Disclosed herein are methods and systems for identifying malicious network activity. In an embodiment, a method comprises monitoring, by a computer, network activity of a user having a baseline network activity; executing, by the computer, a machine learning model to determine a network activity score indicating a likelihood of the network activity being malicious activity for the baseline network activity, the machine learning model having been previously trained based on malicious activity and corresponding baseline network activity; and displaying, by the computer, the network activity score.
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
2.
Systems and methods for processing electronic requests
Disclosed herein are embodiments of systems, methods, and products comprises a server for efficiently processing electronic requests. The server receives a plurality of predictive computer models and a specification file for each model for registration. The server extracts validation codes for each model based on the specification file. When the server receives an electronic request, the API layer of the server validates the request by verifying the inputs of the request satisfying the validation codes of the corresponding model. If the electronic request is invalid, the server returns an error message immediately; otherwise, the API layer of the server sends the electronic request to the model execution layer. Within the model execution layer, the server executes the corresponding model based on the request inputs and generates output results. The model execution layer transmits the output results back to the API layer, which transmits the output results to the user device.
Disclosed herein are embodiments of systems, methods, and products comprises a server for efficiently processing electronic requests. The server receives a plurality of predictive computer models and generates a specification file for each model by parsing the source code of each model. When the server receives an electronic request, the API layer of the server validates the request by verifying the inputs of the request satisfying validation codes in the specification file of the corresponding model. If the electronic request is invalid, the server imputes valid values for the request and sends the imputed values to the model execution layer. Within the model execution layer, the server utilizes an integrated development environment of a third-party server to call the function of the corresponding model. The model execution layer transmits the function's output results back to the API layer, which transmits the output results to the user device.
Disclosed herein a system having an artificial intelligence model, which is executed to generate and display valuation reports on an interactive graphical user interface. The valuation reports include valuation information of companies. The valuation reports include multiple variables associated with the valuation information of the companies whose values are dynamic, and the values may be updated in real-time. The swift turnaround time of the valuation reports on the interactive graphical user interface may allow the client user to trade swiftly and efficiently.
G06Q 40/06 - Asset managementFinancial planning or analysis
G06Q 40/02 - Banking, e.g. interest calculation or account maintenance
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
5.
Systems and methods for risk factor predictive modeling with dynamic training
A system and method for dynamic model training of a predictive machine learning model accesses data points of a training dataset including a plurality of model covariates. The predictive machine learning model is configured to generate an output including a risk rank representative of a mortality risk. The method selects one of the covariates and generates a historical data distribution for the selected covariate by applying the model to the training dataset including a plurality of historical application records. The method determines a current data distribution for the selected covariate. When comparison of the current data distribution with the historical data distribution indicates a data distribution shift exceeding a predetermined threshold, the method automatically updates parameters of the predictive machine learning model and retrains the predictive machine learning model using the updated parameters. Comparison of the current data distribution with the historical data distribution may employ covariate shift adaptation.
G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
Embodiments described herein are related to systems and methods for converting hierarchical data into relational data. In one aspect, a system obtains a data object of the set of data objects in the hierarchical data. For the data object, the system can determine an anchor path and an object path. An anchor path may indicate or may be associated with a corresponding table to generate. An object path may indicate or may be associated with a corresponding column of the table. The system can determine, from a set of candidate anchor paths, an anchor path for the data object. The system can determine, from a set of candidate object paths, an object path for the data object. The system can generate a row of the table associated with the anchor path, where the row may include a value of the data object at a column associated with the object path.
Systems and methods for managing a list of huddle board participants are disclosed. The huddle collaboration system includes a huddle management system having an authentication module, a data processing module, a huddle board management module, and a module manager, among other suitable components. The system runs an automatic process to update a list of huddle boards and huddle board participants, which includes the process of adding or eliminating team members from the list of participants of one or more huddle boards and/or modifying a dotted line member's permissions within one or more huddle boards. The huddle board management module enables the automatic update of permissions assigned to a team member in one or more huddle boards, in a faster and more accurate manner; therefore enhancing the productivity of the huddle and leveraging the human and information technology resource of the company.
Discussed herein are methods and systems for an interdependent series/suite of AI models. In one embodiment, a processor receives projection assumption inputs from a user device and executes a population builder machine learning model to predict a dynamic adjustment table. It applies the model to population data to generate a value population file, which simulates a subset of the population based on the predicted table. The file contains value cells representing instances of a product. The device then runs a mortality machine learning model to determine mortality data for the product using the simulated population. Finally, it executes a flow projection model to generate a projection report for the product, incorporating mortality data and projection assumptions.
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
Disclosed are method and systems to program a server to identify the value of a fund comprising shares of multiple private entities. The server receives transaction data associated with a fund where the transaction data identifies a proportion of shares within the fund associated with each private entity, price per share of each private entity, and other relevant data. The server then executes multiple artificial intelligence models to identify comparable public entities to each private entity. The server then retrieves stock price data for each public entity and calculates a value for each private entity in real time. The server also displays a value of the fund in real time where identification of each private entity is anonymized.
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
10.
Systems and methods for remote huddle collaboration
Systems and methods for remote huddle collaboration are disclosed. The huddle collaboration system may include a huddle management system which may include an authentication module, a data processing module, and a module manager, among other components. Huddle collaboration system may allow huddle members of a company to fully engage in remote huddle sessions through different kind of client computing devices, in a consistent way regardless of their location. The system may act as an alternative to traditional whiteboards, displaying, collecting, and storing information during an active huddle session, where this information may be available to the users in the form of one or more standard and/or custom sub-applications, according to the user's and/or project's requirements.
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 3/04817 - 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 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 3/04842 - Selection of displayed objects or displayed text elements
Provided methods and systems allow dynamic rendering of a reflexive questionnaire based on a modifiable spreadsheet for users with little to no programming experience and knowledge. Some methods comprise receiving a modifiable spreadsheet with multiple rows, each row comprising rendering instructions for a reflexive questionnaire from a first computer, such as a data type cell, statement cell, logic cell, and a field identifier; rendering a graphical user interface, on a second computer, comprising a label and an input element corresponding to the rendering instructions of a first row of the spreadsheet; receiving an input from the second computer; evaluating the input against the logic cell of the spreadsheet; in response to the input complying with the logic cell of the spreadsheet, dynamically rendering a second label and a second input element to be displayed on the graphical user interface based on the logic of the first row.
A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high risk target, the applicant is required to submit clinical data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
13.
