An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model having a plurality of run-time bi-directional nodes and a plurality of run-time connection links. Each run-time bi-directional node includes a node template and a node instance. The processing circuitry receives a stimulus and identifies a first run-time bi-directional node based on the stimulus. The processing circuitry determines a first run-time connection link based on the identification of the first run-time bi-directional node. The first run-time bi-directional node is coupled to a second run-time bi-directional node via the first run-time connection link. The processing circuitry further identifies the second run-time bi-directional node and executes the operation associated with the stimulus based on the first run-time bi-directional node, the second run-time bi-directional node, and the first run-time connection link.
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model having various active nodes. The processing circuitry may receive a stimulus associated with the overlay system and identify, based on the stimulus, an active node. Further, the processing circuitry may generate a proxy node for the active node. The proxy node includes a reference to the active node. The processing circuitry may implement the proxy node in the executable graph-based model by way of an actor interface. Further, the processing circuitry may store the first proxy node in a primary storage of the storage element. Additionally, as a response to the first stimulus, the processing circuitry may unload the first active node from the executable graph-based model.
The present disclosure facilitates optimized storage of historical data. A graph is generated using various messages. For each message, a message node is instantiated in the graph. In a message-driven architecture, processing of a message leads to the generation of a data node in the graph. The data node represents an output associated with the message processing. In response to the data node generation, another message node is instantiated in the graph. The other message node represents a message that is indicative of the generation of the data node and comprises data and transactional information associated with the data node. Further, a history node is created linking the data node and the two message nodes to indicate that the data node is an output associated with the message processing and the newly generated message node stores a change associated with the data node.
This disclosure relates to method and system for facilitating legacy code transformation. The method includes receiving legacy code data and natural language document from one or more data sources. Each of the one or more data sources is one of an external data source or an internal data source. Further, the method includes generating a first natural language output based on the legacy code data through a first LLM, and a second natural language output based on the natural language document through a second LLM. Further, the method includes fine-tuning one of the first LLM or the second LLM based on the first natural language output and the second natural language output, through a third LLM. Further, the method includes generating a natural language specification document corresponding to the legacy code data based on the first natural language output and the second natural language output through the third LLM.
A system for message management using directed property graphs is provided. A directed property graph is generated using various messages. To generate the graph, for each message, a message node representing the message is instantiated. Each message has various attributes. Some message attributes are associated as node properties of the message node. Further, one or more shared attributes are derived for each message and corresponding attribute nodes are instantiated. One or more attributes are associated as node properties of these attribute nodes. Further, each message node is coupled to a corresponding attribute node by way of an edge. Each edge has edge attributes associated as edge properties. The edge attributes may be derived from node properties of the message node and the corresponding attribute node and may indicate an association therebetween. The generated graph facilitates query response generation.
A system for time-series message management using directed property graphs is provided. A directed property graph is generated using various messages. The directed property graph comprises various message nodes and attribute nodes, with each message node representing a message, having some attributes of the message associated as node properties thereof, and being associated with attribute nodes that represent shared attributes of the message. To execute a query indicative of time-series computation functions, one or more attribute nodes are identified in the directed property graph. Based on the identified attribute nodes, a set of message nodes is determined. Further, from node properties of each determined message node, one or more message timestamps are identified. Based on the identified message timestamps, the time-series computation functions are executed.
The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.
A system for improving performance of variational quantum classifiers (VQCs) using large language models (LLMs) is provided. Features and classes are extracted from a classification dataset. Weights are assigned to the features based on contribution of the features in class identification. Further, segregation parameters are determined for the features such that a segregation parameter indicates a degree of separation between two classes based on a correlation between two features. A prompt is generated based on the weights, the segregation parameters, current ansatz and feature map of a VQC, a performance metric of the VQC for the current ansatz and feature map, details of hardware executing the VQC, and a number of qubits of the VQC. Using an LLM, a response to the prompt is generated. Further, using the response, the ansatz and the feature map are updated. The update continues until the performance metric is within a desired range.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable computer software for use in data processing, analytics, automation, artificial intelligence, machine learning, and digital transformation; downloadable computer software for enterprise resource planning (ERP), customer relationship management (CRM), supply chain management, and business process management; downloadable computer software for managing, analyzing, visualizing, and reporting business and enterprise data; downloadable software platforms for developing, deploying, and managing cloud-based applications; downloadable application programming interfaces (APIs) and software development kits (SDKs); downloadable computer software for cybersecurity, authentication, data protection, and regulatory compliance; downloadable computer software for workflow automation, robotic process automation (RPA), and orchestration of business processes; downloadable software for use in database management, data warehousing, and big data processing; downloadable computer software for integration of computer systems, applications, and networks; downloadable software for use in Internet of Things (IoT) device management and data collection; downloadable software for use in financial services, healthcare, retail, telecommunications, manufacturing, and other enterprise industries; recorded and downloadable computer programs for use in application development, testing, maintenance, and monitoring; downloadable educational software and training materials in the field of information technology, artificial intelligence, and digital transformation; downloadable digital content featuring a fictional character or mascot for branding, promotional, training, and educational purposes; downloadable multimedia files, animations, graphics, digital images, and audiovisual recordings featuring a mascot or animated character; downloadable interactive digital media, namely, avatars, virtual assistants, and digital agents featuring a fictional character or mascot; downloadable software applications featuring a virtual mascot character that provides AI guidance, tutorials, and interactive assistance to users of information technology services.