for constructing the enhanced subdivision at the select location, and (5) output the building plan for the enhanced subdivision and each of the enhanced buildings including a materials list and design drawings.

IPC Classes  ?

• G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06Q 10/0875 - Itemisation or classification of parts, supplies or services, e.g. bill of materials

Abstract

A system may include one or more processors, and one or more non-transitory, computer-readable media including instructions which, when executed by the one or more processors, cause the one or more processors to receive, via a first communication protocol, from a first sensor device, a first data packet including vehicle telematics data associated with a vehicle, the vehicle telematics data captured using one or more sensors of a second sensor device coupled to the vehicle, wherein the first sensor device received the vehicle telematics data from the second sensor device via a second communication protocol, generate a second data packet including the vehicle telematics data and an identifier of the second sensor device, and transmit the second data packet via a third communication protocol to an external computing system.

IPC Classes  ?

• G08C 17/02 - Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link

Abstract

Systems and methods for dynamically generating output in response to user input through interconnected data retrieval and processing are disclosed. The method may include, such as by processor(s), transceiver(s), and/or sensor(s): (1) receiving a request from a device; (2) utilizing a LLM to process the request to determine a request purpose; (3) transmitting data corresponding to the request purpose to a RAG model; (4) receiving relevant data associated with the request purpose from the RAG model, the RAG model communicates with agent-based artificial intelligence system(s) to identify relevant data from (i) LLM, (ii) SLMs, (iii) dynamic data stream(s) associated with interconnected system(s), and/or (iv) generative software system(s); (5) transmitting the relevant data to a LLM data deconfliction model; (6) utilizing the LLM data deconfliction model to reconcile, filter, or resolve conflicting or redundant information of the relevant data; and/or (7) generating the output using the generative software system(s).

IPC Classes  ?

• G06F 16/2452 - Query translation
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

Systems and methods for dynamically generating output in response to user input through interconnected data retrieval and processing are disclosed. The method may include, such as by processor(s), transceiver(s), and/or sensor(s): (1) receiving a request from a device; (2) utilizing a LLM to process the request to determine a request purpose; (3) transmitting data corresponding to the request purpose to a RAG model; (4) receiving relevant data associated with the request purpose from the RAG model, the RAG model retrieves relevant data based upon user-defined configurations from (i) LLM, (ii) SLMs, (iii) dynamic data stream(s) associated with interconnected system(s), and/or (iv) generative software system(s); (5) transmitting the relevant data to a LLM data deconfliction model; (6) utilizing the LLM data deconfliction model to reconcile, filter, or resolve conflicting or redundant information of the relevant data; and/or (7) generating the output using the generative software system(s).

IPC Classes  ?

• G06F 16/248 - Presentation of query results
• G06F 40/20 - Natural language analysis

Abstract

A computer system programmed to: (1) receive smart building analytics data associated with a first plurality of buildings, (2) receive claims data associated with a second plurality of buildings, (3) train the one or more AI models using the smart building analytics data and the claims data, the one or more AI models trained to output a building plan for constructing an enhanced building at a select location, wherein the enhanced building includes materials and/or features customized for the select location that reduce an overall likelihood of loss at the enhanced building, (4) input into the one or more AI models construction data for constructing the enhanced building at the select location, and (5) output the building plan for the enhanced building including a materials list and design drawings for constructing the enhanced building based upon the construction data.

IPC Classes  ?

• G06Q 50/16 - Real estate
• G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
• G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06Q 10/10 - Office automationTime management
• G06Q 50/08 - Construction

Abstract

A computer system programmed to: (1) receive smart building analytics data associated with a first plurality of buildings each located at different locations; (2) receive claims data associated with a second plurality of buildings each located at different locations, wherein the first plurality of buildings includes at least some of the second plurality of buildings; (3) receive input data including at least a select location; (4) access one or more artificial intelligence (AI) models trained to analyze input data associated with the select location; (5) input the smart building analytics data and the claims data into the one or more AI models to generate one or more recommendations for the select location based upon the smart building analytics data and the claims data; and (6) transmit the one or more recommendations to a user computing device.

IPC Classes  ?

• G06Q 50/16 - Real estate
• G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
• G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06Q 10/10 - Office automationTime management
• G06Q 50/08 - Construction

Abstract

A technique is provided for determining a loss risk assessment score for a vehicle trip. The technique includes, at a vehicle, a computing device receiving first information indicative of operation of the vehicle. The technique also includes, at the vehicle, the computing device receiving second information indicative of an environment at a particular location and time. The computing device correlates the first information and the second information to generate a data set. The technique also includes determining a score for the vehicle trip based at least in part upon the generated data set.

IPC Classes  ?

• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

The following relates generally to determining and/or displaying a home score and/or homeowner points. In some embodiments, one or more processors: (1) determine a location subscore; (2) determine a property subscore; (3) determine a home score based upon the location subscore and/or property subscore; (4) determine homeowner points based upon completion of one or more insights; and/or (5) display the home score and/or the homeowner points.

IPC Classes  ?