Systems and methods for generating high performant daily snapshots and trend analysis on large scale data marts
A system may include a processor configured to receive a first set of data indicating a first period of time during which a item has a first status, and a second period of time during which the item has a second status. The processor may generate, based on the first set of data, a second set of data containing a plurality of logical rows, each indicating an encoded value of the first status of the item and an encoded value of the second status of the item during a respective period of time that is smaller than or equal to each of the first period of time and the second period of time. The processor may cause a display to present, based on the first set of data and the second set of data, statistical information of the first status and the second status during a third period of time.
Methods and systems disclosed herein describe a server that generates an artificial intelligence model comprising a neural network corresponding to at least two sets of data points corresponding to a first independent variable and each data point within a second set of data points corresponding to a second variable dependent upon a corresponding first variable; executes a clustering algorithm to generate a plurality of clusters corresponding to at least one data point within the set of data points; generates a training dataset comprising a third set of data points corresponding to a pairwise distance between each two data points within at least one cluster; and trains the artificial intelligence model based on the training dataset, wherein when the trained artificial intelligence model is executed, the artificial intelligence model identifies a distance between the new data point and at least one data point within at least one cluster.
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for electronic requests routing and distribution. The server receives a plurality of requests from a plurality of electronic user devices. Aiming to routing the plurality of requests to appropriate agents, the server trains an artificial intelligence model for each agent based on historical data. For each request, the server executes the artificial intelligence model to determine a score indicating the probability of the agent converting the request to a successful sale. The server determines an entropy value for each request based on the scores and order the requests into a queue based on the entropy values. The server also calculates a capacity for each agent based on historical agent data. For each request in the queue, the server routes the request to an agent based on at least one of the score and capacity of the agent.
A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.
A system may generate all possible character mistakes in a first uniform resource locator associated with a first website, which may produce a set of unique and similar uniform resource locators associated with a set of similar websites. The system may execute machine vision algorithms to compare visual images of the first website and the set of similar websites, and identify a subset of similar websites, which may be undistinguishable from the first website. The system may block the subset of websites, and thereby prevent any user from accessing these fraudulent and malicious websites.
Systems and methods for improving computational efficiency of data processing and storage are disclosed. The system can identify computing devices capable of performing a data transformation process on a data feed of a data repository, and determine an amount of computational resources needed to perform the data transformation process on the data feed based on attributes of the data feed and computational resources used to process historic processing jobs associated with the data feed. The system can dynamically provision, while performing the data transformation process, a subset of the computing devices based on the amount of computational resources, and execute the data transformation process at the subset of the plurality of computing devices to process the data feed. The system can dynamically re-provision the subset of the plurality of computing devices based on a change in the attributes of the data feed.
This disclosure discloses systems, devices, and methods for parallelized data structure processing in context of machine learning and reverse proxy servers.
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Financial services, namely, small business administration lending services, namely, commercial lending services, loan origination services, and financing loans for small businesses Software as a Service (SAAS) services featuring software for small business administration lending, namely, for loan processing and administration, loan portfolio analytics and reporting, financing facility analytics and reporting, and loan origination analytics and reporting
22.
System and method for managing routing of customer calls
A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a Polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the Polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data. Retrieved customer data is used in predictive modeling and scoring value of the inbound call, and in routing the scored inbound call.
Embodiments disclosed herein disclose a back-end computer to generate a risk score and a front-end visualization engine to hierarchically display the generated risk core. The back-end computer users a machine learning model for a stepwise perturbation from a digital reference profile until a user profile to be score is reached. The computer may calculate intermediate risk score for each perturbation and calculate the final risk score after all the perturbations are completed. The front-end visualization engine generates an interactive hierarchical display showing information associated with the risk score calculation. More specifically, the visualization engine may show a filtered list of users sharing one or more attributes with the user profile, a visual rendering of the top factors contributing to the risk score, and individual input values within a factor; and juxtapose the scores and attributes of the user profile in the graphical information display of the associated population.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
24.
Systems and methods for contextual communication between devices
An artificial intelligence (AI) model, using logistic regression and gradient boosting tree, may determine a priority score for a user. The priority score may be associated with a likelihood of redemption of investment funds of the user by the user. Based on the priority score for the user, a server may prioritize transmission of a communication message to the user via one of a plurality of communication channels. The communication message may describe why the user should continue with their investment funds without redemption.
A computerized system and method may include, in response to receiving a blockchain via a communications network that includes information associated with an event, parsing, by a blockchain parsing engine being executed by a blockchain node, the information to identify a status state of an item related to the event. The blockchain may be inclusive of the information along with the status state of the item may be stored in a storage unit. An event tracking engine may determine from the parsed information that the status state of the item transitioned from a first state to a second state. Responsive to the event tracking engine determining that a qualifying state is satisfied by the item being in the second state, automatically executing, by the blockchain node, a smart code inclusive of initiating communications between a first party and a second party.
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 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
A system herein provides automated call-back of customers who have terminated an inbound call by exercising a call-back option of an interactive voice response unit or by abandoning the inbound call, using predictive modeling of caller value to prioritize call-backs. The call management system monitors the inbound customer call and detects any termination of the customer call. A call-back module opens a call-back record for the terminated customer call and associates that call-back record with an identified customer. The call-back module retrieves customer demographic data and other data associated with the identified customer. A predictive module determines a value prediction signal for the identified customer by modeling purchase and lapse behaviors and classifies each identified customer for either priority call-back or subordinate call-back treatment. Priority call-back classification may result in assignment to a priority call-back queue, assignment to a priority call-back queue position, or call-back by a selected agent.
A computer implemented method for safe, efficient, and fraud-proof continuous retrieval of health data is disclosed. The method comprises receiving a request to update a record associated with a user blockchain comprising identification information associated with a health tracker, a health tracker server, and user authentication data; generating an instruction to receive user data based on the identification information and user authentication data; receiving health data from the health tracker server; retrieving and verifying the validity of the user's latest blockchain; storing the data in a volatile memory; and creating a new block instance corresponding to the data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
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
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
Disclosed are method and systems to program a server to identify the value of a fund comprising shares of multiple private entities. The server receives transaction data associated with a fund where the transaction data identifies a proportion of shares within the fund associated with each private entity, price per share of each private entity, and other relevant data. The server then executes multiple artificial intelligence models to identify comparable public entities to each private entity. The server then retrieves stock price data for each public entity and calculates a value for each private entity in real time. The server also displays a value of the fund in real time where identification of each private entity is anonymized.
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
29.