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Information technology consulting services; consulting services in the fields of digital transformation, artificial intelligence, machine learning, data analytics, and cloud computing; design, development, implementation, and maintenance of computer software for others; computer programming services; integration of computer systems and networks; software engineering services; providing temporary use of non-downloadable computer software and software platforms for use in data processing, analytics, artificial intelligence, automation, and enterprise business operations (SaaS); providing temporary use of non-downloadable cloud-based software platforms for application development, deployment, and hosting (PaaS); cloud computing services; infrastructure as a service (IaaS); platform as a service (PaaS); software as a service (SaaS) featuring software for enterprise resource planning, customer relationship management, supply chain management, financial management, and business process automation; technical support services, namely, troubleshooting and help desk services in the field of computer software and information technology; software as a service (SaaS) featuring an virtual mascot guide for use in information technology support, troubleshooting, and user onboarding; computer consulting and information technology support services; application service provider (ASP) services featuring software for managing business processes and enterprise data; data hosting services and electronic storage of data; database design and development services; Computer services, namely, providing online non-downloadable software tools for data visualization and reporting; providing artificial intelligence as a service (AIaaS); design and development of computer software for use in IoT, cybersecurity, and digital platforms; consulting in the field of IT architecture and digital infrastructure; providing online non-downloadable educational software and training platforms in the field of information technology and artificial intelligence; design and development of digital and multimedia content featuring a fictional character or mascot; creating and maintaining websites and digital platforms for others featuring branded content, including virtual assistants, chatbots, and animated characters; providing temporary use of non-downloadable virtual assistants and AI-powered digital agents featuring a fictional character or mascot for customer interaction, training, and enterprise support services.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Information technology consulting services; consulting services in the fields of digital transformation, artificial intelligence, machine learning, data analytics, and cloud computing; design, development, implementation, and maintenance of computer software for others; computer programming services; integration of computer systems and networks; software engineering services; providing temporary use of non-downloadable computer software and software platforms for use in data processing, analytics, artificial intelligence, automation, and enterprise business operations (SaaS); providing temporary use of non-downloadable cloud-based software platforms for application development, deployment, and hosting (PaaS); cloud computing services; infrastructure as a service (IaaS); platform as a service (PaaS); software as a service (SaaS) featuring software for enterprise resource planning, customer relationship management, supply chain management, financial management, and business process automation; technical support services, namely, troubleshooting and help desk services in the field of computer software and information technology; software as a service (SaaS) featuring an virtual mascot guide for use in information technology support, troubleshooting, and user onboarding; computer consulting and information technology support services; application service provider (ASP) services featuring software for managing business processes and enterprise data; data hosting services and electronic storage of data; database design and development services; Computer services, namely, providing online non-downloadable software tools for data visualization and reporting; providing artificial intelligence as a service (AIaaS); design and development of computer software for use in IoT, cybersecurity, and digital platforms; consulting in the field of IT architecture and digital infrastructure; providing online non-downloadable educational software and training platforms in the field of information technology and artificial intelligence; design and development of digital and multimedia content featuring a fictional character or mascot; creating and maintaining websites and digital platforms for others featuring branded content, including virtual assistants, chatbots, and animated characters; providing temporary use of non-downloadable virtual assistants and AI-powered digital agents featuring a fictional character or mascot for customer interaction, training, and enterprise support services.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable computer software for use in data processing, analytics, automation, artificial intelligence, machine learning, and digital transformation; downloadable computer software for enterprise resource planning (ERP), customer relationship management (CRM), supply chain management, and business process management; downloadable computer software for managing, analyzing, visualizing, and reporting business and enterprise data; downloadable software platforms for developing, deploying, and managing cloud-based applications; downloadable application programming interfaces (APIs) and software development kits (SDKs); downloadable computer software for cybersecurity, authentication, data protection, and regulatory compliance; downloadable computer software for workflow automation, robotic process automation (RPA), and orchestration of business processes; downloadable software for use in database management, data warehousing, and big data processing; downloadable computer software for integration of computer systems, applications, and networks; downloadable software for use in Internet of Things (IoT) device management and data collection; downloadable software for use in financial services, healthcare, retail, telecommunications, manufacturing, and other enterprise industries; recorded and downloadable computer programs for use in application development, testing, maintenance, and monitoring; downloadable educational software and training materials in the field of information technology, artificial intelligence, and digital transformation; downloadable digital content featuring a fictional character or mascot for branding, promotional, training, and educational purposes; downloadable multimedia files, animations, graphics, digital images, and audiovisual recordings featuring a mascot or animated character; downloadable interactive digital media, namely, avatars, virtual assistants, and digital agents featuring a fictional character or mascot; downloadable software applications featuring a virtual mascot character that provides AI guidance, tutorials, and interactive assistance to users of information technology services.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable computer software for use in data processing, analytics, automation, artificial intelligence, machine learning, and digital transformation; downloadable computer software for enterprise resource planning (ERP), customer relationship management (CRM), supply chain management, and business process management; downloadable computer software for managing, analyzing, visualizing, and reporting business and enterprise data; downloadable software platforms for developing, deploying, and managing cloud-based applications; downloadable application programming interfaces (APIs) and software development kits (SDKs); downloadable computer software for cybersecurity, authentication, data protection, and regulatory compliance; downloadable computer software for workflow automation, robotic process automation (RPA), and orchestration of business processes; downloadable software for use in database management, data warehousing, and big data processing; downloadable computer software for integration of computer systems, applications, and networks; downloadable software for use in Internet of Things (IoT) device management and data collection; downloadable software for use in financial services, healthcare, retail, telecommunications, manufacturing, and other enterprise industries; recorded and downloadable computer programs for use in application development, testing, maintenance, and monitoring; downloadable educational software and training materials in the field of information technology; Artificial intelligence, and digital transformation; downloadable digital content featuring a fictional character or mascot for branding, promotional, training, and educational purposes; downloadable multimedia files, animations, graphics, digital images, and audiovisual recordings featuring a mascot or animated character; downloadable interactive digital media, namely, avatars, virtual assistants, and digital agents featuring a fictional character or mascot. downloadable software applications featuring a virtual mascot character that provides AI guidance, tutorials, and interactive assistance to users of information technology services
