A method of encrypting information provided to a large language model (“LLM”) can include receiving first unencrypted information, identifying a first word within the first unencrypted information that is to be encrypted, replacing the first word within the first unencrypted information with an automatically generated first key to create first encrypted information, automatically replacing all instances of the first word with the first key to maintain referential integrity amongst the first word and the first encrypted information, and providing the first encrypted information to the LLM along with a first prompt requesting that the LLM generate a first encrypted output dependent upon the first encrypted information. The example method can further include receiving, from the LLM, the first encrypted output dependent upon the first encrypted information and replacing, using the first word-key pair database, all instances of the first key with the first word to create a first unencrypted output.
A method of interacting with a large language model to elicit a semantic feature of interest from a document under review includes electronically inputting, in an application program interface of a chat application, a first prompt assigned to a user, the first prompt yielding a plurality of possible responses from the language model based on content of the document under review; generating an example set comprising text from example documents representative of each of the plurality of possible responses; and electronically inputting, before the first prompt in an application program interface of a chat application, a fabricated history of a conversation between the user and the language model. The fabricated history includes the example set and a plurality of possible responses assigned to the language model.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
3.
ENHANCED ENCRYPTION WITH FORMAT PRESERVATION AND REFERENTIAL INTEGRITY FOR USE WITH A LARGE LANGUAGE MODEL
A system for encrypting information for use with a large language model can include unencrypted information; an identification module configured to identify words to be encrypted in the unencrypted information; a key generation module configured to generate keys corresponding to the words to be encrypted in the unencrypted information, the keys having a similar format as the corresponding words so that the keys preserves the format of the corresponding words to maintain a similar context; a replacement module configured to replace the words with the corresponding keys; and a prompt module configured to determine a prompt to the large language model requesting the large language model to determine an encrypted output based upon the encrypted information, wherein, in response to the reception of the encrypted output from the large language model, the replacement module is configured to replace the keys with the corresponding words to form an unencrypted output.
A system for determining a natural language output regarding a digital network using a large language model (“LLM”) can include a computer processor configured to receive a desired output dependent upon information associated with the digital network; a prompt module configured to determine a query prompt to the LLM requesting the LLM to generate a query dependent upon the information and the desired output; and a graph database management system configured to determine, dependent upon a graph database representative of at least a portion of the digital network, a response to the query as received from the LLM, wherein the prompt module is also configured to determine an output prompt to the LLM requesting the LLM to generate the natural language output dependent upon the response to the query, and wherein the LLM generates the natural language output as requested in the output prompt.
A method of determining a natural language output regarding a digital network using a large language model (“LLM”) can include formulating a desired output dependent upon information associated with the digital network; providing, to the LLM, the information associated with the digital network and a first prompt requesting the LLM to generate a query dependent upon the information and the desired output; receiving, from the LLM, the query dependent upon the information and the desired output; determining, dependent upon a graph database, a response to the query with the graph database being representative of at least a portion of the digital network; providing, to the LLM, the response and a second prompt requesting the LLM to generate the natural language output dependent upon the response; and receiving, from the LLM, the natural language output dependent upon the response and associated with the digital network.
A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06T 5/90 - Dynamic range modification of images or parts thereof
G06V 10/36 - Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given pointNon-linear local filtering operations, e.g. median filtering
G06V 20/40 - ScenesScene-specific elements in video content
H04N 23/661 - Transmitting camera control signals through networks, e.g. control via the Internet
H04N 23/80 - Camera processing pipelinesComponents thereof
A method of selectively migrating data for a mixed server environment including deleting a first dataset from the first database server in response to a first delete command issued to the first database management system, issuing a second delete command after deleting the first dataset, copying to a second database server a complete set of data stored by the first database server, starting a second application server, starting a second business management software platform on the second application server, and providing the second business management software platform with the business software configuration data from the second database. The first dataset is deleted by a first database management system of a first database server, and the second delete command is issued to a first business management software platform and instructs the first business management software platform to delete the first dataset.
A method of improving a main output of a main processing application for processing first video data can include preprocessing incoming video data according to first preprocessing parameters, wherein the preprocessing includes formatting the incoming video data to create the first video data, and processing the first video data by the main processing application to determine the main output. The method can further include identifying first test preprocessing parameters, preprocessing the incoming video data according to the first test preprocessing parameters to create first test video data, processing the first test video data by a test processing application to determine a first test output, comparing the first test output to a baseline criterion, and, in response to the first test output satisfying the baseline criterion, altering the first preprocessing parameters to be at least substantially similar to the first test preprocessing parameters.
Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Installation of computer hardware, servers, and networks;
maintenance and repair of computer hardware, servers, and
networks. Consulting services in the fields of selection and
implementation of computer hardware and software systems for
others for voice over internet protocol (VOIP) and internet
telephony; consulting services, namely, computer software,
hardware, server, network and work station selection,
implementation and design for others; computer software,
hardware, server and network development; technical support,
namely, testing technological functions of computer
hardware, servers and networks; computer services, namely,
configuration of computer hardware, software, servers and
networks; installation of computer software; implementation
of computer software; testing and maintenance of computer
software; technical support services, namely,
troubleshooting of computer hardware and software problems;
maintenance of computer software; computer security
services, namely, restricting access by computer networks in
connection with e-mail, spam, viruses, worms, websites, or
other undesired content.
11.
POLLING AND SERIAL PROMPTING FOR TRAVERSAL OF MULTIPLE SPECIALIST LANGUAGE MODELS
A method of processing a compound prompt using a plurality of specialized large language models (LLMs) includes decomposing the compound prompt into a plan with multiple steps. For each step, an approach defining a subset of the specialized LLMs is selected and executed to produce multiple model outputs, and these model outputs are collectively used to generate a step output. The step outputs associated with each step are assembled into a syntactically and semantically coherent final output via an integration module utilizing a large language model.
A method of processing a compound prompt includes decomposing the compound prompt into a plan having multiple steps, and associating a domain descriptor with each of several specialized machine learning models. For each step of the plan, a relevance score for each specialized model is assigned by semantic comparison between its domain descriptor and the step. A subset of the models are selected based on relevance score and used to produce a step output. The outputs of all steps are assembled into a syntactically and semantically coherent final output via an integration module utilizing a large language model.
A method of automated labeling of text data includes receiving a first query vector and receiving a plurality of labeled reference vectors. The first query vector represents a first unlabeled text segment and each labeled reference vector corresponds to a labeled text segment of a plurality of labeled text segments and is labeled according to the corresponding labeled text segment of the plurality of labeled text segments. The method further comprises generating a first subset of reference vectors of the plurality of reference vectors by comparing the first query vector to each reference vector of the plurality of reference vectors, determining that a first label of the first subset of labeled reference vectors has a numerosity exceeding a first threshold value, and labeling the first unlabeled text segment with the first label to create a first labeled text segment.