Systems and methods for developing convertible term products
Methods and systems for developing profitable convertible term products are disclosed. The methods include the disaggregation of the pricing of existing term products and the use of big data analytics to identify opportunities to improve the acceptance of the product within a particular share of the market. Then, a pricing model and a selling model are built to test the product.
An underwriting estimator predictive machine learning model receives as inputs a limited number of details about an applicant, and outputs an immediate underwriting estimate of risk class. A preliminary pre-screening review redirects applicants with one or more screening impairments to a human-in-the-loop quick quote process. Model inputs include estimator inputs data that are pre-selected from the dataset of impairments data after excluding the screening impairments from the dataset of impairments. The underwriting estimator model may incorporate alternative pathways that output individualized underwriting estimates for some applicants and cohort-level marginal distributions for other applicants. Model outputs also include explanation files providing interpretability of underwriting estimates. The explanation files may include additive feature attribution data and rule based natural language explanations. The underwriting estimator predictive model may apply random forest models with smoker and non-smoker components to model inputs.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
31.
Systems and methods for reflexive questionnaire generation using a spreadsheet
Provided methods and systems allow dynamic rendering of a reflexive questionnaire based on a modifiable spreadsheet for users with little to no programming experience and knowledge. Some methods comprise receiving a modifiable spreadsheet with multiple rows, each row comprising rendering instructions for a reflexive questionnaire from a first computer, such as a data type cell, statement cell, logic cell, and a field identifier; rendering a graphical user interface, on a second computer, comprising a label and an input element corresponding to the rendering instructions of a first row of the spreadsheet; receiving an input from the second compute; evaluating the input against the logic cell of the spreadsheet; in response to the input complying with the logic cell of the spreadsheet, dynamically rendering a second label and a second input element to the displayed on the graphical user interface based on the logic of the first row.
System and method for automatically calling back a customer via a predictive model determines a plurality of call-back metrics for a plurality of advisor records. The predictive model is applied to call-back data to identify customers that are likely to require a series of call-backs, and automatically generates a preferred call-back to such customers to reduce this risk. The automated call-back may follow termination of an identified customer's inbound call, or at some time after completion of a previous call interaction of the identified customer with an advisor. In the predictive model, a first compilation of call-back metrics record is representative of an overall likelihood of call-backs associated with each advisor record, and a second compilation of the plurality of call-back metrics is representative of a likelihood of call-backs for each of the plurality of products of the enterprise associated with the advisor record.
A method comprises receiving a request to generate a customized dataset comprising data stored onto a blockchain; retrieving user work history data associated with a user from one or more block instances of the blockchain; in response to presenting the user work history data on a display of the second computing device, receiving a selection of a subset of the user work history data from the second computing device; generating a blockchain address corresponding to one or more hash values of a subset of the one or more block instances associated with the selection of the subset of the user work history data; and transmitting the blockchain address to the second computing device.
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
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
Disclosed herein are display techniques that will allow sensitive data displayed on a computer screen to only be viewed by authorized users and will render computer screen unreadable to unauthorized users. One or more display techniques are capable of automatically scrambling and unscrambling display screen of the computing device in which only an intended viewer is able to view data on the display screen using deciphering glasses.
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
G06V 40/18 - Eye characteristics, e.g. of the iris
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents, the agent being selected on the basis of likelihood of a favorable outcome. The method determines a predictive model appropriate for the identified customer, with model variables including call center data, and targeted marketing data based upon risk data for the customer. An analytical engine calculates outcome predictions by applying the predictive model to values of model variables over a recent time interval. In a time-series analysis, this calculation is repeated while dynamically adjusting the recent time interval, until identifying a call routing option that satisfies a favorable outcome criterion. This method may be used to select the agent to handle the incoming call, and optionally to select a product for that agent to discuss with the identified customer.
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which dynamically predicts future events for web users. The analytic server generates prediction models based on historical click-through analytics data received from the web server. The analytic server captures the current event (e.g., the current operation of the web user) on the web page, and determines the next event by predicting the web user behavior using the prediction models on an event-by-event basis. The analytic server also queries the web user data from a database to better understand the web user's intention, and improve the prediction accuracy. The analytic server modifies the HTML code to display the web page to include a graphical user interface comprising the predicted event. Based on the web users' reactions to the predicted event, the analytic server updates the prediction models.
Provided methods and systems allow dynamic rendering of a reflexive questionnaire based on a modifiable spreadsheet for users with little to no programming experience and knowledge. Some methods comprise receiving a modifiable spreadsheet with multiple rows, each row comprising rendering instructions for a reflexive questionnaire from a first computer, such as a data type cell, statement cell, logic cell, and a field identifier; rendering a graphical user interface, on a second computer, comprising a label and an input element corresponding to the rendering instructions of a first row of the spreadsheet; receiving an input from the second computer; evaluating the input against the logic cell of the spreadsheet; in response to the input complying with the logic cell of the spreadsheet, dynamically rendering a second label and a second input element to be displayed on the graphical user interface based on the logic of the first row.
Disclosed herein is a product inspection apparatus that may include an electronic device having a visual inspection software application. The visual inspection software application may activate a camera of the electronic device to capture images of a product, such as a vehicle. A display screen of the electronic device may present product images. The product images may be digitally processed, and augmented by the addition of computer-generated images associated with a status of inspection of each element of the vehicle in the product images. Each computer-generated image may include a graphical indicator associated with the status of inspection of a particular element. Each computer-generated image may be projected on top of a real world image of the particular element presented on the display screen.
A method comprises retrieving a file comprising a parent worksheet comprising a first row comprising a first statement, a first data type identifier, and a first logic; in response to receiving a first rendering request from a client computing device, generating a child worksheet in the spreadsheet comprising a second row, wherein the second row inherits the first row; receiving a second request to modify at least one of the first statement in the second statement cell, the first data type identifier in the second data type cell, or the first logic in the second logic cell; and rendering a graphical user interface based on the modified child worksheet.