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Information technology consulting services; consulting services in the fields of digital transformation, artificial intelligence, machine learning, data analytics, and cloud computing; design, development, implementation, and maintenance of computer software for others; computer programming services; integration of computer systems and networks; software engineering services; providing temporary use of non-downloadable computer software and software platforms for use in data processing, analytics, artificial intelligence, automation, and enterprise business operations (SaaS); providing temporary use of non-downloadable cloud-based software platforms for application development, deployment, and hosting (PaaS); cloud computing services; infrastructure as a service (IaaS); platform as a service (PaaS); software as a service (SaaS) featuring software for enterprise resource planning, customer relationship management, supply chain management, financial management, and business process automation; technical support services, namely, troubleshooting and help desk services in the field of computer software and information technology; software as a service (SaaS) featuring an virtual mascot guide for use in information technology support, troubleshooting, and user onboarding; computer consulting and information technology support services; application service provider (ASP) services featuring software for managing business processes and enterprise data; data hosting services and electronic storage of data; database design and development services; Computer services, namely, providing online non-downloadable software tools for data visualization and reporting; providing artificial intelligence as a service (AIaaS); design and development of computer software for use in IoT, cybersecurity, and digital platforms; consulting in the field of IT architecture and digital infrastructure; providing online non-downloadable educational software and training platforms in the field of information technology and artificial intelligence; design and development of digital and multimedia content featuring a fictional character or mascot; creating and maintaining websites and digital platforms for others featuring branded content, including virtual assistants, chatbots, and animated characters; providing temporary use of non-downloadable virtual assistants and AI-powered digital agents featuring a fictional character or mascot for customer interaction, training, and enterprise support services
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable computer software for use in data processing, analytics, automation, artificial intelligence, machine learning, and digital transformation; downloadable computer software for enterprise resource planning (ERP), customer relationship management (CRM), supply chain management, and business process management; downloadable computer software for managing, analyzing, visualizing, and reporting business and enterprise data; downloadable software platforms for developing, deploying, and managing cloud-based applications; downloadable application programming interfaces (APIs) and software development kits (SDKs); downloadable computer software for cybersecurity, authentication, data protection, and regulatory compliance; downloadable computer software for workflow automation, robotic process automation (RPA), and orchestration of business processes; downloadable software for use in database management, data warehousing, and big data processing; downloadable computer software for integration of computer systems, applications, and networks; downloadable software for use in Internet of Things (IoT) device management and data collection; downloadable software for use in financial services, healthcare, retail, telecommunications, manufacturing, and other enterprise industries; recorded and downloadable computer programs for use in application development, testing, maintenance, and monitoring; downloadable educational software and training materials in the field of information technology; Artificial intelligence, and digital transformation; downloadable digital content featuring a fictional character or mascot for branding, promotional, training, and educational purposes; downloadable multimedia files, animations, graphics, digital images, and audiovisual recordings featuring a mascot or animated character; downloadable interactive digital media, namely, avatars, virtual assistants, and digital agents featuring a fictional character or mascot. downloadable software applications featuring a virtual mascot character that provides AI guidance, tutorials, and interactive assistance to users of information technology services
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Information technology consulting services; consulting services in the fields of digital transformation, artificial intelligence, machine learning, data analytics, and cloud computing; design, development, implementation, and maintenance of computer software for others; computer programming services; integration of computer systems and networks; software engineering services; providing temporary use of non-downloadable computer software and software platforms for use in data processing, analytics, artificial intelligence, automation, and enterprise business operations (SaaS); providing temporary use of non-downloadable cloud-based software platforms for application development, deployment, and hosting (PaaS); cloud computing services; infrastructure as a service (IaaS); platform as a service (PaaS); software as a service (SaaS) featuring software for enterprise resource planning, customer relationship management, supply chain management, financial management, and business process automation; technical support services, namely, troubleshooting and help desk services in the field of computer software and information technology; software as a service (SaaS) featuring an virtual mascot guide for use in information technology support, troubleshooting, and user onboarding; computer consulting and information technology support services; application service provider (ASP) services featuring software for managing business processes and enterprise data; data hosting services and electronic storage of data; database design and development services; Computer services, namely, providing online non-downloadable software tools for data visualization and reporting; providing artificial intelligence as a service (AIaaS); design and development of computer software for use in IoT, cybersecurity, and digital platforms; consulting in the field of IT architecture and digital infrastructure; providing online non-downloadable educational software and training platforms in the field of information technology and artificial intelligence; design and development of digital and multimedia content featuring a fictional character or mascot; creating and maintaining websites and digital platforms for others featuring branded content, including virtual assistants, chatbots, and animated characters; providing temporary use of non-downloadable virtual assistants and AI-powered digital agents featuring a fictional character or mascot for customer interaction, training, and enterprise support services
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The processing circuitry receives a stimulus associated with the overlay system and creates an executable graph-based model that implements an external datastore in the storage element, based on the stimulus. The processing circuitry creates, in the executable graph-based model, a plurality of node groups that implements a plurality of schemas associated with the external datastore, with a first node group created for a first schema. The processing circuitry creates, in the first node group, a first set of active nodes that implements a first database object associated with the first schema. The processing circuitry creates, in the first node group, a second set of active nodes that implements a first set of database records associated with the first database object. The processing circuitry executes one or more operations in the executable graph-based model.