• G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
• G06Q 40/08 - Insurance
• G06Q 50/163 - Real estate management

Abstract

A computer-implemented method for providing covered parking to vehicles is provided. The method may include transmitting a location identifier of a covered parking location and a communication address of a covered parking location (CPL) computing device to a vehicle computing device and/or an insurance computing device. The method may further include transmitting a signal to the vehicle computing device indicating a number of available parking spots of the covered parking location when the vehicle computing device contacts the CPL computing device, receiving a request from the vehicle computing device to reserve a parking spot of the covered parking location, determining whether to accept the request, and transmitting a response to the vehicle computing device. Accepting the request causes the CPL computing device to permit a vehicle associated with the vehicle computing device access to the covered parking location.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G07B 15/02 - Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points taking into account a variable factor such as distance or time, e.g. for passenger transport, parking systems or car rental systems
• G08G 1/14 - Traffic control systems for road vehicles indicating individual free spaces in parking areas
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Goods & Services

Downloadable software in the nature of a mobile application for billing of insurance premiums, providing insurance rate quotes, insurance underwriting, insurance claims administration; Downloadable software in the nature of a mobile application for consolidating and managing insurance underwriting and insurance claims administration, accounts, and connections to existing and emerging application programming interfaces (APIs); Downloadable software in the nature of a mobile application for providing insurance agency services in the fields of automobile, home, health, life, and fire insurance. Insurance underwriting services in the fields of auto, home, health, life, and fire; providing banking services; mutual fund investments; financial analysis and consultation. Providing temporary use of on-line non-downloadable software for providing a simplified user interface, data integration, database management, sharing of data and information sharing between users, customer relationship management, email marketing and lead management, billing of insurance premiums, providing insurance rate quotes, insurance underwriting, insurance claims administration; Providing temporary use of on-line non-downloadable software for providing insurance agency services in the fields of automobile, home, health, life, and fire insurance Computer services, namely, hosting an interactive web site that allows users to consolidate and manage insurance underwriting and insurance claims administration, accounts, and connections to existing and emerging application programming interfaces (APIs).

Goods & Services

Downloadable software in the nature of a mobile application for billing of insurance premiums, providing insurance rate quotes, insurance underwriting, insurance claims administration; Downloadable software in the nature of a mobile application for consolidating and managing insurance underwriting and insurance claims administration, accounts, and connections to existing and emerging application programming interfaces (APIs); Downloadable software in the nature of a mobile application for providing insurance agency services in the fields of automobile, home, health, life, and fire insurance. Insurance underwriting services in the fields of auto, home, health, life, and fire; providing banking services; mutual fund investments; financial analysis and consultation Providing temporary use of on-line non-downloadable software for providing a simplified user interface, data integration, database management, sharing of data and information sharing between users, customer relationship management, email marketing and lead management, billing of insurance premiums, providing insurance rate quotes, insurance underwriting, insurance claims administration; Providing temporary use of on-line non-downloadable software for providing insurance agency services in the fields of automobile, home, health, life, and fire insurance Computer services, namely, hosting an interactive web site that allows users to consolidate and manage insurance underwriting and insurance claims administration, accounts, and connections to existing and emerging application programming interfaces (APIs).

Goods & Services

Downloadable software in the nature of a mobile application for billing of insurance premiums, providing insurance rate quotes, insurance underwriting, insurance claims administration; Downloadable software in the nature of a mobile application for consolidating and managing insurance underwriting and insurance claims administration, accounts, and connections to existing and emerging application programming interfaces (APIs); Downloadable software in the nature of a mobile application for providing insurance agency services in the fields of automobile, home, health, life, and fire insurance. Insurance underwriting services in the fields of auto, home, health, life, and fire; providing banking services; mutual fund investments; financial analysis and consultation. Providing temporary use of on-line non-downloadable software for providing a simplified user interface, data integration, database management, sharing of data and information sharing between users, customer relationship management, email marketing and lead management, billing of insurance premiums, providing insurance rate quotes, insurance underwriting, insurance claims administration; Providing temporary use of on-line non-downloadable software for providing insurance agency services in the fields of automobile, home, health, life, and fire insurance Computer services, namely, hosting an interactive web site that allows users to consolidate and manage insurance underwriting and insurance claims administration, accounts, and connections to existing and emerging application programming interfaces (APIs).

Abstract

Systems and methods are disclosed for providing a graphic user interface. The systems and methods may (1) receive an indication of a parametric event involving a vehicle; (2) provide a series of component graphic user interfaces, wherein the component graphic user interfaces may be configured to (i) initiate a claim filing, (ii) communicate with an insurer entity, (iii) communicate with an emergency response entity, (iv) communicate with a towing service entity, (v) communicate with a taxi or ride-share service entity, (vi) communicating with a vehicle repair service entity, (vii) communicating with a vehicle salvage entity, and/or (viii) communicating with a vehicle rental service entity; (3) detect one or more user interactions that may include one or more selections of the one or more actions; (4) perform the one or more actions; and/or (5) record a transaction indicating that the one or more actions has been performed to the blockchain.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range

Abstract

A computer-implemented method, including receiving a first file composed of unstructured data with a first file format; applying one or more programmatic solutions for identifying one or more duplicate text in the first file format wherein the one or more duplicate text are associated with one or more attributes contained in unstructured data of the first file; identifying duplicate text contained in the first file wherein the duplicate text is associated with at least a first attribute of the unstructured data; in response to identifying duplicate text contained in the first file format, applying a mechanism for modifying the duplicate text in the first file format to change the first attribute of the unstructured data to a second attribute of a second file format; and converting the unstructured data of the first file into a second file of structured data of the second file format composed of the second attribute.

IPC Classes  ?

• G06F 16/11 - File system administration, e.g. details of archiving or snapshots
• G06F 16/174 - Redundancy elimination performed by the file system

Abstract

Described herein are systems and techniques to infer the accuracy and variability of third-party machine-learned models based on input and output data. A baseline dataset may be used as input data to a model multiple times, and the resulting output data is compared to determine whether any significant differences are observed. A dataset having available ground truth data may be used as input data to a model and the resulting output data compared to the ground truth data to determine model accuracy. Input data metadata and output data metadata may be analyzed to determine changes over time that may indicate model and/or data source changes. Responsive actions may be taken, such as switching models, retraining downstream models, and/or suspending systems affected by adverse model changes.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

This disclosure describes techniques for automated data extraction, validation, and routing based on unstructured text data. In some cases, the techniques described herein include receiving text data, segmenting the text data into multiple segments, assigning each segment to a category, generating a prompt for each segment based on the segment's category, extracting field values from each segment using the generated prompt, validating or rejecting the extracted field values based on category-specific validation rules, and routing the validated field values to category-specific target databases and/or reviewer platforms based on the validation results.