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which evaluates user data for premium financing status and dynamically renders graphical user interfaces. The server trains an artificial intelligence model based on historical user data. The artificial intelligence model comprises one or more data points with each data point representing one of a plurality of attributes and applies a logistic regression algorithm to identify a weight factor for each attribute. The server uses a dynamic algorithm to generate a score by combining the plurality of attributes based on the weight factors. The server receives responses regarding the scores that indicate the premium financing status of each case. The server retrains the artificial intelligence model to identify new weight factors based on negative responses data. The server automatically displays new scores calculated based on the new weight factors.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
The embodiments recite systems and methods that improve the traditional underwriting process within a financial institution. These embodiments produce an underwriting model that emulates the resolution patterns of top performing underwriters. The underwriting model once is built and tested is incorporated into decision tools that provide underwriters with insightful advices when underwriting a client. The embodiments use statistical learning techniques such as support vector machine and logistic regression. These techniques can assume a linear or nonlinear relationship between factors and risk classes. Furthermore, the underwriting model also uses artificial intelligence tools such as expert systems and fuzzy logic. A company's underwriting standards and best underwriting practices may be updated periodically so that underwriting model based on decision heuristic keep improving the quality of its output over time.
This disclosure discloses systems, devices, and methods for parallelized data structure processing in context of machine learning and reverse proxy servers.
Methods and systems described herein allow dynamic rendering of a reflexive questionnaire based on a modifiable spreadsheet for users with little to no programming experience and knowledge. The method and system allow retrieving a spreadsheet to generate a dynamic and reflexive graphical user interface and to pre-populate one or more input elements within the reflexive graphical user interface based on user information retrieved from a disparate data source, where the spreadsheet may be configured for a worksheet inheritance or where the worksheet may be accessed through a check-in/check-out functionality.
G06F 16/25 - Integrating or interfacing systems involving database management systems
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Disclosed herein is a system for diagnosing faults in a vehicle using multiple audio sensors. The audio sensors are placed in predetermined locations within the vehicle. The audio sensors continually detect sound signals being originated from components of the vehicle. The audio sensors process detected sound signals to remove unwanted noise from the detected sound signals. The audio sensors compare processed sounds signals with reference sound signals to identify one or more faulty components. Each reference sound signal is associated with a particular fault. The audio sensors transmit information associated with the one or more faulty components to an analyst computer. An interactive graphical user interface of the analyst computer may present the information to an analyst.
G01N 29/44 - Processing the detected response signal
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
G07C 5/00 - Registering or indicating the working of vehicles
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
A routing system of a call center determines a plurality of advisor clusters to be assigned to each of a plurality of lead records stored in a lead model database. The predictive machine learning model inputs lead model data and advisor model data into a clustering analysis. Various modeling data are extracted from source lead data, sales data, and advisor data, in which the advisor data has been flattened for modeling. The predictive machine learning model applies a combination of a clustering analysis, a cluster model, and an aggregate conversion model to lead model data and user model data. The clustering analysis utilizes unsupervised clustering and supervised clustering, and outputs a plurality of advisor clusters and sales conversion scores. The clustering analysis clusters each of the advisors into one of the plurality of advisor clusters based on degree of similarity of a clustering vector.
Methods and systems described herein describe a central server that continuously monitors network connectivity of remote computers operated by remote employees. When a customer establishes an electronic communication session with the server (e.g., call or chat session), the server identifies one or more applications to be executed to satisfy the customer's requests. The server then calculates a network traffic value threshold corresponding to a minimum network connectivity attributes needed to execute the identified applications. The server then route the customer's electronic communication session to an agent whose remote computer satisfies the network traffic value threshold.
H04L 43/045 - Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
H04L 43/0876 - Network utilisation, e.g. volume of load or congestion level
H04L 51/046 - Interoperability with other network applications or services
H04L 41/5041 - Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
47.
Computer method and system for creating a personalized bundle of computer records
System and method for providing personalized, time-varying layered bundles of computer records to users. The system includes personalized servers, a communications network, user interfaces, and client devices employed by users. The personalized server includes a needs analysis module, a bundle building module, and an bundle generating module. A method of providing personalized bundle of computer records includes receiving a request for a personalized bundle of computer records, and requesting user needs data associated with the client. The method further includes converting the user data into determined needs data, and building a bundle of computer records personalized to the user using the determined needs data, which may include a determined needs timeline. The personalized, time varying bundle of computer records includes a plurality of computer records and plurality of types of bundles of computer records represented in the determined needs data. Following user approval of the personalized, time-varying layered bundle of computer records, the method generates the bundle of computer records based upon bundle generating criteria.
Disclosed herein a system having an artificial intelligence model, which is executed to generate and display valuation reports on an interactive graphical user interface. The valuation reports include valuation information of companies. The valuation reports include multiple variables associated with the valuation information of the companies whose values are dynamic, and the values may be updated in real-time. The swift turnaround time of the valuation reports on the interactive graphical user interface may allow the client user to trade swiftly and efficiently.
G06F 16/2457 - Query processing with adaptation to user needs
G06N 7/02 - Computing arrangements based on specific mathematical models using fuzzy logic
G06Q 40/02 - Banking, e.g. interest calculation or account maintenance
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
Disclosed herein are systems and methods of artificial intelligence learning systems. In some embodiments the artificial intelligence system presents options to users based on their life stage and personality profile. Family or group structures may be created within an application. Options may be created and presented based on the family structure such as chores may be assigned to children, money may be transferred between family members, and scores may be assigned to different users.
G09B 5/06 - Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06F 18/2134 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
50.
Systems and methods for processing air particulate datasets
Disclosed is a method and a system for efficiently and accurately processing air particulate datasets when facing a high number of air particulate datasets from multiple locations to generate an artificial intelligence model having one or more computer-based rules that determines eligibility of a user to avail a health-related service based on air particulate records associated with current and past locations of the user.
G06F 16/9035 - Filtering based on additional data, e.g. user or group profiles
G06F 16/909 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
G01N 1/22 - Devices for withdrawing samples in the gaseous state
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which aggregates different accounts for a user. The analytic server queries data associated with the user. From the queried data, the analytic server determines the accounts and the account servers. From the account servers, the analytic server queries the transactions of the accounts. The analytic server generates an instance to aggregate the determined accounts and transactions. The analytic server further scans the user's email content to determine potentially unknown transactions. The analytic server compares the potentially unknown transaction from the email content with the transactions in the instance. If there is no match, the analytic server determines the account of the potentially unknown transaction from the email content is a new account that is not aggregated. The analytic server notifies the user regarding the new account and updates the instance to reflect the new account.
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A system may include a server, which may receive a request from a customer device. The server may retrieve data records associated with the request from data sources. The server may process and present a standardized data record on analyst devices. The server may remove data gaps from the standardized data record, in response to receiving inputs from at least one analyst device. The server may generate and update status indicators on a user interface of each analyst device when any analyst device may operate on the standardized data record. The server may use a completed data record to generate a dynamic electronic document. The server may present the dynamic electronic document on a user interface of the customer device. The server may update values within the dynamic electronic document when there is a change in information within the data records.