A system for context-based graphical element replacement is provided. Various graphical elements (e.g., images, icons, or the like) are detected in a source file. For each graphical element, a visual context of the graphical element and one or more additional contexts are determined. The one or more additional contexts include a context associated with information present within a predefined range of the graphical element, a visual context of at least one other graphical element, and a context associated with the source file. A replacement graphical element is then generated for each detected graphical element based on the determined visual context and the one or more additional contexts associated with the corresponding graphical element. The graphical elements in the source file are replaced with the replacement graphical elements to generate a replacement file. The replacement file is then rendered on a user device.
A system for framework selection for task execution is provided. Framework records of various frameworks are analyzed and based on the analysis, a translation instruction set is generated for each framework to translate an operation from a predefined format to a format associated with the corresponding framework. The most suitable framework is determined for a particular operation. A mapping between various operations and the most suitable framework for each operation is generated. During task execution, multiple natural-language tasks are derived from a natural-language task description. Each natural-language task is translated into a neo-syntax operation. Based on the mapping, a suitable framework is selected for the execution of each neo-syntax operation. The operations are translated to the syntax of the suitable frameworks by using the corresponding translation instruction set. A task output for the natural-language task description is generated based on the execution of the translated operations.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
A system for framework selection for task execution is provided. Framework records of various frameworks are analyzed and based on the analysis, a translation instruction set is generated for each framework to translate an operation from a predefined format to a format associated with the corresponding framework. The most suitable framework is determined for a particular operation. A mapping between various operations and the most suitable framework for each operation is generated. During task execution, multiple natural-language tasks are derived from a natural-language task description. Each natural-language task is translated into a neo-syntax operation. Based on the mapping, a suitable framework is selected for the execution of each neo-syntax operation. The operations are translated to the syntax of the suitable frameworks by using the corresponding translation instruction set. A task output for the natural-language task description is generated based on the execution of the translated operations.
The disclosure relates to a method and system for dynamically mitigating threats of generative Artificial Intelligence (AI) models. Conventional systems often suffer from inefficiencies due to sequentially applying threat detection checks leading to unnecessary preprocessing and increased computational demands. Additionally, such systems typically focus only on input data, neglecting potential threats in outputs. The disclosed system and method addresses these drawbacks by employing a hierarchical structure of macro and nano classifiers. The system utilizes macro classifiers for broad initial threat categorization followed by specialized nano classifiers for detailed analysis of specific threat subtypes, thereby optimizing processing time and computational resources. The system operates in real time, applying predefined moderation rules to both input and output data to ensure comprehensive threat mitigation. Additionally, continuous telemetry data updates refine nano classifiers and threat identification mechanisms, maintaining high accuracy and adaptability. The disclosed method enhances safety efficiency and reliability of generative AI models.
This disclosure relates to a method and a system for generating a network design document for a communication network using a generative AI model. The method includes receiving, by the generative AI model, a user query corresponding to the communication network to generate the network design document for the communication network. The method further includes retrieving, by the generative AI model, current network information associated with the communication network using a predefined retrieving technique, upon receiving the user query. The method further includes processing, by the generative AI model, the user query and the current network information associated with the communication network. The method further includes generating in a pre-defined format, by the generative AI model, the network design document for the communication network in response to the processing.
A method and system for prediction based on asynchronous and heterogeneous time-series data streams is provided. The asynchronous and heterogeneous time-series data streams are aligned onto a unified temporal grid. The aligned time-series data streams are synchronous, and sampling frequencies of the aligned time-series data streams are identical. Cross-attention is executed on the aligned time-series data streams across multiple attention windows. Each attention window is associated with a different time duration. A cross-attention output is generated based on the execution of the cross-attention for each attention window. Fused embeddings are generated based on the cross-attention outputs generated for the multiple attention windows. A prediction output is generated based on the plurality of fused embeddings for the time-series data streams.
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model. The processing circuitry receives a stimulus associated with simulation of an object of an external system. The processing circuitry instantiates, in the executable graph-based model, a preliminary node for the object. The preliminary node is associated with a set of attributes. The processing circuitry maps, to the set of attributes, a set of attribute values associated with the object. Further, the processing circuitry instantiates, in association with the preliminary node, a simulation overlay node in the executable graph-based model. Based on the association with the simulation overlay node, the processing circuitry customizes the preliminary node. Further, based on the customization of the preliminary node, the processing circuitry generates a simulated node simulating the object.