A method of processing a compound prompt includes mapping a domain to each of several distinct machine learning models (MLMs) and decomposing the compound prompt into a plan having several steps. For each step, one of the MLMs is selected by matching the step to a corresponding mapped domain, and a language output is generated using the selected MLM. These outputs are integrated into a syntactically and semantically coherent final output using a large language model.
A method includes receiving a natural-language prompt from a user and a user identifier corresponding to the user, querying a first database with the user identifier to retrieve first information, generating a vector embedding representative of the first information and the natural-language prompt, and querying a second database using the vector embedding to retrieve second information. The second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, and the second information comprises at least one text segment of the plurality of text segment. The method further includes generating, by a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information.
A method of hybrid technical support includes receiving, by a network-connected device, a first user prompt including at least one technical support query and generating, by a language model executed by the network-connected device, a first natural-language response to the first user prompt, the first natural-language response configured to elicit first additional information describing the at least one technical support query. The method further includes receiving, by the network-connected device, a second user prompt including the first additional information describing the at least one technical support query, generating a pre-summarization prompt based on the first user prompt and the second user prompt, generating a summarization of the pre-summarization prompt using a language summarization model executed by the network-connected device, and providing the summarization to a support technician device configured to be operated by a support technician. The language summarization model is configured to generate summaries of text prompts.
A method of automated technical support includes receiving a natural-language prompt from a user and a user identifier corresponding to the user, the natural-language prompt including at least one technical support query, querying a first database with the user identifier to retrieve first information, generating a vector embedding representative of the first information and the natural-language prompt, querying a second database using the vector embedding to retrieve second information, and generating a natural-language response text based on the natural-language prompt, the first information, and the second information. The second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, the second information comprises at least one text segment of the plurality of text segments, and the natural-language response text responsive to the at least one technical support query.
A method includes receiving, by a server and from a user device, a natural-language text prompt provided by the user to a chat application operating on the user device and at least one user preference for a user, the at least one user preference indicative of at least one preferred characteristic of natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs. The method further includes, by the server, modifying a system prompt for the machine-learning language model with the received at least one user preference to generate a modified system prompt, providing the modified system prompt as an initial input to the machine-learning language model, providing the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output after providing the modified system prompt, and transmitting the natural-language text output to the user device.
Instances of an identity and access management (IAM) system access resources through a cloud platform. An association module executed on control circuitry of a computing device is configured to determine a resident location of a base instance of the IAM system, obtain manager identity data for a manager instance of the IAM system associated with the resident location, and edit access settings for one or more containers within the base instance to provide access to the manager instance. The manager instance can access the one or more containers to service the resources within the one or more containers.
A method of automated network documentation includes receiving a first network address for a first device connected to a local network, sending a first simple network management protocol (SNMP) request to the first network address for the first device, receiving a first SNMP response from the first device in response to the first SNMP request, and extracting first device identity information from the first SNMP request. The first network address is received by, the first SNMP request is sent by, the first SNMP response is received by, and the device identity information is extracted by a server configured to operate a network documentation platform, the network documentation platform comprising a network documentation database. The method further includes providing the first device identity information to the network documentation platform and modifying the network documentation database to create first device documentation data based on the first device identity information.
H04L 41/0853 - Retrieval of network configurationTracking network configuration history by actively collecting configuration information or by backing up configuration information
21.
INTERMITTENT SYNCHRONIZATION FOR NETWORK MANAGEMENT DATA
A method of network management data synchronization includes instantiating a local instance of a network management platform on an edge device and instantiating a remote instance of the network management platform on a remote server. The edge device is directly connected to a local network and to a wide area network, and the local instance of the network management platform maintains a first instance of network management data. The remote server is directly connected to the wide area network and not to the first local network, and the remote instance of the network management platform maintains a second instance of the network management data. The method further includes modifying the first instance of the network management data, providing an indication of at least one modification made to the first instance of the network management data to the remote server, and modifying the second instance of the network management data.
H04L 41/0816 - Configuration setting characterised by the conditions triggering a change of settings the condition being an adaptation, e.g. in response to network events
A method of selectively providing data from a caching edge device includes receiving a request from a local device for data stored on a server, the caching edge device configured to receive requests from the local device and to selectively forward the requests to the server and determining whether to deliver to the local device a copy of the data by analyzing the request using a forwarding rules engine, the copy of the data stored to a memory of the caching edge device. The method further includes, after determining to deliver the copy of the data to the local device, electronically transmitting the copy of the data from the memory of the edge caching device to the local device and blocking, by the edge caching device, the request from the local device to the server.
A system for training a user for a conversational encounter with a customer receives encounter-defining parameters via a user interface. The encounter-defining parameters are descriptive of attributes of the conversational encounter. The system formats the encounter-defining parameter for transmission to a large language model (LLM). The LLM receives the instructions and outputs a conversational opening viewable by a user via a user interface. The user then responds to the conversational opening and iteratively converses with the LLM. After a defined maximum number of iterations have been reached, the LLM provides an evaluation to the user via the user interface. The evaluation is indicative of the evaluated outcome of the conversational encounter, including at least one of a score, a summary of the encounter, a likelihood that a deal is reached, and suggested improvements.
A method of organizing a software offering development process is disclosed herein that includes selecting a framework reflective of an extent to which the software offering is to be developed and providing, based on the selected framework, a list of development roles needed to complete the software offering development process. The method can also include designating individuals to fill each of the development roles within the list of development roles and, in response to the selection of the framework, creating the software offering development process based at least in part on the selected framework with the process being divided into multiple phases of development with each phase having multiple tasks automatically assigned to at least one of the individuals depending on the individuals designated to fill each of the development roles. The method can further include creating, based on the selected framework, a development schedule for the offering.
A method of database operations includes receiving a user query, generating a query vector embedding representative of the user query, querying a vector database using the query vector embedding, retrieving a first database vector of the plurality of database vectors based on the query and representative of a first data file corresponding to a first time and belonging to a first time-series data set, receiving a first plurality of delta encodings describing differences between vector representations of temporally-adjacent data files of the first time-series data set, identifying a second data file of the first time-series data set having a second vector representation that differs from the first database vector and corresponds to a second time, and retrieving the second data file from a database.
A method of generating a support summary includes extracting chat information including natural language data from a support chat with a user. Tokenized language is generated by performing feature extraction on this natural language data. This tokenized language is subjected to sentiment analysis to produce sentiment data reflecting sentiment of the support user during the support chat, and to semantic analysis to extract support-relevant features. A support summary made up of natural language text identifying a support issue and information germane to the support issue is then generated from the extracted chat information using a language model, at least in part from the extracted support-relevant features and the sentiment data.