Systems and methods for assessing the needs of customers using predictive modeling techniques are disclosed. The method receives customer data from a first database and provided by the user. The method generates an instruction to a second database and receives additional customer information received from external databases to generate a basic profile for data based on the customer. The system further analyzes data provided by the user. The method generates a customer profile based on the basic customer data and additional data. The method determines missing data from the customer profile associated and a set of attributes of the user. The method identifies a profile with the customer similar set of attributes and estimates the missing data using predictive modeling techniques to generate estimated customer information. The system further pre-populates one or more missing fields of the full profile associated with the customer based on said estimated customer information. The system. The method additionally analyzes the full updated customer profile associated with the customer to generate one or more insurance recommendations for the customer that will allow customers to fulfill one or more proposed future financial goals while ensuring the financial stability of the customer. The systems and methods disclosed allow the level of data-entry efforts required from the user to be significantly reduced.
Disclosed herein is a security training apparatus configured to operate an interactive cybersecurity training application, which provides customized and tailored cybersecurity training to each employee of an organization. The security training apparatus uses augmented reality to facilitate customized cybersecurity training for each user. The augmented reality is a computer application, which deals with the combination of real world images of personal workspace environment of each user where the cyber-crime may occur and computer generated data associated with cybersecurity risk objects that may aid the cyber-crime. The interactive cybersecurity training comprises the use of live video imagery of the personal workspace environment of each user, which is digitally processed and augmented by the addition of computer generated graphics associated with the cybersecurity risk objects. The cybersecurity risk objects are selected based on the items within the personal workspace environment for each user.
Provided method and system allow dynamic rendering of a reflexive questionnaire based on a modifiable spreadsheet for users with little to no programming experience and knowledge. The method comprises receiving a modifiable spreadsheet with multiple rows, each row comprising rendering instructions for a reflexive questionnaire from a first computer, such as a data type cell, statement cell, logic cell, and a field identifier; rendering a graphical user interface, on a second computer, comprising a label and an input element corresponding to the rendering instructions of a first row of the spreadsheet; receiving an input from the second computer; evaluating the input against the logic cell of the spreadsheet; in response to the input complying with the logic cell of the spreadsheet, dynamically rendering a second label and a second input element to be displayed on the graphical user interface based on the logic of the first row.
A computerized system and method may include, in response to receiving a blockchain via a communications network that includes information associated with an event, parsing, by a blockchain parsing engine being executed by a blockchain node, the information to identify a status state of an item related to the event. The blockchain may be inclusive of the information along with the status state of the item may be stored in a storage unit. An event tracking engine may determine from the parsed information that the status state of the item transitioned from a first state to a second state. Responsive to the event tracking engine determining that a qualifying state is satisfied by the item being in the second state, automatically executing, by the blockchain node, a smart code inclusive of initiating communications between a first party and a second party.
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 9/06 - 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
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
A DI recovery management system generates a plurality of ranked claimant records and recovery scores. A predictive machine learning model inputs disability income claim data and disability income claimant data into an event history model utilizing discrete-time survival analysis in conjunction with a gradient boosting machine learning model. The claim termination event is one of a plurality of preselected recovery events that indicate that a claimant has achieved return-to-work capacity. Claimant data used in modeling includes diagnosis data representative of workplace disability duration guidelines. The predictive machine learning model is continually trained using updated disability income claims data. The training procedure transforms claimant records extracted from a DI claims database into a longitudinal format that includes multiple person-year records corresponding to each claimant record. A DI recovery dashboard displays a hazard plot representing a conditional probability over time that a claimant will realize a claim termination event.
A system and a method for identifying and ranking agents are disclosed herein. The system includes an analytics engine which retrieves information from external and internal databases. The analytics engine uses the information retrieved from these databases, in addition to one or more success factors or key attributes, to identify and rank prospective agents. The analytics engine can also match one or more prospective agents with a general agent and provide ranking and performance assessment reports for evaluating and following up on the agent’s career development.
A method comprising displaying a first GUI to a first client comprising an option to check-out and a check-in a spreadsheet comprising at least one row comprising a statement a statement, a data type identifier, and a logic; checking-out the file such that the file cannot be modified by a second client; receiving from the first client, a modification request and a modification input; modifying the checked-out spreadsheet based on the modification input; checking-in the file; generating a set of rendering instructions corresponding to a second GUI based on the modified spreadsheet; and transmitting the set of rendering instructions to a computing device associated with a third client, whereby the set of rendering instructions causes the computing device associated with the third client to display the second graphical user interface.
A method comprises receiving a device identifier and location information from a receiving device, where the device identifier comprises a distinctive combination of at least one of numbers and characters uniquely identifying the receiving device and the location information is received from a plurality of beacons. The method comprises determining a current location associated with the receiving device based on the location information. The method comprises generating a first instruction configured to query a calendar event associated with the receiving device, transmitting the first instruction to a database, and receiving data associated with the calendar event. The method comprises, in response to the current location of the receiving device being associated with a location associated with the calendar event, generating a meeting report associated with the calendar event and transmitting the meeting report to the receiving device.
Methods and systems described herein describe a central server that continuously monitors network connectivity of remote computers operated by remote employees. When a customer establishes an electronic communication session with the server (e.g., call or chat session), the server identifies one or more applications to be executed to satisfy the customer's requests. The server then calculates a network traffic value threshold corresponding to a minimum network connectivity attributes needed to execute the identified applications. The server then route the customer's electronic communication session to an agent whose remote computer satisfies the network traffic value threshold.
H04L 43/045 - Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
H04L 43/0876 - Network utilisation, e.g. volume of load or congestion level
H04L 51/046 - Interoperability with other network applications or services
H04L 41/5041 - Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
62.
System and method for managing routing of customer calls
A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data. Retrieved customer data is used in predictive modeling and scoring value of the inbound call, and in routing the scored inbound call.
A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.
Methods and systems disclosed herein allow data to be transferred from a data source to a target database with little to no offline period or data corruptions. The methods and systems describe a server that generates a temporary data repository having a similar configuration as the target data repository; transmits the set of new data records from the data source to the temporary data repository; identifies dependency relationship attributes among the data records stored within the target data repository; and when the server identifies that a predetermined number of data records and their respective dependent data records are stored within the temporary data records, the server merges the set of data records and the set of new data records. The server also stores a pre/post merger record of data such that the server can revert to a previous version of data or roll forward to another version.