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model having various bi-directional nodes and connection links. The processing circuitry is configured to receive a stimulus and identify, based on the stimulus, a first bi-directional node in the executable graph-based model. The processing circuitry is further configured to determine, coupled to the first bi-directional node, a first connection link that includes an outward connection object defining association with the first bi-directional node and an inward connection object defining association with a second bi-directional node. The processing circuitry is further configured to identify, based on the first connection link, the second bi-directional node and execute an operation associated with the stimulus based on at least one of the first bi-directional node, the second bi-directional node, or the first connection link.
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model having various signal nodes and connection links. The processing circuitry receives a stimulus and identifies a first signal node based on the stimulus. The processing circuitry determines a connection link coupled to the first signal node. The connection link includes an outward connection object defining association with the first signal node and an inward connection object defining association with a second signal node. The second signal node is thus identified based on the first connection link. The processing circuitry generates a signal using the first signal node and determines whether a signal potential of the signal exceeds a potential threshold associated with the first signal node. The processing circuitry, based on the signal potential exceeding the potential threshold, communicates the signal to the second signal node.
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model having various fractal nodes and various connection links. The processing circuitry receives a stimulus and identifies a first fractal node based on the stimulus. The first fractal node includes various bi-directional nodes and connection links. Further, the processing circuitry determines a set of bi-directional nodes and a set of connection links from the first fractal node for processing the stimulus. The set of connection links couples the set of bi-directional nodes to each other. Additionally, the processing circuitry executes an operation associated with the stimulus based on the determined bi-directional nodes and connection links.
An overlay system is provided that includes a plurality of storage elements and processing circuitry coupled thereto. The plurality of storage elements store a plurality of executable graph-based models such that first and second storage elements store first and second executable graph-based models, respectively. Each executable graph-based model includes a plurality of nodes. The processing circuitry receives a stimulus to share a first node of the first executable graph-based model with a second node of the second executable graph-based model. The processing circuitry instantiates a tenant overlay node that is associated with the second node and includes a set of constraints to be adhered to by the second node while sharing the first node. The processing circuitry creates a sharing channel as a medium between the first and second storage elements. The sharing channel and the tenant overlay node enable sharing of the first node with the second node.
This disclosure relates to method and system for determination of personality traits of agents in a contact center. The method includes retrieving textual data corresponding to a conversation between a first agent and a first customer. The method further includes generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model. The natural language justification may include one or more sentences. The one or more sentences may include a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits. The method further includes determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.
A system for executing a search in a dataset is provided. The system includes a storage element that stores a directed property graph derived from the dataset. The directed property graph includes entity vertices corresponding to entities of the dataset, edges corresponding to properties of the entities, and value vertices corresponding to data values of the properties. Each edge couples an entity vertex to a value vertex and includes a label indicating an association therebetween. The system further includes processing circuitry that receives a search query including a reference value. The processing circuitry identifies a value vertex having a data value that is associated with the reference value and generates a response to the search query based on labels of edges coupled to the value vertex, and entities of the dataset represented by entity vertices coupled to the value vertex by way of the edges.
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model having a plurality of nodes. The processing circuitry receives a stimulus indicative of creation of a complex node in the executable graph-based model. The processing circuitry identifies, from the plurality of nodes, a set of nodes associated with the creation of the complex node. The processing circuitry determines, for each of the set of nodes, a node-type that indicates a node behavior of the corresponding node. The processing circuitry further determines, based on the node-type of each of the set of nodes, a complex node behavior that is indicative of a set of operations to be performed for the creation of the complex node. The processing circuitry executes the set of operations on the set of nodes to create the complex node.
The disclosure relates to method and system for managing recaching of pages. The method includes extracting a set of attributes associated with a page. The method includes generating a set of first scores and a second score. Each of the set of first scores is generated based on an associated subset of the set of attributes and the second score is generated based on a set of network parameters. The method includes determining a recaching action for the page, based on the set of first scores and the second score using a Machine Learning (ML) model.
This disclosure relates to a method and a system for generating a network design document for a communication network using a generative AI model. The method includes receiving, by the generative AI model, a user query corresponding to the communication network to generate the network design document for the communication network. The method further includes retrieving, by the generative AI model, current network information associated with the communication network using a predefined retrieving technique, upon receiving the user query. The method further includes processing, by the generative AI model, the user query and the current network information associated with the communication network. The method further includes generating in a pre-defined format, by the generative AI model, the network design document for the communication network in response to the processing.
This disclosure relates to method and system for managing co-development of applications. The method includes receiving a request corresponding to a first user to edit a Graphical User Interface (GUI) of a development environment for an application. The application includes a plurality of pages, which includes a plurality of blocks. The method includes retrieving lock data of each of the plurality of blocks and a first set of user privileges associated with the first user. The lock data of each block includes a lock status. The method includes determining editability of the first user to each of the plurality of blocks based on the lock data of each of the plurality of blocks and the first set of user privileges. The method includes rendering the GUI on a first user device associated with the first user based on the determined editability.
The disclosure relates to a method and a system for generating seasonally adjusted responses in real time. The method includes receiving a set of parameters from a device associated with a user. The method further includes querying a first database based on the set of parameters. The first database is generated using a machine learning (ML) model. The method further includes retrieving a plurality of query fragments related to the set of parameters from the first database. The method further includes generating a seasonally adjusted response based on the plurality of query fragments and a plurality of performance metrices.