IPC Classes  ?

• G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
• G06N 3/0475 - Generative networks

Abstract

Systems and methods for identifying a condition associated with an individual in a home environment are provided. Sensors associated with the home environment detect data, which is captured and analyzed by a local or remote processor to identify the condition. In some instances, the sensors are configured to capture data indicative of electricity use by devices associated with the home environment, including, e.g., which devices are using electricity, what date/time electricity is used by each device, how long each device uses electricity, and/or the power source for the electricity used by each device. The processor analyzes the captured data to identify any abnormalities or anomalies, and, based upon any identified abnormalities or anomalies, the processor determines a condition (e.g., a medical condition) associated with an individual in the home environment. The processor generates and transmits a notification indicating the condition associated with the individual to a caregiver of the individual.

IPC Classes  ?

• G08B 21/04 - Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
• G06N 20/00 - Machine learning
• G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Abstract

A computer system for generating a virtual reality replicant persona for interaction may be provided. The computer system may include at least one processor in communication with at least one memory device. The at least one processor may be programmed to (i) store a replicant persona of an individual; (ii) receive a request from a user to interact with the replicant persona; (iii) generate an avatar of the replicant persona based upon the request; (iv) place avatar in a virtual reality environment based upon the request; and (v) conduct avatar interaction with the user.

IPC Classes  ?

• G06T 19/00 - Manipulating 3D models or images for computer graphics
• G06T 13/40 - 3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
• G06T 17/00 - 3D modelling for computer graphics

Abstract

Systems and methods for utilizing geotagged data for predictive modeling are disclosed. The method may include, such as by one or more processors, transceivers, and/or sensors: (1) receiving a first set of geotagged data from devices associated with a user; (2) processing data received from data sources for supplemental data corresponding to locations in the first set of geotagged data; (3) inputting the first set of geotagged data and the supplemental data into a machine-learning model, wherein the machine-learning model is trained to generate (i) an event prediction corresponding to event occurrences at the locations, and/or (ii) recommendations corresponding to the predicted events; (4) generating a risk profile for the locations based upon a frequency of the event occurrences of the predicted events; and/or (5) presenting a visual and/or audible prediction presentation based upon the event prediction, the risk profile, and/or the recommendations to user via a user device.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

Systems and methods for energy optimization and identifying an energy efficiency program for a property are disclosed. The method may include, such as by one or more processors, transceivers, and/or sensors: (1) receiving input data associated with the property; (2) analyzing the input data to determine a property profile; (3) inputting the property profile and data from data sources for energy efficiency programs into a machine-learning model; (4) processing, utilizing the machine-learning model, the input data to (i) classify energy usage patterns, and/or (ii) predict energy savings inefficiencies; (5) matching, utilizing the machine-learning model, an energy efficiency program to the property based upon the input data and the predicted energy savings inefficiencies; (6) generating an energy assessment and a cost analysis based upon the predicted energy savings inefficiencies and the energy efficiency program; and/or (7) generating an interactive visualization of the energy assessment and the energy efficiency program.

IPC Classes  ?

• G06Q 30/018 - Certifying business or products
• G06Q 30/0234 - Rebates after completed purchase
• G06Q 50/06 - Energy or water supply
• G06Q 50/26 - Government or public services

Abstract

Methods and systems for identifying and correcting abnormal electrical activity about a home or other structure are provided. An electricity monitoring device may monitor electrical activity including transmission of electricity via an electrical distribution board to devices about the home. Electrical activity may be correlated with respective electrical devices to build an electrical profile associated with the structure. In response to a claimed damage to the structure or to electric devices therein, some electrical activity may be compared to the electrical profile to determine whether the damage occurred voluntarily or involuntarily.

IPC Classes  ?

• H02H 3/04 - Emergency protective circuit arrangements for automatic disconnection directly responsive to an undesired change from normal electric working condition, with or without subsequent reconnection Details with warning or supervision in addition to disconnection, e.g. for indicating that protective apparatus has functioned
• G05B 15/02 - Systems controlled by a computer electric
• G06Q 40/08 - Insurance
• H02J 3/001 -
• H04W 4/02 - Services making use of location information

Abstract

Techniques and devices for enhancing media content and presence are discussed herein. An example technique may include aggregating social media data from a plurality of agent profiles and determining a composite score corresponding to each respective agent profile by applying a media enhancement model to the social media data. The example technique may further include cataloging each respective agent profile into an agent profile group of a plurality of agent profile groups based upon the composite score corresponding to the respective agent profile, and determining one or more top media posts by applying the media enhancement model to the plurality of agent profile groups and the social media data. The example technique may further include displaying the one or more top media posts on a virtual social media board for viewing by a respective agent associated with each respective agent profile.

IPC Classes  ?

• G06Q 10/40 -
• G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G06N 20/20 - Ensemble learning

Abstract

A computer system is provided and is programmed to: (1) store, in a first database, knowledge base data sets for each of a plurality of datasets, wherein each knowledge base data set includes at least data relating to the use of the associated dataset and a link to a separate database storing the corresponding dataset; (2) receive, at the first database from a user computer device, a request for knowledge base data for a first dataset; (3) instruct the user computer device to display the knowledge base data for the first dataset; (4) receive a request for access to the first dataset, wherein the request for access includes one or more database operations to be performed on the first dataset; (5) access the first dataset; and/or (6) execute the one or more database operations on the first dataset to provide results to the user computer device.

IPC Classes  ?