G06F 3/06 - Digital input from, or digital output to, record carriers
G06F 7/14 - Merging, i.e. combining at least two sets of record carriers each arranged in the same ordered sequence to produce a single set having the same ordered sequence
65.
Method of evaluating heuristics outcome in the underwriting process
Systems and methods for validating outputs from a machine learning model is disclosed. The machine learning model and a statistical model are executed to generate electronic documents in response to customer requests. A random sample of electronic documents generated from the machine learning model and the statistical model are then selected. A comparison is performed between the random sample of electronic documents generated from the machine learning model and the statistical model. The performance of the machine learning model is validated based on results of the comparison.
Methods and systems disclosed herein describe automatically establishing two concurrent electronic communication sessions. Participants of a primary electronic communication session may request a private (secondary) electronic communication session in which only a subset of the participants of the primary electronic communication session can participate. Methods and systems described herein also describe automatically identifying participants of the second electronic communication session based on various factors including predetermined lists, commonality among different users or user identifiers, and geographic location of each participant of the primary and/or secondary electronic communication session. The methods and systems described herein also describe monitoring location of all participants of the primary and secondary electronic communication sessions and causing input and output elements of various electronic devices based on each user's location and/or whether the user is participating in the secondary or primary electric communication session.
H04L 65/401 - Support for services or applications wherein the services involve a main real-time session and one or more additional parallel real-time or time sensitive sessions, e.g. white board sharing or spawning of a subconference
H04L 65/4053 - Arrangements for multi-party communication, e.g. for conferences without floor control
H04L 12/18 - Arrangements for providing special services to substations for broadcast or conference
H04M 3/56 - Arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities
67.
Systems and methods for electronic request routing and distribution
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for electronic requests routing and distribution. The server receives a plurality of requests from a plurality of electronic user devices. Aiming to routing the plurality of requests to appropriate agents, the server trains an artificial intelligence model for each agent based on historical data. For each request, the server executes the artificial intelligence model to determine a score indicating the probability of the agent converting the request to a successful sale. The server determines an entropy value for each request based on the scores and order the requests into a queue based on the entropy values. The server also calculates a capacity for each agent based on historical agent data. For each request in the queue, the server routes the request to an agent based on at least one of the score and capacity of the agent.
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which determines user health attributes by tracking the user’s behaviors and activities within a predetermined space. The analytic server receives tracking data from a set of sensors installed in the predetermined space. The sensors track a beacon worn by the user. The analytic server determines micro-locations and user behaviors based on the tracking data. The analytic server determines the coordinates of the sensors based on the sensor identifiers and maps the coordinates to regions by referring to a floor plan map. The analytic server determines the user behaviors and activities by aggregating the micro-locations and regions the user visited at different time. The analytic server determines the user’s health score based on the micro-locations and user behaviors by executing an artificial intelligence model. The analytic server determines a recommendation of premium based on the health score.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
H04W 4/33 - Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
H04W 4/029 - Location-based management or tracking services
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
69.
Systems and methods for risk factor predictive modeling with model explanations
A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. Fluidless models are trained by application of a random forest ensemble including survival, regression, and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high risk target, the applicant is required to submit clinical data, and an explanation model generates an explanation file for user interpretability of any high risk model prediction and the adverse underwriting decision.
G06Q 20/00 - Payment architectures, schemes or protocols
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
Methods and systems disclosed herein utilize location signals received from beacons and other indoor positioning systems along with an application program on customer devices for better management of customer traffic in physical queues and virtual queues, specifically in environments such as airports, food courts, shopping malls, and amusement parks. These methods and systems also provide a customer with a token for his place in the queue on his mobile device, so he is free to continue with his activities until it is time for him to acquire a product or a service.
Method and system disclosed herein facilitate retrieval of a blockchain key. The method comprises receiving a key store comprising a first encryption method, a second encryption method, and identification information of one or more network nodes storing a plurality of encrypted storage keys; displaying an authentication request and receiving and input form the user in response to the authentication request; upon the input received matching a record within a database, instructing the one or more network nodes to transmit the encrypted key segments; decrypting each encrypted key segment based on the first encryption method; and generating a blockchain key by appending the strings of the key segments based on the second encryption method.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
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 9/06 - 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 67/10 - Protocols in which an application is distributed across nodes in the network
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
72.
Systems and methods for excluded risk factor predictive modeling
A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high risk target, the applicant is required to submit clinical data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Various systems and methods use a value in a data file for a data process, as the data process is scaled up in terms of dataset dimensionality, data volume, data types, data content, data source quantity, and data source speed, while remaining compliant with ACID principles. As such, these technologies provide for sourcing of data from various data sources, where the data includes the data file storing the value. The data is cleansed and fused, which enables a report to be generated. In response to the value in the data file being modified, the data, inclusive of the data file storing the value, is again cleansed and fused based on the value being modified. This processing in-turn enables the report to modified based on the value being modified.
A system herein provides automated call-back of customers who have terminated an inbound call by exercising a call-back option of an interactive voice response unit or by abandoning the inbound call, using predictive modeling of caller value to prioritize call-backs. The call management system monitors the inbound customer call and detects any termination of the customer call. A call-back module opens a call-back record for the terminated customer call and associates that call-back record with an identified customer. The call-back module retrieves customer demographic data and other data associated with the identified customer. A predictive module determines a value prediction signal for the identified customer by modeling purchase and lapse behaviors and classifies each identified customer for either priority call-back or subordinate call-back treatment. Priority call-back classification may result in assignment to a priority call-back queue, assignment to a priority call-back queue position, or call-back by a selected agent.
The embodiments described herein relate to a method and system for social awareness which may be based on social networks for knowledge exchange. More specifically, the embodiments may refer to specific social networks with social elements in the user interface based on knowledge exchange, social theory of group memberships within an enterprise or organization context. In addition, the disclosed group memberships may be predicated upon many different types of relationships. Furthermore, the social network (through a program interface) may provide to users the required specific project resources (project team members), which may be need to develop a better project performance according to the experience and knowledge of the new members. The required project team members may match with the attributes and criteria established during the project planning.
G06F 16/25 - Integrating or interfacing systems involving database management systems
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
An artificial intelligence (AI) model, using logistic regression and gradient boosting tree, may determine a priority score for a user. The priority score may be associated with a likelihood of redemption of investment funds of the user by the user. Based on the priority score for the user, a server may prioritize transmission of a communication message to the user via one of a plurality of communication channels. The communication message may describe why the user should continue with their investment funds without redemption.