A method of formulating a desired network configuration graph database representative of a network can include receiving network information regarding multiple devices; generating an existing network configuration graph database representative of the multiple devices; and determining whether each interface description for each device correctly describes the device by comparing each interface description to an actual state of the device. The method can include, in response to the interface description being inaccurate, generating an entry in an interface description database that includes identification information of the device, the incorrect interface description for the device, and a correct interface description for the device for each incorrect interface description; formulating the desired network configuration graph database representative of the multiple devices; providing the desired network configuration graph database and a first prompt to a large language model; and determining an output regarding the network and dependent upon the desired network configuration graph database.
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
A name conversion system includes computer-readable memory encoded with instructions that, when executed by one or more processors, cause the name conversion system to make name changes identified in a list of name conversions to tables of data by identifying name changes for records in the tables in a database, identifying each table in the database, determining whether each table has an index, searching a column of a table designated in the index and changing one or more names of records in the column as identified in the list of name conversions so that any column not included in the index is not searched when the table has an index, and searching an entirety of a table and changing one or more names of records in the table as identified in the list of name conversions when the table does not have an index.
A method of collecting and conditioning image data of an item for use in training a machine-learning model to detect at least one defect can include capturing a first set of images by a first camera with each image of the first set of images having a first viewpoint of the item that is the same viewpoint as the other images in the first set of images and can be illuminated by various wavelengths of light including ultraviolet light, infrared light, and visible light. The method can further include examining one image of the first set of images to determine if the item contains at least one defect, identifying a first location of the at least one defect on the one image, designating the first location on the other images of the first set of images so that all images in the first set of images identify the first location.
A method of automated network documentation includes receiving a first network address for a first device connected to a local network, sending a first simple network management protocol (SNMP) request to the first network address for the first device, receiving a first SNMP response from the first device in response to the first SNMP request, and extracting first device identity information from the first SNMP request. The first network address is received by, the first SNMP request is sent by, the first SNMP response is received by, and the device identity information is extracted by a server configured to operate a network documentation platform, the network documentation platform comprising a network documentation database. The method further includes providing the first device identity information to the network documentation platform and modifying the network documentation database to create first device documentation data based on the first device identity information.
H04L 41/0853 - Retrieval of network configurationTracking network configuration history by actively collecting configuration information or by backing up configuration information
31.
AUTOMATED PASSWORD CHANGES FOR MULTIPLE SYSTEM USERS
A system for changing a user's password across multiple servers accesses a master server within a multiple-server system architecture. The master server has a user database and a connection database. The system accesses a target server via a remote log in from the master server. The target server includes a user database and a connection database. The system changes an existing password within the user database of the target server. The system reads the connection database of the target database to determine which additional servers the target server connects to. The system updates the connection database of the target server with updated connection information. The system can alternatively receive a file upload containing information pertaining to which servers the target server can connect to, and subsequently update the connection database of the target server. This process is repeated for multiple servers and/or user accounts to perform mass password updates.
A method of generating and storing metadata in a data processing system includes defining metadata sets of process definition metadata based on a process definition of the data processing system. The metadata sets include a first set of metadata corresponding to a processing step of the data processing system, a second set of metadata corresponding to a processing step successor, and a third set of metadata corresponding to a data object that is produced or consumed by the processing step in the data processing system. The method further includes executing one or more steps of the data processing system according to the process definition and generating runtime metadata during an execution of the one or more data processing system steps. The method further includes storing the runtime metadata and forming a metadata data store that integrates the runtime metadata and the metadata sets of process definition metadata.
A method of integrating multiple software programs with one another to allow for communication therebetween can include defining resource information regarding integration resources in a cloud platform for use in integrating the multiple software programs with the resource information being defined in a landing zone configuration module, defining an integration process in an integration pattern module with the integration pattern module referencing the resource information in the landing zone configuration module, uploading the landing zone configuration module and the integration pattern module to a container repository having storage media on the cloud platform, generating a first integration module having information specific to the integration of a first software program with the first integration module referencing the landing zone configuration module and the integration pattern module, automatically downloading, by a computer processor, the landing zone configuration module and the integration pattern module to the first integration module.
A method of starting and stopping SAP processes across a plurality of servers via an SAP start/stop utility is presented. A system-scale SAP start/stop script is executed by specifying an action and a type, the action identifying one of START, STOP, and RESTART, the type identifying categories of server to be stopped or started, the categories comprising database servers, message servers, and application servers. In response to the action including STOP or RESTART, the system-scale SAP start/stop script halts, in order and among servers in categories matching the type: application servers, message servers, and database servers. In response to the action including START or RESTART, the system-scale SAP start/stop script then starts, in order and among servers in categories matching the type: database servers, message servers, and application servers.
A method of creating a software program structure for a project is presented. A plurality of project inputs are collected, including a type of software program that is to be created and at least one of: client name, application/project name, database server name, database name, username, password, custom message, configuration key, and cache system key. At least one structure placeholder in the software program structure is replaced with first project information dependent upon the plurality of project inputs, and at least one code placeholder in software code of the software program structure with second project information dependent upon the plurality of project inputs. The software program structure for the project is provided, including first project information and software code that include second project information. The software code is fully runnable software code that does not require further editing.
A method of improving a main output of a main processing application processing first video data includes analyzing incoming video data via a first processing pipeline and analyzing incoming video data via a second processing pipeline. The second processing pipeline includes identifying, by a parameter optimization module, first test preprocessing parameters; preprocessing the incoming video data according to the first test preprocessing parameters, wherein the first test preprocessing includes formatting the incoming video data to create first test video data; processing the first test video data by a test processing application to determine a first test output that is indicative of a first test inference dependent upon the first test video data; and comparing the first test output and the main output to a baseline criterion. In response to the first test output satisfying the baseline criterion, the parameter optimization module can alter the first preprocessing parameters to be similar to the first test preprocessing parameters.
A coating analyzer is configured to receive electronic image data of a physical coating and to generate information regarding the pigments of the physical coating. The coating analyzer applies a computer vision model trained on baseline image data to the electronic image data. The coating analyzer assigns color values to the pigments forming the electronic image data and generates pigment groups based on the assigned color values. The pigment groups provide color palette data regarding the pigments forming the coating.
A method of pre-provisioning a computer hardware device for a client can include receiving an order for the device from the client, identifying whether the client has a pre-provisioning profile, in response to the client having a pre-provisioning profile, extracting device identification information for the device, configuring (by a computer processor) the device identification information into reformatted device identification information suitable for upload to a pre-provisioning center with the pre-provisioning center having the pre-provisioning profile for the client that designates a provisioning configuration for the device and configured to provision the device in response to a start-up prompt by the device, and uploading (by the computer processor), in response to the configurating of the device identification information into reformatted device identification information, the reformatted device identification information to the pre-provisioning center to link the device with the provisioning configuration of the client.