A method for managing updates includes receiving a first set of elements indicative of a first set of updates to be made to a first webpage that is currently deployed. Prior to deployment of the updated first webpage, a first impact score indicative of an impact of the first set of updates on a set of performance metrics associated with the first webpage is predicted. A first deployment strategy is selected from a plurality of pre-defined deployment strategies based on the predicted first impact score and a set of rules. The first deployment strategy is executed for updating and deployment of the first webpage based on the first set of updates.
This disclosure relates to a page management method and system thereof. The method includes categorizing, using a first ML model, a set of pages into at least one of a plurality of strands, based on a set of first parameters. Further, the method includes classifying each of the set of pages, using a second ML model, into one of a plurality of sub-strands, based on an associated set of second parameters and the associated strand from the plurality of strands. Further, the method includes determining, for each of the set of pages, a score, based on the associated sub-strand, the weight assigned to each of the subset of second parameters of the associated sub-strand, and values of each of the subset of second parameters of the associated sub-strand. Further, the method includes performing an action on at least one of the set of pages based on the determined score.
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
39.
GENERATIVE ADDITION OF MUSICAL INSTRUMENT TONES TO SONGS
Generative filling of musical instrument tones to songs is provided. For a vocal track, a genre and musical instruments to be added to the vocal track are determined. Using an audio synthesis model, an audio tone is generated for each musical instrument in conformity with the genre. Further, each audio tone is converted into a spectrogram, which when processed based on temporal dependencies, generates a refined temporal sequence for the audio tone. Based on the refined temporal sequence of each audio tone, an audio waveform is generated. A simple additive mixing operation is executed on the vocal track and the audio waveforms generated for the musical instruments to generate an audio track (e.g., a new song).
The disclosure relates to system and method for exchanging data between data processing systems. The method includes receiving, from a second data processing system by a first data processing system, a data trigger associated with an event within the second data processing system. The first data processing system employs first symmetric keys for data communication and the second data processing system employs second symmetric keys for data communication. The first symmetric keys are distinct from the second symmetric keys. The data trigger is received without application of the second symmetric keys. The method further includes transmitting by the first data processing system to the second data processing system, a data snippet. The data snippet is representative of a data superset within the first data processing system corresponding to the data trigger. The data snippet is transmitted without application of the first symmetric keys.
A method for access management in graph-based model is provided. The graph-based model comprises a plurality of nodes and an overlay structure comprising processing logic. The overlay structure is associated with one or more nodes of the plurality of nodes. Processing circuitry determines a first node group of the graph-based model. The first node group comprises at least one node. The processing circuitry associates a first contract with the first node group such that the first contract is configured to act as a proxy for one or more nodes within the first node group in relation to requests from outside the first node group. The processing circuitry receives a stimulus and a context associated therewith. The stimulus is associated with the first contract. The processing circuitry maps the stimulus to the first contract to determine an access response. The processing circuitry processes the stimulus based on the access response.
A method for contract-based loading of executable-graph based models is provided. A stimulus and an associated context are received. The stimulus is associated with a contract node having a contract identifier. Based on the contract identifier, a contract node manifest associated with the contract node, a storage location associated with a contract node state, and node identifiers of nodes associated with the contract node are obtained. The contract node is generated based on the contract node manifest and the contract node state. Node manifests associated with the nodes are obtained. Each node manifest comprises a node identifier and a storage location associated with a respective node state. Node states for the nodes are obtained. Each node state is obtained from the storage location associated with the respective node state. The nodes are generated based on the node manifests and the node states.
A method for access management in graph-based model is provided. The graph-based model comprises a plurality of nodes and an overlay structure comprising processing logic. The overlay structure is associated with one or more nodes of the plurality of nodes. Processing circuitry determines a first node group of the graph-based model. The first node group comprises at least one node. The processing circuitry associates a first contract with the first node group such that the first contract is configured to act as a proxy for one or more nodes within the first node group in relation to requests from outside the first node group. The processing circuitry receives a stimulus and a context associated therewith. The stimulus is associated with the first contract. The processing circuitry maps the stimulus to the first contract to determine an access response. The processing circuitry processes the stimulus based on the access response.
The disclosure relates to system and method for smart test execution in a network test automation platform. The method includes retrieving set of parameter values for each of plurality of test cases in test suite corresponding to set of risk parameters based on latest release of product code. The method further includes calculating risk index for each of plurality of test cases based on set of parameter values and predefined set of weightage scores. The method further includes determining execution order of plurality of test cases based on calculated risk index. The method further includes executing set of priority test cases from plurality of test cases based on execution order and predefined threshold risk escape value. The method further includes dynamically rendering in real-time, one or more live charts based on current risk escape value upon execution of the set of priority test cases via GUI.
A method for loading run-time nodes of a template-based executable graph-based model is provided. A node instance manifest state is obtained based on a first node identifier associated with a node instance. The node instance manifest state includes a second node identifier associated with a node template. A node instance manifest is generated from the node instance manifest state. The node instance manifest comprises a first storage location and the first node identifier. A node instance state is obtained from the first storage location. The node template is obtained based on the second node identifier. The node instance is generated from the node instance manifest, the node instance state, and the node template. A run-time node, comprising a composition of the node instance and the node template, is generated.
This disclosure relates to method and system for framework agnostic smart test orchestration in network test automation in a network. The method includes receiving input data for executing one or more test scripts to test one or more functions on one or more devices in the network. The input is received from a user device via a web-based application. The method comprises parsing the input data and selecting an execution module from a plurality of execution modules for executing the one or more test scripts on the one or more devices based on the parsed input data. The method comprises executing the one or more test scripts to test the one or more functions on the one or more devices using the selected execution module.