• G06F 16/248 - Presentation of query results
• G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content

Abstract

The disclosure describes a method for optimizing data flow graphs to represent relationships between data sets. In an example implementation, a processor receives an initial graph showing direct links between data sets and elements. The processor then expands this graph by replacing direct source-target links with new paths going through intermediate elements. Next, the processor simplifies the expanded graph by removing intermediary nodes and edges, replacing them with direct source-target links. If the graph shows an indirect path between two data sets through intermediate nodes, the path will be consolidated into a single direct link. Additionally, the optimized graph is persisted in a database and made accessible through queries. Overall, this improves lineage graph efficiency, allowing easier traversal and analysis, while also enabling incremental updates as data flows change.

IPC Classes  ?

• G06F 16/245 - Query processing
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2455 - Query execution

Abstract

Methods and systems for processing a blockchain comprising a plurality of immutable sales records corresponding to sales made by agents of an entity are provided. According to certain aspects, a transaction request indicating a sale made by an agent of the entity may be received at a first node. A block including a sales record indicating the sale made by the agent may be added to a blockchain and transmitted to another node for validation. The first node may add the block to a copy of the blockchain, where the block may be identified by a hash value that references a previous block in the blockchain that includes at least one additional sales record.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• G06Q 10/10 - Office automationTime management
• G06Q 20/10 - Payment architectures specially adapted for electronic funds transfer [EFT] systemsPayment architectures specially adapted for home banking systems
• G06Q 20/22 - Payment schemes or models
• G06Q 20/38 - Payment protocolsDetails thereof
• G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
• G06Q 40/08 - Insurance
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/08 - Key distribution
• H04L 9/14 - Arrangements for secret or secure communicationsNetwork security protocols using a plurality of keys or algorithms
• H04L 9/30 - Public key, i.e. encryption algorithm being computationally infeasible to invert and users' encryption keys not requiring secrecy
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/40 - Network security protocols
• H04L 67/104 - Peer-to-peer [P2P] networks

Abstract

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives light detection and ranging (LIDAR) data generated from a LIDAR camera; receives preexisting utility line data; and determines a location of the utility line based upon: (i) the received LIDAR data, and (ii) the received preexisting utility line data.

IPC Classes  ?

• G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
• B64C 39/02 - Aircraft not otherwise provided for characterised by special use
• B64U 101/30 - UAVs specially adapted for particular uses or applications for imaging, photography or videography
• G01C 21/20 - Instruments for performing navigational calculations
• G01C 21/34 - Route searchingRoute guidance
• G01S 7/48 - Details of systems according to groups , , of systems according to group
• G01S 7/51 - Display arrangements
• G01S 17/06 - Systems determining position data of a target
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• G01S 19/45 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
• G02B 27/01 - Head-up displays
• G06F 16/29 - Geographical information databases
• G06F 18/24 - Classification techniques
• G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
• G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• G06N 3/08 - Learning methods
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G06Q 20/08 - Payment architectures
• G06Q 20/12 - Payment architectures specially adapted for electronic shopping systems
• G06Q 30/0601 - Electronic shopping [e-shopping]
• G06T 7/60 - Analysis of geometric attributes
• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
• G06T 7/90 - Determination of colour characteristics
• G06T 17/00 - 3D modelling for computer graphics
• G06T 17/05 - Geographic models
• G06T 19/00 - Manipulating 3D models or images for computer graphics
• G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• G06V 20/17 - Terrestrial scenes taken from planes or by drones
• G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
• G06V 20/40 - ScenesScene-specific elements in video content
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks

Abstract

Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving telematics data related to an insured asset; (2) analyzing the telematics data to identify a characteristic associated with the insured asset; (3) identifying a record on the blockchain related to the asset; (4) recording the indication of the detected characteristic associated with the asset; and/or (5) updating one or more entries of the asset record associate with the asset.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06Q 20/38 - Payment protocolsDetails thereof

Abstract

Systems and methods for customized data management (CDM) may include: (i) establishing a communication link with a computing device associated with a user account of a user; (ii) receiving user data from the user account via the communication link; (iii) executing a CDM tool to actively monitor the user account, wherein the CDM tool includes a user-specific artificial intelligence (AI) model trained based upon historical user data associated with the user and the user account; (iv) detecting, by the CDM tool, that the user account requires a managed response to an event associated with the user account; (v) in response to the detected event, generating the managed response using the user-specific AI model and without contemporaneous input from the user, the managed response satisfying a condition associated with the detected event; and/or (vi) transmitting, via the communication link, the managed response to the computing device associated with the user account.

IPC Classes  ?

• G06Q 20/42 - Confirmation, e.g. check or permission by the legal debtor of payment

Abstract

Systems and methods are disclosed for enhancing and developing a damage scene virtual reality (VR) visualization. Annotated immersive multimedia image(s) may be received from a first user, where the annotated immersive multimedia image(s) can be associated with a damage scene. A VR visualization of the annotated immersive multimedia image(s) may be rendered using a VR device associated with a second user. The VR visualization may be used to determine a damage amount, where the damage amount is determined from one or more damaged items identifiable in the annotated immersive multimedia image(s).

IPC Classes  ?

• G06T 19/00 - Manipulating 3D models or images for computer graphics
• G06F 40/169 - Annotation, e.g. comment data or footnotes
• G06Q 40/08 - Insurance
• G06T 11/00 - 2D [Two Dimensional] image generation
• H04L 67/025 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications

Abstract

The following relates generally to light detection and ranging (LIDAR). In some embodiments, a homeowners insurance quote is produced based upon data received from a LIDAR camera. For instance, in some embodiments, a system: receives light detection and ranging (LIDAR) data generated from one or more LIDAR cameras; analyzes the LIDAR data to determine or identify one or more features or characteristics of a home; and generates an electronic homeowners insurance quote based upon, at least in part, the one or more features or characteristics of the home determined or identified from the LIDAR data.

IPC Classes  ?