Provided methods and systems allow dynamic rendering of a reflexive questionnaire based on a modifiable spreadsheet for users with little to no programming experience and knowledge. Some methods comprise receiving a modifiable spreadsheet with multiple rows, each row comprising rendering instructions for a reflexive questionnaire from a first computer, such as a data type cell, statement cell, logic cell, and a field identifier; rendering a graphical user interface, on a second computer, comprising a label and an input element corresponding to the rendering instructions of a first row of the spreadsheet; receiving an input from the second computer; evaluating the input against the logic cell of the spreadsheet; in response to the input complying with the logic cell of the spreadsheet, dynamically rendering a second label and a second input element to be displayed on the graphical user interface based on the logic of the first row.
Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents, the agent being selected on the basis of likelihood of a favorable outcome. The method determines a predictive model appropriate for the identified customer, with model variables including call center data, and targeted marketing data based upon risk data for the customer. An analytical engine calculates outcome predictions by applying the predictive model to values of model variables over a recent time interval. In a time-series analysis, this calculation is repeated while dynamically adjusting the recent time interval, until identifying a call routing option that satisfies a favorable outcome criterion. This method may be used to select the agent to handle the incoming call, and optionally to select a product for that agent to discuss with the identified customer.
The methods and systems disclosed herein provide a server that periodically monitors a user's location using one or more beacons while periodically monitoring hazardous conditions using a variety of electronic sensors, such as thermographic imaging. When the user is within a predetermined proximity of a hazardous condition the server transmits an instruction to an electronic wearable device to present a notification (e.g., haptic, noise, and the like) warning the user of the hazardous condition.
In one embodiment of the present disclosure, an ingestible medication device is a self-contained electronic device that stores an active agent, and that controls release of the active agent using an on board processor. The ingestible medication device embodies one or more ingestible device identifiers, including personal identifiers and active agent identifiers, which are compared with external device identifiers to determine whether to release the active agent. A method for managing an ingestible medication device detects proximity to a limited range, RFID-enabled patient wristband, indicating that the wristband is worn by the patient that ingested the ingestible medication device. Various methods enable a nurse to track medication information to monitor compliance with medication regimen and dosage information. Other methods track an ingestible medication device selected for filling a prescription at a pharmacy of the health care provider, including transfer to a caregiver station using a transport cart.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
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
A61B 90/98 - Identification means for patients or instruments, e.g. tags using electromagnetic means, e.g. transponders
81.
Augmented reality system for remote product inspection
Disclosed herein is a product inspection apparatus that may include an electronic device having a visual inspection software application. The visual inspection software application may activate a camera of the electronic device to capture images of a product, such as a vehicle. A display screen of the electronic device may present product images. The product images may be digitally processed, and augmented by the addition of computer-generated images associated with a status of inspection of each element of the vehicle in the product images. Each computer-generated image may include a graphical indicator associated with the status of inspection of a particular element. Each computer-generated image may be projected on top of a real world image of the particular element presented on the display screen.
A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which evaluates user data for premium financing status and dynamically renders graphical user interfaces. The server trains an artificial intelligence model based on historical user data. The artificial intelligence model comprises one or more data points with each data point representing one of a plurality of attributes and applies a logistic regression algorithm to identify a weight factor for each attribute. The server uses a dynamic algorithm to generate a score by combining the plurality of attributes based on the weight factors. The server receives responses regarding the scores that indicate the premium financing status of each case. The server retrains the artificial intelligence model to identify new weight factors based on negative responses data. The server automatically displays new scores calculated based on the new weight factors.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
Systems for assisting users in selecting various promotional offers while shopping online are disclosed. The system is configured to receive promotional information being offered by one or more business merchants to a user, determine a navigation to a webpage on a user device by the user from where an item can be purchased, identify one or more attributes associated to the webpage, determine one or more promotions from the promotional information based on the one or more attributes identified, and display on the user device of the user the one or more promotions associated with the item being purchased by the user on the webpage of the user device.
A transdermal delivery device for dispensing personalized transdermal dosage formulations from a plurality of reservoirs, and a personalized method for producing the transdermal delivery device. A prescription fill service receives electronic prescription data for a plurality of transdermal dosage formulations to be administered to a given patient. The prescription fill service deposits transdermal dosage formulations in two or more reservoirs of a transdermal device substrate via 3D printing of printable pharmaceutical agent. The electronic prescription data may include transdermal dosage formulations data used to select printable pharmaceutical agent deposited in respective reservoirs. The electronic prescription data further may include medication regimen data for administration of transdermal medications, such as timing data for release of selected transdermal dosage formulations. In an embodiment, a finished transdermal delivery device includes barriers formed at reservoir openings, a controller, and a controlled energy source that degrades the barriers to actuate release of reservoir contents.
B33Y 30/00 - Apparatus for additive manufacturingDetails thereof or accessories therefor
B29C 64/20 - Apparatus for additive manufacturingDetails thereof or accessories therefor
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
A61M 37/00 - Other apparatus for introducing media into the bodyPercutany, i.e. introducing medicines into the body by diffusion through the skin
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
The system and methods described herein provide for managing user datasets by facilitating interactions between users and their advisors following location-based notification of certain triggering events in the user dataset. The geolocation of the user is used to identify nearby advisors who can provide consultation as required by the user. Some embodiments facilitate introductions to a potential user of a set of advisors matched to the user's profile and in response to certain triggering events in the user's dataset.
H04L 67/52 - Network services specially adapted for the location of the user terminal
G06Q 40/06 - Asset managementFinancial planning or analysis
H04L 51/222 - Monitoring or handling of messages using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
G06F 16/9537 - Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
A computerized system and method may include, in response to receiving a blockchain via a communications network that includes information associated with an event, parsing, by a blockchain parsing engine being executed by a blockchain node, the information to identify a status state of an item related to the event. The blockchain may be inclusive of the information along with the status state of the item may be stored in a storage unit. An event tracking engine may determine from the parsed information that the status state of the item transitioned from a first state to a second state. Responsive to the event tracking engine determining that a qualifying state is satisfied by the item being in the second state, automatically executing, by the blockchain node, a smart code inclusive of initiating communications between a first party and a second party.
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 9/06 - 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
A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data. Retrieved customer data is used in predictive modeling and scoring value of the inbound call, and in routing the scored inbound call.