A method of selectively forwarding data transmissions to a wide area network includes receiving, by a network device interposed between a local device and the wide area network, a data transmission from the local device and directed to a destination address accessible through the wide area network, detecting that a connection between the network device and the wide area network has been interrupted, storing the data transmission to a storage device electronically connected to the network device after detecting the connection has been interrupted, detecting that the connection has been restored after storing the data transmission, determining whether to forward the data transmission to the destination address by analyzing the data transmission using a forwarding rules engine, and transmitting the data transmission from the storage device to the destination address in response to determining to forward the data transmission.
A method of selectively forwarding data transmissions to a wide area network includes receiving, from at least one local device and by a network device interposed between the at least one local device and the wide area network, first and second data transmissions intended for first and second destination addresses, respectively, accessible through the wide area network, detecting that a connection between the network device and the wide area network has been interrupted, storing the first data transmission and the second data transmission to a storage device electronically connected to the network device, and detecting that the connection has been restored. The method further includes generating a forwarding order by analyzing the first data transmission and the second data transmission using a forwarding rules engine, the forwarding order describing an order in which to transmit the first data transmission and the second data transmission.
A method includes resolving a plurality of network addresses for a plurality of systems connected to a network, discovering a plurality of application programming interfaces operated by the plurality of systems, inspecting the plurality of application programming interfaces to determine a plurality of invokable elements of the plurality of application programming interfaces, and creating a plurality of connectors executable by a development platform to invoke the plurality of invokable elements. Each network address of the plurality of network addresses corresponds to a system of the plurality of systems, the development platform includes one or more graphically-represented programming functions, and the plurality of connectors is configured to allow the one or more graphically-represented programming functions of the development platform to invoke one or more of the plurality of invokable elements.
A method of predicting the likelihood of a future unsafe incident occurring at a project can include receiving project data and observation data, extracting features from the types of data that are most indicative of the occurrence of the future unsafe incident at the project, and predicting, dependent upon the predictive features that are most indicative of the occurrence of a future unsafe incident, the likelihood of the future unsafe incident occurring at the project. Extraction of the features can include receiving another project's project data, observation data, and incident data, associating the other project's observation data with the incident data by linking a date of the observation data to a date of the unsafe incidents, identifying predictive features that include the types of project data and the types of observation data that are most indicative of an occurrence of the unsafe incident at the other project.
An obfuscation system includes a first database for storing raw data, a quality assurance testing database for storing modified data, one or more processors, and computer-readable memory. The computer-readable memory is encoded with instructions that, when executed by the one or more processors, cause the obfuscation system to: access raw data from the production database, copy the raw data from the production database to the quality assurance testing database, identify sensitive data within the raw data for obfuscation in the quality assurance testing database, obfuscate the sensitive data that has been identified by replacing the sensitive data in each field with a concatenation of a unique descriptor that describes the sensitive data in the field and a primary key, and store the modified data in the quality assurance testing database for access via a user interface.
A method of improving compute performance of a distributed database system includes querying a control node of the distributed database system to ascertain a plurality of attributes characterizing an initial state of a table object in the distributed database system. Queries executed in the distributed database system are recorded. Based on these queries and at least some of the plurality of attributes, an candidate state of the table object with lower compute load than the initial state is generated. An executable transformation mapping the initial state of the table object to the candidate state is then assembled and applied to the table object.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
45.
METADATA-BASED DATA PROCESSING WITH MACHINE LEARNING FRAMEWORK
A method of processing data in a metadata-based data processing system that includes a machine learning framework includes connecting, by a computer device, to a data store that includes client data and accessing, by the computer device, the client data from the data store. The method further includes generating, by the computer device, a machine learning model to use with the client data; deploying, by the computer device, the machine learning model to a production environment as an operational model; and running the operational model in the production environment with the client data during a machine learning processing step carried out by the computer device. The method further includes generating inferences about the client data using the operational model and outputting the inferences via a user interface.
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts a request to perform each of the functional operations in the selected set. Each of a plurality of listening computing entities connected to the network receives the request and determines capability of performing each of the functional operations in the selected set. After determining itself capable, a capable one of the plurality of listening computing entities transmits a response to the request indicating such capability, and then performs each of the functional operations in the selected set, thereby generating and transmitting to the broadcasting computing entity the output of the selected set of functional operations. The broadcasting computing entity then performs an action using the output of the functional operation received.
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts an announcement of the functional operation of which the broadcasting computing entity is capable. Each of a plurality of listening computing entities connected to the network receives the announcement and compares the functional operation with a list of operational needs. After determining itself in need of performance of such a functional operation, a subscribing one of the plurality of listening computing entities transmits a response to the request indicating subscription to the output of the functional operation. The broadcasting computing entity then performs the functional operation, thereby generating and transmitting to the subscribing one of the plurality of listening computing entities the output of the functional operation. The subscribing one of the plurality of listening computing entities then performs an action using the output of the functional operation received.
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts a request to perform each of the functional operations in the selected set. Each of a plurality of listening computing entities connected to the network receives the request and determines capability of performing each of the functional operations in the selected set. After determining itself capable, a capable one of the plurality of listening computing entities transmits a response to the request indicating such capability, and then performs each of the functional operations in the selected set, thereby generating and transmitting to the broadcasting computing entity the output of the selected set of functional operations. The broadcasting computing entity then performs an action using the output of the functional operation received.
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts a request to perform each of the functional operations in the selected set. Each of a plurality of listening computing entities connected to the network receives the request and determines capability of performing each of the functional operations in the selected set. After determining itself capable, a capable one of the plurality of listening computing entities transmits a response to the request indicating such capability, and then performs each of the functional operations in the selected set, thereby generating and transmitting to the broadcasting computing entity the output of the selected set of functional operations. The broadcasting computing entity then performs an action using the output of the functional operation received.
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts an announcement of the functional operation of which the broadcasting computing entity is capable. Each of a plurality of listening computing entities connected to the network receives the announcement and compares the functional operation with a list of operational needs. After determining itself in need of performance of such a functional operation, a subscribing one of the plurality of listening computing entities transmits a response to the request indicating subscription to the output of the functional operation. The broadcasting computing entity then performs the functional operation, thereby generating and transmitting to the subscribing one of the plurality of listening computing entities the output of the functional operation. The subscribing one of the plurality of listening computing entities then performs an action using the output of the functional operation received.
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts an announcement of the functional operation of which the broadcasting computing entity is capable. Each of a plurality of listening computing entities connected to the network receives the announcement and compares the functional operation with a list of operational needs. After determining itself in need of performance of such a functional operation, a subscribing one of the plurality of listening computing entities transmits a response to the request indicating subscription to the output of the functional operation. The broadcasting computing entity then performs the functional operation, thereby generating and transmitting to the subscribing one of the plurality of listening computing entities the output of the functional operation. The subscribing one of the plurality of listening computing entities then performs an action using the output of the functional operation received.