• G06Q 40/08 - Insurance
• B64C 39/02 - Aircraft not otherwise provided for characterised by special use
• B64U 101/30 - UAVs specially adapted for particular uses or applications for imaging, photography or videography
• G01S 7/48 - Details of systems according to groups , , of systems according to group
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• G05B 15/02 - Systems controlled by a computer electric
• G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
• G06Q 50/16 - Real estate
• G06V 20/10 - Terrestrial scenes
• G06V 20/17 - Terrestrial scenes taken from planes or by drones
• G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

A computer system may be programmed to: (1) receive a first home inspection report associated with a first home; (2) extract, using an artificial intelligence model, home data from the first home inspection report, the artificial intelligence model including extraction tools and trained using correlations between historical home inspection reports and historical home data; (3) store the extracted home data for the first home in a data structure including a plurality of data fields; (4) identify at least one data field of the plurality of data fields that is missing a data value; (5) generate, using the artificial intelligence model, at least one predicted data value for the identified at least one data field based upon the historical home data; and/or (6) store the at least one predicted data value in the identified at least one data field.

IPC Classes  ?

• G06Q 50/16 - Real estate
• G06N 5/022 - Knowledge engineeringKnowledge acquisition

Abstract

Methods and systems for providing emergency heating in an electric vehicle (EV) running out of power are described herein. An on-board computer or mobile device in an EV may determine that an amount of charge remaining for powering the EV is below a threshold charge level. The on-board computer or mobile device may then route the remaining amount of charge to power a heating system in the EV to maintain a temperature in the EV above a threshold temperature level, and shut down power to other components within the EV.

IPC Classes  ?

• B60H 1/00 - Heating, cooling or ventilating devices
• B60L 1/02 - Supplying electric power to auxiliary equipment of electrically-propelled vehicles to electric heating circuits
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

The following relates generally to AI-based review of insurance claims complaints. In some embodiments, one or more processors: (1) receive, via a chatbot, an insurance claim complaint; (2) categorize, via the chatbot, the insurance claim complaint by determining a category of the insurance claim complaint, the category comprising a tone category or a policy category; (3) build, via the chatbot, a complaint report including information of the insurance claim complaint and an indication of the category; and/or (5) send, via the chatbot, the complaint report to an insurance complaint administrator computing device.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06F 40/30 - Semantic analysis
• G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• G06Q 10/40 -
• G06Q 30/016 - After-sales
• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages

Goods & Services

Insurance claims processing for others in the area of home repair

Goods & Services

Charitable services, namely, providing basketball nets to communities in need

Goods & Services

Basketball nets Charitable services, namely, providing basketball nets to communities in need

Goods & Services

Charitable services, namely, providing basketball nets to communities in need

Abstract

A computer system is provided. The computer system may be programmed to: (a) receive appliance data relating to a first appliance; (b) compute, using an artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the received appliance data, wherein the artificial intelligence model is trained based upon historical appliance data including historical lifetimes of appliances; and/or (c) transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the predicted remaining lifetime.

IPC Classes  ?

• G06F 11/30 - Monitoring
• G06F 11/32 - Monitoring with visual indication of the functioning of the machine
• G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
• G06Q 10/20 - Administration of product repair or maintenance

Abstract

Provided herein is a computing system including a processor in communication with at least one memory. The processor is configured to (i) receive a video executable file, (ii) identify one or more interactive elements in the video executable file by scanning the video executable file, (iii) prompt a user to input at least one response option for each identified interactive element, (iv) convert the at least one response option into an executable code snippet, (v) create an enhanced video executable file by embedding the executable code snippet of each respective interactive element into the video executable file for execution at a designated time, and (vi) store the enhanced video executable file to be accessible by a viewer device, wherein the video executable file is executed by the viewer device.

IPC Classes  ?

• G11B 27/036 - Insert-editing
• G06N 3/08 - Learning methods
• G09B 7/02 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student
• G09B 7/06 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers
• G09B 21/00 - Teaching, or communicating with, the blind, deaf or mute
• G11B 27/34 - Indicating arrangements

Goods & Services

Charitable services, namely, providing basketball nets to communities in need

Abstract

Systems and methods are described for detecting and responding to a likelihood of a heating system malfunction. The method may include: (1) receiving home telematics data associated with a structure, wherein the home telematics data includes local weather data for an area associated with the structure; (2) receiving operation data regarding a functionality of the heating system associated with the structure; (3) diagnosing, based upon at least the home telematics data and the operation data, a potential malfunction in the heating system; (4) determining that the potential malfunction includes an obstacle blocking one or more exhaust pipes for the heating system; and (5) causing one or more apparatuses associated with the one or more exhaust pipes to expel air.

IPC Classes  ?

• F24F 11/38 - Failure diagnosis
• F24F 11/50 - Control or safety arrangements characterised by user interfaces or communication
• F24F 11/74 - Control systems characterised by their outputsConstructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
• F24F 130/10 - Weather information or forecasts

Abstract

A computer system for performing cloud-based enhanced rules conflict checking is provided. The computer system is programmed to store a plurality of rules for transmitting to a plurality of destination systems and receive a data message for transmission to the plurality of destination systems from a first requesting system. The computer system is programmed to compare the data message to a first set of rules to validate the data message and if the data message is validated for the first set of rules, instruct the first requesting system to transmit the data message to the plurality of destination systems. The computer system is further programmed to receive the data message for transmission to one or more remaining destination systems from a second requesting system and compare the data message to a second set of rules for validating the data message.

IPC Classes  ?

• G06F 16/23 - Updating
• G06F 16/245 - Query processing

Abstract

A computer-implemented method of predicting and providing an efficient driving route for an autonomous or semi-autonomous electric vehicle based upon battery health impact includes (i) generating one or more driving routes for the autonomous or semi-autonomous electric vehicle; (ii) predicting a projected battery health impact on a battery of the autonomous or semi-autonomous electric vehicle for each driving route of the one or more driving routes, wherein the projected battery health impact is based at least upon a predicted time of day for travel; (iii) determining one or more recommendations for the autonomous or semi-autonomous electric vehicle for a driving route of the one or more driving routes, the one or more recommendations based upon at least the projected battery health impact for each driving route and a preferred form of operation; and (iv) causing the autonomous or semi-autonomous electric vehicle to automatically drive along the recommended route.