This disclosure relates to a method and system for sanitization of sensitive data. The method includes analyzing sensitive data present within a page of an application based on a deterministic algorithm. Further, the method includes classifying the sensitive data into a high risk sensitive data and a low risk sensitive data based on a ML classification algorithm. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a destructive sanitization on each of the high risk sensitive data. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a non-destructive sanitization on each of the high risk sensitive data.
This disclosure relates to method and system for generating user role-specific responses through Large Language Models (LLMs). The method includes receiving a user query from a user account. The user account is associated with a user role from a plurality of user roles. The method includes combining, based on the user role, the user query with contextual information corresponding to the user query to obtain a combined query. The method includes inputting the combined query to an LLM. The method includes generating a user role-specific response corresponding to the combined query through the LLM. The method includes rendering the user role-specific response to a display of a user device associated with the user account.
The disclosure relates to method and system for generating contextual thumbnail previews. The method includes extracting data associated with a project including at least one component. The data includes one or more breakpoints, and one or more locales defined for the at least one component of the project. The method further includes generating a contextual map based on the data received; generating one or more interim thumbnails based on the contextual map; mapping the one or more interim thumbnails with pre-stored thumbnails within a database through a Machine Learning (ML) model; generating a consolidated contextual map based on the contextual map and the mapping; and generating one or more final thumbnails corresponding to the contextual thumbnail previews based on the consolidated contextual map.
G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
The disclosure relates to method and system for hyper-localization of an application. The method includes obtaining a first page associated with the application. The first page includes at least one element. The method includes receiving user input corresponding to the at least one element associated with the first page for a first predefined locale of a plurality of locales. Each of the plurality of locales includes at least one of a geography and a language. The method includes associating the first page with a hyper-localization database. The hyper-localization database includes information corresponding to the at least one element of a plurality of pages for the plurality of locales. The method includes extracting data associated with the at least one element based on the user input from the hyper-localization database; and generating a second page for the first predefined locale based on the extracted data and a schema of the first page.
This disclosure relates to method and system for facilitating legacy code transformation. The method includes receiving legacy code data and natural language document from one or more data sources. Each of the one or more data sources is one of an external data source or an internal data source. Further, the method includes generating a first natural language output based on the legacy code data through a first LLM, and a second natural language output based on the natural language document through a second LLM. Further, the method includes fine-tuning one of the first LLM or the second LLM based on the first natural language output and the second natural language output, through a third LLM. Further, the method includes generating a natural language specification document corresponding to the legacy code data based on the first natural language output and the second natural language output through the third LLM.
This disclosure relates to a method and a system for providing real-time assistance to a user using a generative AI model. The method includes receiving by the generative AI model, a user query corresponding to an activity. The user query includes one or more multi-modal inputs. The generative AI model is pretrained based on a set of predefined policies associated with an entity. The method further includes processing in real-time, by the generative AI model, the user query to determine a type of the user query based on the activity. The method further includes providing, by the generative AI model, assistance to the user in real-time by generating instructions in response to the processing of the user query.
The disclosure relates to a method and system for dynamically mitigating threats of generative Artificial Intelligence (AI) models. Conventional systems often suffer from inefficiencies due to sequentially applying threat detection checks leading to unnecessary preprocessing and increased computational demands. Additionally, such systems typically focus only on input data, neglecting potential threats in outputs. The disclosed system and method addresses these drawbacks by employing a hierarchical structure of macro and nano classifiers. The system utilizes macro classifiers for broad initial threat categorization followed by specialized nano classifiers for detailed analysis of specific threat subtypes, thereby optimizing processing time and computational resources. The system operates in real time, applying predefined moderation rules to both input and output data to ensure comprehensive threat mitigation. Additionally, continuous telemetry data updates refine nano classifiers and threat identification mechanisms, maintaining high accuracy and adaptability. The disclosed method enhances safety efficiency and reliability of generative AI models.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
This disclosure relates to method and system for legacy network transformation. The method includes receiving data corresponding to a legacy WAN from one or more data sources. Further, the method includes analyzing the data to identify one or more transformation requirements for the legacy WAN, based on one of a statistical technique, or a machine learning technique. Further, the method includes determining one or more configuration parameters corresponding to the legacy WAN based on a set of pre-defined business policy rules and the one or more transformation requirements. Further, the method includes generating a high-level design for SD-WAN based on the one or more transformation requirements and the one or more configuration parameters. Further, the method includes generating a low-level design including configuration guidelines templates for the SD-WAN based on the high-level design. Each configuration guidelines template may facilitate the transformation of legacy WAN to the SD-WAN.
H04L 41/084 - Configuration en utilisant des informations préexistantes, p. ex. en utilisant des gabarits ou en copiant à partir d’autres éléments
H04L 41/0894 - Gestion de la configuration du réseau basée sur des règles
H04L 41/0896 - Gestion de la bande passante ou de la capacité des réseaux, c.-à-d. augmentation ou diminution automatique des capacités
H04L 41/142 - Analyse ou conception de réseau en utilisant des méthodes statistiques ou mathématiques
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
55.
Method and system for continuously tracking humans in an area
The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.