A method of evaluating a proposed message to a conversation for inclusion or exclusion comprising recording messages of the conversation, extracting a relevant subset of the recorded messages, evaluating a thread length of the relevant subset of the recorded messages, and generating a conversation model from the relevant subset in response when thread length is sufficient. Relevance of the proposed message is scored according to the conversation model and compared threshold criteria. If this comparison indicates sufficient correlation between the conversation and the proposed message, the proposed message is submitted to the conversation; otherwise, a user prompt is generated querying whether the proposed message should be submitted to the conversation, and user approval is required before the proposed message is entered into the conversation.
A method of selectively providing data from a caching edge device includes receiving a request from a local device for data stored on a server, the caching edge device configured to receive requests from the local device and to selectively forward the requests to the server and determining whether to deliver to the local device a copy of the data by analyzing the request using a forwarding rules engine, the copy of the data stored to a memory of the caching edge device. The method further includes, after determining to deliver the copy of the data to the local device, electronically transmitting the copy of the data from the memory of the edge caching device to the local device and blocking, by the edge caching device, the request from the local device to the server.
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
H04L 67/1008 - Server selection for load balancing based on parameters of servers, e.g. available memory or workload
H04L 67/288 - Distributed intermediate devices, i.e. intermediate devices for interaction with other intermediate devices on the same level
H04L 67/568 - Storing data temporarily at an intermediate stage, e.g. caching
A method of automatically generating baggage driver staffing recommendations including receiving a first set of flight parameters for a first flight, creating a first predictive staffing model for the first flight by simulating missed bag quantities for a range of driver quantities using a first computer-implemented machine learning model and the first set of flight parameters, and automatically generating a first recommended driver quantity predicted to result in a quantity of missed bags using the first predictive staffing model and a threshold quantity of missed bags. The missed bag quantities are simulated using a simulator, the first computer-implemented machine learning model is configured to relate driver quantities and flight parameters to expected missed bag quantities, and the first predictive staffing model relates predicted quantities of missed bags to quantities of staffed drivers
A clustered metasearch system receives a search query from a user. The system uses Natural Language Processing to identify an object of the search query and descriptors of the search query. The system sorts the search into an applicable realm based on the object of the search query. The system then conducts the search across a variety of search engines and collects root domains from the search results. Root domains within the same realm as the search query are prioritized and additional factors such as the presence of descriptors in the result, the recency of the result, the search engine rank of the result, and the distance from the center of the realm are used to determine the final ranking of the results. The results are then displayed to a user.
A clustered metasearch system receives a search query from a user. The system uses Natural Language Processing to identify an object of the search query and descriptors of the search query. The system sorts the search into an applicable realm based on the object of the search query. The system then conducts the search across a variety of search engines and collects root domains from the search results. Root domains within the same realm as the search query are prioritized and additional factors such as the presence of descriptors in the result, the recency of the result, the search engine rank of the result, and the distance from the center of the realm are used to determine the final ranking of the results. The results are then displayed to a user.
Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
A system for using generative AI to automate electronic communication responses includes receiving, from an initiating entity, an electronic communication at a receiving entity. The system provides the electronic communication to a large language model (LLM). The system provides instructions to the LLM, causing the LLM to evaluate the electronic communication to determine whether a meeting is requested, to produce a meeting indicator, to evaluate the electronic communication to determine if there are one or more tasks, to produce a task list, and to produce a responsive electronic communication based on user-defined rules. The system receives a dataset from the LLM. If a meeting is requested, the system identifies mutually available meeting times between the initiating and receiving entities. The system sends a meeting invitation at a mutually available meeting time. The system sends the responsive electronic communication. The system generates the tasks from the task list.
A product count of a number of physical products within a physical grouping of a plurality of the physical products is determined based on an image of the physical grouping. A cycle counter generates image coordinates for visible product surfaces within the image. The cycle counter generates three-dimensional virtual base coordinates for the expected locations of the plurality of the physical products within the physical grouping. The cycle counter determines the actual locations of the visible product surfaces within three-dimensional space based on a comparison of the image coordinates and the virtual base coordinates. The cycle counter determines the product count of the number of physical products within the physical grouping based on the actual locations.
A method of creating a dimension table or a fact table for a data warehouse includes accessing an ordered sequence of activities that are arranged in a template. A first set of code associated with creating the dimension table is organized into a dimension processing class, a second set of code associated with creating the fact table is organized into a fact processing class, and a third set of code associated with creating both the dimension table and the fact table is organized into a common processing class. The method further includes executing the ordered sequence of activities and creating the dimension table when an instance of the dimension processing class is created in the ordered sequence of activities and creating the fact table when an instance of the fact processing class is created in the ordered sequence of activities.
A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06T 5/90 - Dynamic range modification of images or parts thereof
G06V 10/36 - Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given pointNon-linear local filtering operations, e.g. median filtering
G06V 20/40 - ScenesScene-specific elements in video content
H04N 23/661 - Transmitting camera control signals through networks, e.g. control via the Internet
H04N 23/80 - Camera processing pipelinesComponents thereof
A method of automatically generating baggage driver staffing recommendations including receiving a first set of flight parameters for a first flight, creating a first predictive staffing model for the first flight by simulating missed bag quantities for a range of driver quantities using a first computer-implemented machine learning model and the first set of flight parameters, and automatically generating a first recommended driver quantity predicted to result in a quantity of missed bags using the first predictive staffing model and a threshold quantity of missed bags. The missed bag quantities are simulated using a simulator, the first computer-implemented machine learning model is configured to relate driver quantities and flight parameters to expected missed bag quantities, and the first predictive staffing model relates predicted quantities of missed bags to quantities of staffed drivers.
A clustered metasearch system receives a search query from a user. The system uses Natural Language Processing to identify an object of the search query and descriptors of the search query. The system sorts the search into an applicable realm based on the object of the search query. The system then conducts the search across a variety of search engines and collects root domains from the search results. Root domains within the same realm as the search query are prioritized and additional factors such as the presence of descriptors in the result, the recency of the result, the search engine rank of the result, and the distance from the center of the realm are used to determine the final ranking of the results. The results are then displayed to a user.
A method is presented for executing an application having multiple mutually-exclusive possible dependencies associated with an interface. First, a repository of implementations is scanned using a configuration utility to identify a subset of the implementations tagged for usability with the configuration utility. The configuration utility identifies keys within each of the subset of implementations, and filters the implementations by these configuration keys. The configuration utility scans configurations for a match with any of the identified configuration keys, and selects one implementation from among the subset of the implementations for dependency injection based on matching of configurations with the identified configuration keys. The configuration utility then injects a dependency using the selected implementation, and initializes configurations specific to the injected dependency.