Disclosed is a method and a system for receiving a request to generate an anonymized pool dataset, wherein the request comprises overall number of people in the dataset, one or more genetic condition categories, a genetic attribute associated with each category, and a percentage of the number of users associated with each category; querying and receiving from a second server a set of datasets associated with people, wherein each dataset comprises health data associated with a user and a corresponding genetic data; generating a pool dataset from the anonymized set of datasets, wherein the pool dataset corresponds to the received overall number of users in the dataset, genetic condition categories, the genetic attribute associated with each category, and a percentage of the number of people in each category.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
90.
Systems and methods for processing electronic requests
Disclosed herein are embodiments of systems, methods, and products comprises a server for efficiently processing electronic requests. The server receives a plurality of predictive computer models and a specification file for each model for registration. The server extracts validation codes for each model based on the specification file. When the server receives an electronic request, the API layer of the server validates the request by verifying the inputs of the request satisfying the validation codes of the corresponding model. If the electronic request is invalid, the server returns an error message immediately; otherwise, the API layer of the server sends the electronic request to the model execution layer. Within the model execution layer, the server executes the corresponding model based on the request inputs and generates output results. The model execution layer transmits the output results back to the API layer, which transmits the output results to the user device.
A computerized system and method may include, in response to receiving a blockchain via a communications network that includes information associated with an event, parsing, by a blockchain parsing engine being executed by a blockchain node, the information to identify a status state of an item related to the event. The blockchain may be inclusive of the information along with the status state of the item may be stored in a storage unit. An event tracking engine may determine from the parsed information that the status state of the item transitioned from a first state to a second state. Responsive to the event tracking engine determining that a qualifying state is satisfied by the item being in the second state, automatically executing, by the blockchain node, a smart code inclusive of initiating communications between a first party and a second party.
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 9/06 - 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
An advisor distribution system may include an advisor management system, which may include various software modules. The advisor management system may allow for a balanced distribution of a plurality of advisors operating a plurality of advisor computing devices into multiple groups based on value of a Mahalanobis Distance between each covariate of the plurality of advisors operating the plurality of advisor computing devices.
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for electronic requests routing and distribution. The server receives a plurality of requests from a plurality of electronic user devices. Aiming to routing the plurality of requests to appropriate agents, the server trains an artificial intelligence model for each agent based on historical data. For each request, the server executes the artificial intelligence model to determine a score indicating the probability of the agent converting the request to a successful sale. The server determines an entropy value for each request based on the scores and order the requests into a queue based on the entropy values. The server also calculates a capacity for each agent based on historical agent data. For each request in the queue, the server routes the request to an agent based on at least one of the score and capacity of the agent.
Embodiments disclosed herein disclose a back-end computer to generate a risk score and a front-end visualization engine to hierarchically display the generated risk core. The back-end computer users a machine learning model for a stepwise perturbation from a digital reference profile until a user profile to be score is reached. The computer may calculate intermediate risk score for each perturbation and calculate the final risk score after all the perturbations are completed. The front-end visualization engine generates an interactive hierarchical display showing information associated with the risk score calculation. More specifically, the visualization engine may show a filtered list of users sharing one or more attributes with the user profile, a visual rendering of the top factors contributing to the risk score, and individual input values within a factor; and juxtapose the scores and attributes of the user profile in the graphical information display of the associated population.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
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
95.
Systems, devices, and methods for parallelized data structure processing
This disclosure discloses systems, devices, and methods for parallelized data structure processing in context of machine learning and reverse proxy servers.
Systems for assisting users in selecting various promotional offers while shopping online are disclosed. The system is configured to receive promotional information being offered by one or more business merchants to a user, determine a navigation to a webpage on a user device by the user from where an item can be purchased, identify one or more attributes associated to the webpage, determine one or more promotions from the promotional information based on the one or more attributes identified, and display on the user device of the user the one or more promotions associated with the item being purchased by the user on the webpage of the user device.
Embodiments of systems and methods for fraud review are disclosed. The systems may comprise multi-tiered computing systems which may receive fraud alerts from multiple sources. A computing system in a tier may receive a fraud alert and use one or more fraud risk metrics to determine whether the fraud alert should be escalated. If the computing system determines that the fraud alert should be escalated, the computing system may transmit an escalation message to a higher tier computing system. If the computing system determines that the fraud alert should not be escalated, the computing system may transmit a message to a fraud prevention computing system. In some embodiments, the computing system may determine that the fraud alert is a false positive and transmit a false positive message to the source of the fraud alert such as a lower tier computing system.
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Insurance consulting in the field of life insurance; insurance services, namely, underwriting, issuing and administration of life insurance Development and implementation of software and online computer software systems for purchasing insurance, insurance underwriting, insurance policy issuing, providing insurance administration, insurance quoting, rating, and billing, and insurance claims processing and management, all in the field of life insurance; software as a services (SAAS) services featuring software for purchasing insurance, insurance underwriting, insurance policy issuing, providing insurance administration, insurance quoting, rating, and billing, and insurance claims processing and management, all in the field of life insurance; computer services, namely, providing a website featuring technology that enables users to gather information about life insurance, obtain personalized life insurance needs analysis, obtain price quotes, and apply online for such insurance; providing a website for others featuring technology that automates the insurance underwriting process enabling users to apply for and receive insurance policies; providing a hosted, maintained website featuring technology that enables the website user to choose from an all-inclusive set of options for insurance policy issuance and delivery and receive on-going policyholder services
Disclosed are method and systems to program a server to identify the value of a fund comprising shares of multiple private entities. The server receives transaction data associated with a fund where the transaction data identifies a proportion of shares within the fund associated with each private entity, price per share of each private entity, and other relevant data. The server then executes multiple artificial intelligence models to identify comparable public entities to each private entity. The server then retrieves stock price data for each public entity and calculates a value for each private entity in real time. The server also displays a value of the fund in real time where identification of each private entity is anonymized.
Embodiments disclosed herein describe intelligent e-book readers which provide a significant improvement over the conventional e-books that simply render static content. The intelligent e-book readers may customize a rendered e-book based on, for example, the reading level and preferences of the user, the user's social media profile and activity, and current events. Furthermore, the intelligent e-book reader may provide additional augmented reality (AR)/virtual reality (VR) content associated with one or more portions of the rendered e-book. The intelligent e-book reader may also facilitate virtual, real time communication between multiple users and experts. The intelligent e-book reader may also facilitate one or more users to provide feedback and suggestions to authors and future movie-makers. The intelligent e-book reader may automatically determine difficult portions of an e-book based on the virtual communications and/or real time eye-tracking of a user.