IPC Classes  ?

• B60L 58/13 - Maintaining the SoC within a determined range
• B60L 53/36 - Means for automatic or assisted adjustment of the relative position of charging devices and vehicles by positioning the vehicle
• B60L 53/53 - Batteries
• B60L 58/16 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/34 - Route searchingRoute guidance

Abstract

A server and non-transitory computer-readable storage medium storing instructions for monitoring one or more batteries of an electric vehicle (EV) comprising computing instructions for (i) receiving, from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV; (ii) determining a battery status of the one or more batteries based upon the telematics data; and (iii) mapping the battery status of the one more batteries to a digital record corresponding to the EV in a database.

IPC Classes  ?

• H01M 10/42 - Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
• G06Q 40/08 - Insurance

Abstract

A system and computer-implemented method for approving a dynamic mortgage application using a blockchain. In one aspect, the method may include determining a customer is approved for a mortgage (“mortgage ready”) and determining a real estate property is mortgage ready. The method may include comparing a calculated amount in which the customer is approved for a mortgage loan with a calculated appraisal value of the real estate property, and approving the mortgage application of the customer when the calculated amount the customer is approved for the mortgage loan is equal to, or exceeds, the calculated appraisal value of the real estate property, reducing a processing time and closing time of the mortgage.

IPC Classes  ?

• G06Q 40/03 - CreditLoansProcessing thereof
• G06Q 30/02 - MarketingPrice estimation or determinationFundraising
• G06Q 50/16 - Real estate
• H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols

Abstract

Described herein are systems and techniques to facilitate secure storage and efficient retrieval of vehicle data from a variety of sources that may be used to reconstruct various vehicular scenarios, such as a car accident. Vehicle data may be generated and/or collected by various systems, including vehicle-based systems, user devices (e.g., smartphones), and external systems (e.g., weather or mapping systems). This data may be aggregated by the disclosed systems into a data structure used to generate transaction data for a blockchain block that may then be stored in a blockchain for later use in vehicle scenario reconstructions.

IPC Classes  ?

• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• G07C 5/00 - Registering or indicating the working of vehicles

Abstract

In one aspect, a computing system including a processor in communication with at least one memory may be provided. The processor may be configured to: (i) connect to a plurality of data sources in a nodal network including a plurality of nodes; (ii) determine, for each node of the plurality of nodes, a relationship to at least one other node of the plurality of nodes; (iii) generate, based upon the determined relationships between nodes, a mapping including each node of the plurality of nodes and each relationship of the determined relationships; (iv) analyze each relationship of the determined relationships to determine at least one threat; (v) determine at least one solution for the at least one threat; and/or (vi) cause display of the at least one threat and the at least one solution.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06N 3/08 - Learning methods

Abstract

The following relates generally to creating customized training datasets for improved training of artificial intelligence (AI) and/or machine learning (ML) models, particularly in the insurance industry. In some embodiments, one or more processors are configured to: (1) construct a customized training dataset; (2) train the ML model by inputting the customized training dataset into the ML model; and/or (3) determine, by inputting data of the customer into the trained ML model, one or more of: (i) probability of a loss by cause of loss, (ii) a cost estimate by cause of loss, (iii) probability of loss by loss-comment-code, (iv) indemnity estimate by loss-comment-code, (v) percent change in probability of loss given performed insight, (vi) probability that customer will perform insight, (vii) estimated cost of performed insight, (viii) customer segmentation, and/or (ix) probability of the customer placing an insurance claim.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06N 20/00 - Machine learning

Abstract

A computer system is provided that uses compositional modeling to generate deliverables. The system may include one or more data sources storing historical action outcome data, historical resource data, and current resource data associated with an enterprise, and a compositional intelligence computing device configured to: (i) train a compositional model using the historical action outcome and resource data; (ii) receive a query identifying a deliverable; (iii) input a prompt to the compositional model; (iv) execute the compositional model using the prompt and the current resource data to generate output including the deliverable; (v) provide the deliverable; (vi) receive feedback related to the deliverable; (vii) re-execute the compositional model using the feedback to generate output including a revised deliverable; and/or (viii) control a resource scheduling component to implement one or more actions related to the deliverable.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 10/067 - Enterprise or organisation modelling

Abstract

A system and method for intelligent message routing is provided. The system and method include a) receiving a first message to be transmitted to a participant, wherein the first message includes a first conversation identifier signifying a first conversation with the participant, b) determining a first channel to transmit the first message to the participant, c) transmitting the first message over the first channel; and d) locking the first channel to the first conversation.

IPC Classes  ?

• H04L 51/56 - Unified messaging, e.g. interactions between e-mail, instant messaging or converged IP messaging [CPM]
• H04L 51/214 - Monitoring or handling of messages using selective forwarding

Abstract

A method for responding to inferred caller states during dialog with an intelligent voice interface configured to lead callers through pathways of an algorithmic dialog may include, during a voice communication with a caller via a caller device, receiving from the caller device caller input data indicative of a voice input of the caller, and determining, by processing the caller input data, an inferred state of the caller. Determining the inferred state of the caller may include analyzing one or more characteristics, other than textual content, of the voice input. The method may also include selecting a pathway through the algorithmic dialog based upon the inferred state of the caller.

IPC Classes  ?