A method of performing rule evaluations, with the performance of the rule evaluations by at least one computer processor and storage of the rule evaluations by digital storage media, includes receiving a first input from a first sensor of a plurality of sensors; determining, by the at least one computer processor, which hierarchical nodes from a plurality of hierarchical nodes include a first rule that is dependent upon the first input; retrieving, from the digital storage media, the hierarchical nodes that include the first rule that is dependent upon the first input; and performing an evaluation of the first rule to determine a first output.
A method of detecting inappropriate imagery in a customer user profile image intending to be displayed on digital signage at a quick service restaurant can include identifying the presence of a customer adjacent a digital sign; associating the customer with a customer user profile; analyzing, by a computer processor, the customer user profile image for inappropriate imagery in the customer user profile image; in response to the detection of inappropriate imagery, displaying on the digital sign either no image or a different image that does not contain inappropriate imagery; and, in response to the detection of inappropriate imagery, displaying on the digital sign the customer user profile image.
G09F 9/30 - Indicating arrangements for variable information in which the information is built-up on a support by selection or combination of individual elements in which the desired character or characters are formed by combining individual elements
67.
IDENTIFYING VEHICLES AND ASSOCIATING VEHICLES WITH CUSTOMER PROFILES AT A QUICK SERVICE RESTAURANT
A method of delivering an order to a customer of a quick service restaurant can include identifying, by computer vision using at least one camera, a vehicle that enters a zone surrounding the quick service restaurant; associating, by a computer processor, the vehicle with a profile of the customer that includes the order; directing the vehicle to a location within the zone; and delivering the order to the customer in the vehicle.
A method of delivering an order to a customer of a quick service restaurant can include receiving, from the customer, an order that includes at least one food time in need of preparation; determining, by a computer processor, an amount of time needed to prepare the at least one food item; identifying a vehicle of the customer that enters a zone surrounding the quick service restaurant; depending on an amount of time needed to complete preparation of the at least one food item at the moment when the vehicle is identified within the zone, directing the customer to either at least one drive-through lane or a waiting spot; and delivering the order to the customer either in the at least one drive-through lane or at the waiting spot.
A system for collecting demographic information and associating the demographic information with a purchase by a consumer includes an RF tag having identification information associated with the purchase by the consumer and configured to move in unison with the consumer, a first RF antenna configured to record a location of the RF tag via reception of the identification information from the RF tag, a camera configured to collected video data including the consumer, and a computer processor configured to receive location information and the identification information of the RF tag from the first RF antenna, receive information regarding the purchase and associate the RF tag with the information regarding the purchase, and receive video data from the camera. The computer processor can be configured to determine demographic information of the consumer from the video data and associate the demographic information with the information regarding the purchase.
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
G06K 19/077 - Constructional details, e.g. mounting of circuits in the carrier
G06V 20/40 - ScenesScene-specific elements in video content
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
A method of automatically tailoring sub-accounts of an online user account includes accessing, via a computer-based user data agent, the online user account that includes the sub-accounts and identifying traits associated with each of the sub-accounts. The traits include a first set of traits associated with a first sub-account and a second set of traits associated with a second sub-account. The method further includes generating a first alternate persona based on the first set of traits and generating a second alternate persona based on the second set of traits. The method further includes automatically performing online activities based on the first alternate persona for the first sub-account and based on the second alternate persona for the second sub-account to produce artificial user data that tailors the sub-accounts according to user preferences.
Apparatus and associated methods relate to assessing a confidence level of a verbal communication of a person as determined by a machine learning model operating on a video stream of the person. Video data, audio data, and semantic text data are extracted from a video stream of the person. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a confidence level of the verbal communication of the person is assessed. The confidence level is then associated with a time of the video stream to which the confidence level pertains. The confidence level and the associated time of the video stream are then reported.
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 20/40 - ScenesScene-specific elements in video content
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
G10L 15/06 - Creation of reference templatesTraining of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
G10L 15/18 - Speech classification or search using natural language modelling
G10L 25/57 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for processing of video signals
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
72.
MACHINE LEARNING METHOD FOR PREDICTING A HEALTH OUTCOME OF A PATIENT USING VIDEO AND AUDIO ANALYTICS
Apparatus and associated methods relate to predicting a health outcome of a patient by a machine learning model operating on a video stream of the patient. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is then reported.
G10L 25/66 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
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 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
73.
MACHINE LEARNING METHOD FOR ENHANCING CARE OF A PATIENT USING VIDEO AND AUDIO ANALYTICS
Apparatus and associated methods relate to enhancing care of a patient using video and audio analytics. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is compared with the set of health outcomes of the training patients classified with the patient classification of the patient. Differences are identified between the feature sets corresponding to the patient and feature sets of the training patients who have better health outcomes the patient's predicted health outcome. The differences identified are then reported.
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
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 20/40 - ScenesScene-specific elements in video content
G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
G10L 15/06 - Creation of reference templatesTraining of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
G10L 15/18 - Speech classification or search using natural language modelling
G10L 25/66 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
74.
AUTOMATED COLLECTION OF PRODUCT IMAGE DATA AND ANNOTATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING
A method of obtaining images and data to train an AI model for product detection includes generating, with an image collection system at a first product source, a first annotation package including one or more images of and data about a first product. The first product is placed in a first enclosure located at the first product source. A process for obtaining the one or more images of the first product is initiated in the first enclosure. The one or more images of the first product is obtained with one or more cameras positioned in the first enclosure. The one or more images of the first product is provided to an edge compute device. Data about the first product is input, using an input device, into the edge compute device. An annotation package for the first product is created that includes the one or more images and the data.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
A method is presented for executing an application having multiple mutually-exclusive possible dependencies associated with an interface. First, a repository of implementations is scanned using a configuration utility to identify a subset of the implementations tagged for usability with the configuration utility. The configuration utility identifies keys within each of the subset of implementations, and filters the implementations by these configuration keys. The configuration utility scans configurations for a match with any of the identified configuration keys, and selects one implementation from among the subset of the implementations for dependency injection based on matching of configurations with the identified configuration keys. The configuration utility then injects a dependency using the selected implementation, and initializes configurations specific to the injected dependency.
A method of detecting image defects with a metadata-based data processing system includes accessing and copying source images in a first processing step and storing copied images from the first processing step in a first data store. The method further includes processing the copied images in a second processing step and storing processed images from the second processing step in a second data store. The method further includes deploying a defect detection machine learning model, retrieving the processed images from the second data store, using the processed images as an input to the defect detection machine learning model in a third processing step, and detecting one or more defects in the processed images via the defect detection machine learning model. The method further includes outputting an indication of the one or more defects in the processed images.