• H04M 3/493 - Interactive information services, e.g. directory enquiries
• G06F 3/16 - Sound inputSound output
• G06F 40/35 - Discourse or dialogue representation
• G10L 15/04 - SegmentationWord boundary detection
• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/183 - Speech classification or search using natural language modelling using context dependencies, e.g. language models
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/26 - Speech to text systems
• G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
• G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
• H04L 51/52 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
• H04M 3/42 - Systems providing special services or facilities to subscribers

Abstract

A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analyses of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G06Q 30/0207 - Discounts or incentives, e.g. coupons or rebates
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 30/19 - Recognition using electronic means
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle data is obtained for vehicles having various smart safety features, and a list of translated vehicle build records is generated from the obtained data applying OEM-agnostic terminology for the smart safety features. A machine learning algorithm may be trained to generate an effectiveness score associated with one or more smart safety features, at least by analyzing a plurality of translated vehicle build records, vehicle telematics data, and vehicle accident records associated with each of the plurality of vehicles.

IPC Classes  ?

• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• B60W 30/08 - Predicting or avoiding probable or impending collision
• G07C 5/00 - Registering or indicating the working of vehicles

Abstract

Systems and methods for predicting risk levels of damage to building exterior elements due to weather events are disclosed. Such exterior elements may include siding, gutters, windows, doors, etc. Weather data may be obtained and used to determine that a weather event has impacted, or is predicted to impact, a geographic area. Building data and exterior element data for a building may be received and used to determine that the building is located within the geographic area of the weather event. An event-based risk score for the building indicating a probability of damage to the exterior elements of the building due to the weather event is calculated based upon the building data, the exterior element data, the weather data, and, if available, a baseline risk score for the building. Remedial actions to avoid or limit such damage may be determined based upon the event-based damage prediction.

IPC Classes  ?

• G06Q 40/08 - Insurance

Abstract

Techniques for supervised access to the error logs stored on a protected database to perform test failure remediation with respect to the failures associated with prior executions of test scripts. In some cases, when the execution of a test script is aborted because a data value provided by the test script fails to match a corresponding data record stored on a target database, a corresponding error log may be created. To remediate the noted test failure, an example system may select a test correction template that is associated with a corresponding test correction script and provide a corrected data value as a parameter of the selected template. Subsequently, the system may perform operations associated with a database transaction that is configured to: (i) re-execute the operations associated with the previously aborted test script, and (ii) upon successful re-execution, remove the corresponding error log.

IPC Classes  ?

• G06F 11/3668 - Testing of software
• G06F 11/3698 - Environments for analysis, debugging or testing of software

Abstract

A computer system for analyzing and mitigating risks associated with a building is provided. The computer system is configured to: (i) receive environment data from the at least one sensor; (ii) receive building data from the at least one database; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the building based upon the environment data and the building data; (iv) generate a building risk profile that includes the at least one potential risk associated with the building; and/or (v) generate a risk mitigation output based upon at least one of the building risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a user, and an event are also provided.

IPC Classes  ?

• G06Q 50/26 - Government or public services
• G06F 16/23 - Updating
• G06F 16/9535 - Search customisation based on user profiles and personalisation
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

A computer system for sump pump monitoring and repair service provider notification may include one or more processors configured to: detect that a sump pump is faulty, transmit a prompt for service quotes to a machine learning (ML) chatbot to cause the ML chatbot to: request sump pump replacement or repair services from one or more service providers, receive cost estimates from the one or more repair service providers, receive schedule availability from the one or more repair service providers, receive, from the ML chatbot, the cost estimates and the schedule availability, and communicate the cost estimates and/or the schedule availability to a user associated with the sump pump.

IPC Classes  ?

• G06Q 50/163 - Real estate management
• F04B 51/00 - Testing machines, pumps, or pumping installations
• G06N 3/045 - Combinations of networks
• G06N 3/09 - Supervised learning
• G06Q 10/109 - Time management, e.g. calendars, reminders, meetings or time accounting
• G06Q 10/20 - Administration of product repair or maintenance
• G06Q 30/0283 - Price estimation or determination
• G06Q 30/0601 - Electronic shopping [e-shopping]
• G06Q 40/08 - Insurance
• G08B 21/18 - Status alarms
• H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• H04M 3/493 - Interactive information services, e.g. directory enquiries

Abstract

A shared ledger operated by a group of network participants according to a set of consensus rules manages and resolves subrogation claims between a clamant and a defendant. Evidence regarding the value of the subrogation claim is sent to the shared ledger by entities involved in the claim such as sending to a smart contract deployed on the shared ledger. The parties to the subrogation claim may supplement evidence and settlement proposals on the blockchain by broadcasting a transaction or sending data to the smart contract. Once the claim is resolved, the parties may settle the subrogation payment off-chain or may transact a token having value on the chain. A subrogation smart contract may be programmed to release funds under certain conditions including holding a bond by a claimant and/or upon final resolution of the subrogation claim.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06Q 20/06 - Private payment circuits, e.g. involving electronic currency used only among participants of a common payment scheme
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols

Abstract

Techniques for providing recommendations of actions for a user to perform to improve efficiency in user interactions with a user interface of a user computing device. An intelligent monitoring (IM) computing system may receive data from a user computing device and may determine user interactions associated with the user interface. The user interactions may include selections, an order associated with the selections, times associated with the selections, and/or other data corresponding to user interaction with a user interface. The IM computing device may be configured to determine a fault (e.g., inefficiency) associated with the user interactions and determine an action for the user to perform to correct the fault. The IM computing device may cause a notification including the action to surface on a display of the user computing device, such as to inform the user of a means by which they can improve efficiency of the user interactions.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06N 20/00 - Machine learning
• H04L 67/50 - Network services

Abstract

A system for scheduling a callback includes instructions that, when executed by a processor, cause the processor to: generate a URL to schedule the callback, provide the URL with the embedded security token to a user device, receive information relating to a service request associated with the callback to be scheduled, receive a selection of the URL from the user device, determine a queue from which to retrieve a plurality of timeslots based on the information relating to the service request, retrieve the plurality of timeslots from the determined queue, the plurality of timeslots associated with an agent associated with the determined queue, transmit, to the user device, the plurality of timeslots, receive, from the user device, a selection of a timeslot, and schedule the callback at the selected timeslot.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations

Abstract

Systems and methods for simulating and executing configurable workflows for integration testing of data pipelines are disclosed. The method may include, such as by one or more processors: (1) receiving workflow configuration data corresponding to a data pipeline; (2) executing processes in the data pipeline by (i) accessing each process in the workflow configuration data, (ii) retrieving a corresponding script for each process, (iii) installing dependencies for each process, (iv) executing a main function of the script by passing input argument(s) and data elements, and/or (v) capturing execution log(s) and storing output data; (3) detecting an error during execution of the processes; (4) in response to the detecting, pausing a subsequent execution of the data pipeline; (5) analyzing the workflow configuration data to determine a starting process; (6) resuming the execution of the data pipeline from the starting process; and/or (7) generating a notification summarizing execution outcomes for each process.

IPC Classes  ?

• G06F 11/3668 - Testing of software
• G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance

Abstract

A computer system may be configured or programmed to: (1) receive input data from a user device of the one or more user computing devices, wherein the input data includes one or more data types; (2) input the input data into a machine learning model to identify at least some event data included within the input data, the event data associated with defining an event; (3) cause the machine learning model to extract the event data from the input data; (4) using the machine learning model, determine that the event data includes event parameters that only partially define the event; (5) using the machine learning model, identify additional event data that includes additional event parameters needed to completely define the event; and/or (6) cause the machine learning model to retrieve at least one of the additional event parameters by searching additional data sources for the additional event parameters.

IPC Classes  ?

• G06Q 10/1093 - Calendar-based scheduling for persons or groups

Abstract

A computer system is provided. The computer system may be programmed to: (a) cause a user device to present a user interface prompting a selection of an appliance; (b) receive, from the user device, a selection of a first appliance; (c) retrieve appliance data relating to the first appliance; (d) compute, using an artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the retrieved appliance data of the first appliance, wherein the artificial intelligence model is trained based upon historical appliance data including data associated with historical lifetimes of similar appliances; (e) generate a recommendation to repair or replace the first appliance based upon the predicted remaining lifetime; and/or (f) cause the user interface to present at least the predicted remaining lifetime of the first appliance and the generated recommendation.

IPC Classes  ?

• G06Q 10/20 - Administration of product repair or maintenance

Abstract

A system for providing dynamic roadside assistance coordination may include a customer mobile device or vehicle in direct, or indirect, wireless communication with an insurance provider and/or service provider remote server. The customer's device may transmit a request for roadside assistance (e.g., towing services), and a current GPS location. In response, a closest, trusted service provider vehicle may be determined, and then directed to the customer's location. For instance, a software application may receive the customer location and compare it with availability information to match the customer with a close and trusted service provider. The customer's device may receive an acknowledgement that help is on the way, and be able to track the current location of the service provider vehicle while en route. Payment for the roadside assistance may be automatically and electronically paid by the insurance provider. As a result, prompt and safe roadside assistance may be provided.

IPC Classes  ?

• H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
• G06F 16/951 - IndexingWeb crawling techniques
• G06Q 40/08 - Insurance
• H04L 9/40 - Network security protocols
• H04N 23/60 - Control of cameras or camera modules
• H04N 23/63 - Control of cameras or camera modules by using electronic viewfinders
• H04W 4/02 - Services making use of location information
• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• H04W 4/029 - Location-based management or tracking services
• H04W 12/06 - Authentication

Abstract

Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.

IPC Classes  ?

• G06Q 30/0207 - Discounts or incentives, e.g. coupons or rebates
• G06F 16/9535 - Search customisation based on user profiles and personalisation
• G06Q 30/0251 - Targeted advertisements
• G06Q 30/0282 - Rating or review of business operators or products

Abstract

Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle information related to a vehicle; (2) analyzing the vehicle information to identify one or more safety features installed in the vehicle; (3) generating, based upon the one or more safety features identified, a smart contract that may be configured to (i) maintain a set of indications related to whether safety features in the one or more safety features installed in the vehicle have been activated, and (ii) automatically execute on the blockchain based upon the maintained set of indications; and/or (4) deploying the smart contract at a particular address on the blockchain.

IPC Classes  ?

• G06Q 40/08 - Insurance
• G06Q 20/38 - Payment protocolsDetails thereof

Abstract

Methods for systems generating recommendations regarding a vehicle purchase are disclosed. An artificial intelligence (AI) or machine learning (ML) chatbot or voicebot is provided to receive input from a user, determine information regarding a type of vehicle based upon the input, and generate a total cost of ownership of the type of vehicle for presentation to the user. The chatbot may apply a natural language processing (NLP) algorithm to a text input to generate an intermediate input for use in determining the total cost of ownership. The voicebot may apply an audio recognition algorithm to an audio input to generate text, which may then be processed by the voicebot or the chatbot using an NLP algorithm to generate an intermediate input for use in determining the total cost of ownership.

IPC Classes  ?

• G06Q 30/0601 - Electronic shopping [e-shopping]
• G06F 40/40 - Processing or translation of natural language
• G06Q 30/0201 - Market modellingMarket analysisCollecting market data
• G06Q 40/08 - Insurance

Abstract

A method including receiving text image data corresponding to a claim instance from a user device. Inputting the text and image data into one or more trained machine-learning models to determine a text vector and an image vector corresponding to the text and image data. Inputting the text the image vectors into the one or more machine-learning models to determine one or more claim ratings corresponding to the claim instance. Receiving claims representative user data corresponding to one or more claims representative users from one or more data stores, wherein the claims representative user data includes a claims representative user identifier, a claims representative user availability, and a claims representative user skill level. Analyzing the claims representative user data and the one or more claim ratings to determine the best fit claims representative user. Transmitting the claim instance to a user device corresponding to the best fit claims representative user.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 40/08 - Insurance