A method of product detection includes receiving, at a product detector from a product source, a first annotation package for a first product and a second annotation package for a second product. An artificial intelligence model is trained to detect the first product based on the first annotation package and the second product based on the second annotation package. The artificial intelligence model is implemented on the product detector. At the product detector, the first product is categorized into a first category of products and the second product is categorized into a second category of products. A subscription is received from a retailer to one of: the first category of products; the second category of products; and the first category and the second category of products. An image is received at the product detector from the retailer. The first product is detected in the image by the artificial intelligence model.
G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
G06Q 30/06 - Buying, selling or leasing transactions
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
A method of preparing an order placed by a customer distant from a quick service restaurant includes receiving, from the customer, an order that includes at least one food item in need of preparation; determining, by a computer processor, an amount of time needed to prepare the at least one food item; positioning, dependent upon the amount of time needed to prepare the at least one food item, a geofence around the quick service restaurant; tracking a position of the customer; and, in response to the customer crossing the geofence, beginning preparation of the at least one food item.
The present disclosure presents a method of predicting an amount of time needed to complete preparation of an order at a quick service restaurant. The order includes at least one food item in need of preparation. The method can include receiving the order from a customer, and predicting the amount of time needed to complete preparation of the order using a machine-learning model, based on the food item(s) ordered and on at least one of the following: a time-of-day that the order was placed, a number of employees on duty at the quick service restaurant, a number of other orders currently pending at the quick service restaurant, and inventory of each ingredient needed to complete preparation of the at least one food item.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
A method of delivering an order to a customer of a quick service restaurant can include transmitting, from a first beacon to a receiver adjacent the customer, a first signal; transmitting, from a second beacon to the receiver adjacent the customer, a second signal; from the first signal received by the receiver, a first timestamp reflective of a time the first signal is received by the receiver, the second signal received by the receiver, and a second timestamp reflective of a time the second signal is received by the receiver, determining a location of the customer within a zone surrounding the quick service restaurant; communicating the location of the customer to a display within the quick service restaurant; and delivering the order to the customer at the location.
G01S 1/04 - Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmittersReceivers co-operating therewith using radio waves Details
A method of delivering an order to a customer of a quick service restaurant can include identifying a vehicle of the customer that enters a zone surrounding the quick service restaurant and displaying, on at least one digital sign able to be viewed by the customer, information specific to the customer.
A method of administering a survey includes collecting response data from an introductory question set selected from the questions of the survey. The method calculates disposition probabilities based on the response data and a probability difference between two of the disposition probabilities. The method ends the survey based on a comparison between the probability difference and a probability difference threshold criterion.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
A computing device and method for preparing application data based on a migration recommendation includes collecting survey responses to categorized questions and assigning a score to each of the survey response. The scored survey responses are normalized and rationalization parameters are determined based on the normalized scores. Application data are prepared for migration based on a recommended disposition determined from the rationalization parameters.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
A system for identifying a group of consumers and associating a first purchase with the group that includes a purchase terminal at which the first purchase is made by a first consumer in the group of consumers; an RF tag having identification information and configured to move in unison with the first consumer; a first RF antenna configured to record a location of the RF tag via reception of the identification information from the RF tag; at least one camera configured to collected video data of the group of consumers within an entrance/exit zone; and a computer processor in communication with the purchase terminal, the RF antenna, and the at least one camera. The computer processor can be configured to identify a number of consumers within the group from the video data, collect the location and the identification information of the first RF tag from the RF antenna, receive information regarding the first purchase, associate the RF tag with the information regarding the first purchase, and associate the information regarding the first purchase by the first consumer with the group of consumers.
G06K 7/10 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
G06V 20/40 - ScenesScene-specific elements in video content
A method of protecting user privacy online includes collecting, via a computer-based user data agent, user data generated by a user on a user device and identifying a first set of traits associated with the user data. The method further includes generating a user profile based on the first set of traits associated with the user data and generating an alternate persona defined by a second set of traits that is different from the first set of traits. The method further includes automatically performing online activities based on the alternate persona to produce artificial user data that obscures real user data.
Apparatus and associated methods relate to invoking an alert based upon a behavior of a patient as determined by a machine-learning model operating on a video stream of the patient. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify first, second, and third features sets of video, audio, and semantic text features, respectively, which have been identified by a computer-implemented machine-learning engine as being indicative of at least one of a set of alerting behaviors corresponding to a patient classification of the patient. Using a computer-implemented machine-learning model, a patient behavior of the patient is determined based on the first, second, and/or third features sets. The patient's behavior is compared with the set of alerting behaviors, and, when the patient's behavior is determined to be included therein, the alert is automatically invoked.
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
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/40 - ScenesScene-specific elements in video content
87.
MACHINE LEARNING METHOD FOR DETERMINING PATIENT BEHAVIOR USING AUDIO ANALYTICS
Apparatus and associated methods relate to invoking an alert based upon a behavior of a patient as determined by a machine learning model operating on an audio stream of the patient. Audio data, and semantic text data are extracted from an audio stream of the patient. The audio data are analyzed to identify a first feature set. The semantic text data are analyzed to identify a second feature set. Using a computer-implemented machine-learning model, a patient behavior of the patient is determined based on the first and/or second features sets. The patient behavior is compared with a set of alerting behaviors corresponding to a patient classification of the patient. The alert is automatically invoked when the patient behavior is determined to be included in the set of alerting behaviors corresponding to the patient classification of the patient.
A61B 5/16 - Devices for psychotechnicsTesting reaction times
G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
G10L 15/18 - Speech classification or search using natural language modelling
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
88.
SYSTEMS AND METHODS FOR PERSONALIZED OFFERING RECOMMENDATIONS
A method of creating custom video sequences includes presenting an ecommerce website to a customer and automatically collecting electronic customer data from the ecommerce website by a server system. The ecommerce website is configured to display a generic set of offerings. The electronic customer data describes at least one customer interaction with the ecommerce website is automatically collected concurrently with the customer interaction(s). The method further includes, concurrently with automatically collecting the electronic customer data, creating a customer profile of a customer based on the electronic customer data, determining a set of offering descriptors associated with at least one customer descriptor using a descriptor taxonomy, and generating a custom set of offerings by querying an electronic database with the set of offering descriptors. The method further comprises automatically updating the ecommerce website to display the custom set of offerings instead of the generic set of offerings.
A method of creating custom video sequences includes presenting an ecommerce website to a customer and automatically collecting electronic customer data from the ecommerce website by a server system. The electronic customer data describes at least one customer interaction with the ecommerce website is automatically collected concurrently with customer interaction(s). The method further includes, concurrently with automatically collecting the electronic customer data, creating a customer profile of a customer based on the electronic customer data, determining a set of offering descriptors based on at least one customer descriptor, automatically selecting a subset of video segments from a plurality of video segments based on the set of offering descriptors, and automatically sequencing the subset of video segments into a custom video. The method further includes automatically modifying a web page of the ecommerce website by inserting the custom video into the web page.
A method of creating custom video sequences includes presenting an ecommerce website to a customer and automatically collecting electronic customer data from the ecommerce website by a server system. The ecommerce website is configured to display a generic set of offerings. The electronic customer data describes at least one customer interaction with the ecommerce website is automatically collected concurrently with the customer interaction(s). The method further includes, concurrently with automatically collecting the electronic customer data, creating a customer profile of a customer based on the electronic customer data, determining a set of offering descriptors associated with at least one customer descriptor using a descriptor taxonomy, and generating a custom set of offerings by querying an electronic database with the set of offering descriptors. The method further comprises automatically updating the ecommerce website to display the custom set of offerings instead of the generic set of offerings.
A method of creating custom video sequences includes presenting an ecommerce website to a customer and automatically collecting electronic customer data from the ecommerce website by a server system. The electronic customer data describes at least one customer interaction with the ecommerce website is automatically collected concurrently with customer interaction(s). The method further includes, concurrently with automatically collecting the electronic customer data, creating a customer profile of a customer based on the electronic customer data, determining a set of offering descriptors based on at least one customer descriptor, automatically selecting a subset of video segments from a plurality of video segments based on the set of offering descriptors, and automatically sequencing the subset of video segments into a custom video. The method further includes automatically modifying a web page of the ecommerce website by inserting the custom video into the web page.
A process of determining a sentiment score from a response from at least one individual regarding a status of a project that includes receiving a first numerical rating regarding the status of the project from a first individual of the at least one individual, receiving a first textual description regarding the heath of the project from the first individual, determining a first textual score dependent upon the first textual description with the first textual score being a numerical representation of the first textual description, and aggregating the first numerical rating and the first textual score to determine a first sentiment score reflective of the status of the project by the first individual.
An asset tracking system includes a tracking device associated with a mobile asset and a centralized device management platform. The tracking device is configured via a set of device configurations. The centralized device management platform includes one or more processors in communication with the tracking device and computer-readable memory encoded with instructions that, when executed, cause the system to receive location data corresponding to a location of the tracking device; evaluate the location data to identify a zone corresponding to a physical region that includes the location of the tracking device; compare an identified zone with a set of location-based rules to identify a rule associated with the identified zone; update the set of device configurations with a device configuration that is associated with an identified rule; and sync an updated set of device configurations to the tracking device to reconfigure the tracking device based on the identified rule.
Apparatus and associated methods relate to automating building of a software solution that is cooperatively performed. A broadcasting computing entity selects a functional operation from a set of one or more functional operations associated with the software solution and then broadcasts a request to perform the functional operation selected. Each of a plurality of listening computing entities connected to the network receives the request to perform the functional operation and determines capability of performing the functional operation. A capable one of the plurality of listening computing entities that determines itself capable of performing the functional operation transmits a response to the request received indicating capability of performing the functional operation and performs the functional operation, thereby generating the output of the functional operation, which is transmitted to the broadcasting computing entity. The broadcasting computing entity then performs an action using the output of the functional operation received.
An asset tracking system includes a tracking device associated with a mobile asset and a centralized device management platform. The tracking device is configured via a set of device configurations. The centralized device management platform includes one or more processors in communication with the tracking device and computer-readable memory encoded with instructions that, when executed, cause the system to receive location data corresponding to a location of the tracking device; evaluate the location data to identify a zone corresponding to a physical region that includes the location of the tracking device; compare an identified zone with a set of location-based rules to identify a rule associated with the identified zone; update the set of device configurations with a device configuration that is associated with an identified rule; and sync an updated set of device configurations to the tracking device to reconfigure the tracking device based on the identified rule.
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity selects a selected set of functional operations from one or more sets of functional operations associated with the software solution and then broadcasts a request to perform each of the functional operations in the selected set. Each of a plurality of listening computing entities connected to the network receives the request and determines capability of performing each of the functional operations in the selected set. After determining itself capable, a capable one of the plurality of listening computing entities transmits a response to the request indicating such capability, and then performs each of the functional operations in the selected set, thereby generating and transmitting to the broadcasting computing entity the output of the selected set of functional operations. The broadcasting computing entity then performs an action using the output of the functional operation received.
A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.
G06T 5/90 - Dynamic range modification of images or parts thereof
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/36 - Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given pointNon-linear local filtering operations, e.g. median filtering
G06V 20/40 - ScenesScene-specific elements in video content
H04N 23/661 - Transmitting camera control signals through networks, e.g. control via the Internet
H04N 23/80 - Camera processing pipelinesComponents thereof
98.
Dynamically configured extraction, preprocessing, and publishing of a region of interest that is a subset of streaming video data
A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.
G06T 5/90 - Dynamic range modification of images or parts thereof
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/36 - Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given pointNon-linear local filtering operations, e.g. median filtering
G06V 20/40 - ScenesScene-specific elements in video content
H04N 23/661 - Transmitting camera control signals through networks, e.g. control via the Internet
H04N 23/80 - Camera processing pipelinesComponents thereof
99.
SCHEDULED SCENE MODIFICATION FOR EXTRACTION, PREPROCESSING, AND PUBLISHING OF STREAMING VIDEO DATA
A method of scheduled modifications of preprocessing of incoming video data of at least one region of interest from a camera collecting video data having a first field of view includes receiving incoming video data from the camera and preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file. The preprocessing includes formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view. The method also include publishing the first video data of the first region of interest to an endpoint to allow access and processing by a subscriber; in response to a time schedule, altering the preprocessing parameters defined within the runtime configuration file dependent upon the time schedule to create second video data that is different from the first video data; and publishing the second video data to the endpoint to allow access and processing by the first subscriber.
H04N 19/117 - Filters, e.g. for pre-processing or post-processing
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 21/43 - Processing of content or additional data, e.g. demultiplexing additional data from a digital video streamElementary client operations, e.g. monitoring of home network or synchronizing decoder's clockClient middleware
100.
Method and system for preprocessing optimization of streaming video data
A method of improving a main output of a main processing application processing first video data includes analyzing incoming video data via a first processing pipeline and analyzing incoming video data via a second processing pipeline. The second processing pipeline includes identifying, by a parameter optimization module, first test preprocessing parameters; preprocessing the incoming video data according to the first test preprocessing parameters, wherein the first test preprocessing includes formatting the incoming video data to create first test video data; processing the first test video data by a test processing application to determine a first test output that is indicative of a first test inference dependent upon the first test video data; and comparing the first test output and the main output to a baseline criterion. In response to the first test output satisfying the baseline criterion, the parameter optimization module can alter the first preprocessing parameters to be similar to the first test preprocessing parameters.