This disclosure relates to a method and a system for managing anonymized data across computing devices in a blockchain network. The method includes generating the blockchain network including a blockchain ledger based on a dataset associated with each of a plurality of end users. The method of generating the blockchain network includes constructing the blockchain ledger corresponding to the dataset associated with each of the plurality of end users; generating a unique ledger key corresponding to the dataset associated with each of the plurality of end users; and storing the dataset associated with each of the plurality of end users in the blockchain ledger based on the unique ledger key. The method further includes communicating a joining request to a second computing device for joining the blockchain network. The method further includes granting access to the second computing device to the dataset associated with the plurality of end users.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
The disclosure relates to a method and a system for optimizing transmission of user relevant events. The method includes generating an event metadata for a user relevant event from an event snippet of the user relevant event. The event snippet is obtained from a multimedia content. The multimedia content includes at least one of an audio stream or a video stream. The method further includes transmitting the event metadata associated with the user relevant event. The event metadata includes a set of frames associated with the user relevant event, a start timestamp and an end time stamp associated with the user relevant event, and key information associated with the user relevant event. The method further includes reconstructing a segment of the multimedia content associated with the user relevant event based on the event metadata.
A system for executing a search in a dataset is provided. The system includes a storage element that stores a directed property graph derived from the dataset. The directed property graph includes entity vertices corresponding to entities of the dataset, edges corresponding to properties of the entities, and value vertices corresponding to data values of the properties. Each edge couples an entity vertex to a value vertex and includes a label indicating an association therebetween. The system further includes processing circuitry that receives a search query including a reference value. The processing circuitry identifies a value vertex having a data value that is associated with the reference value and generates a response to the search query based on labels of edges coupled to the value vertex, and entities of the dataset represented by entity vertices coupled to the value vertex by way of the edges.
The disclosure relates to system and method for managing a plurality of applications. The method includes receiving a first input for customization of a master map associated with the plurality of applications. The master map includes a plurality of components and a set of control settings for each of the plurality of components. The method further includes modifying, in the master map, a status of a first control setting for a first component based on the first input; updating each of the plurality of applications based on the modified status of the first control setting in the master map; and modifying a configuration code associated with the each of the plurality of applications, based the update in each of the plurality of applications.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 8/71 - Gestion de versions Gestion de configuration
G06F 9/06 - Dispositions pour la commande par programme, p. ex. unités de commande utilisant des programmes stockés, c.-à-d. utilisant un moyen de stockage interne à l'équipement de traitement de données pour recevoir ou conserver les programmes
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
60.
METHOD AND SYSTEM FOR GENERATION AND PRESENTATION OF USER EXPERIENCE RECOMMENDATIONS
This disclosure relates to method and system for generation and presentation of User Experience (UX) recommendations. The method includes retrieving data corresponding to one or more users of an application from a database. The data includes one or more of user device screen recording data, application usage data, or user feedback data. The method further includes determining a set of UX parameter values and a set of UX recommendations based on the data using a first Artificial Intelligence (AI) model; generating a set of User Interface (UI) screen templates based on one or more of the set of UX parameter values, the set of UX recommendations, and the data, using a second AI model; and displaying a report through a Graphical User Interface (GUI) on a display of an administrator device.
This disclosure relates to method and system for calculation of test automation feasibility indices. The method includes receiving input data comprising user responses to a questionnaire associated with a test case. The questionnaire includes a first set of questions associated with an ease of test case automation, and a second set of questions associated with an impact of test case automation, and each of the plurality of questions comprises a corresponding weightage. The method further includes computing an ease of automation index based on the user responses and a first set of weightages corresponding to the first set of questions, and an impact of automation index based on the user responses and a second set of weightages corresponding to the second set of questions.
This disclosure relates to method and system for calculation of network test automation feasibility and maturity indices. The method includes receiving input data including user responses to a questionnaire associated with a test case. The questionnaire may include a first set of questions associated with a coverage of test case automation, and a second set of questions associated with a usefulness of test case automation. Each question of the questionnaire may include a corresponding weightage. The method further includes computing a coverage of automation index based on the user responses and a first set of weightages corresponding to the first set of questions, and a usefulness of automation index based on the user responses and a second set of weightages corresponding to the second set of questions.
This disclosure relates to method and system for determination of personality traits of agents in a contact center. The method includes retrieving textual data corresponding to a conversation between a first agent and a first customer. The method further includes generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model. The natural language justification may include one or more sentences. The one or more sentences may include a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits. The method further includes determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.
The present disclosure relates to method and system for managing business rules in a process enterprise. Firstly, an input from a user is received for managing one or more business rules from a plurality of business rules associated with the process enterprise. The input is parses for extracting one or more business keywords. Further, one or more technical keywords corresponding to the one or more business keywords are identified based on a pre-stored mapping information, using a Natural Language Processing (NLP) model. Then, at least one business rule is identified from the plurality of business rules based on the one or more technical keywords. Further, one or more actions to be performed on the at least one business rule is identified based on the input. Finally, the at least one business rule is updated, based on the one or more actions.
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
A method for maintaining ownership integrity of templated executable graph-based models is provided. A node template that comprises a predetermined node structure and rules governing generation of node instances is obtained. A bounded executable run-time node is generated. The bounded executable run-time node comprises the node template, a node instance, and an overlay structure. The overlay structure comprises a run-time overlay and an ownership overlay. The run-time overlay comprises an overlay template and an overlay instance that includes processing logic for interaction with the node template and/or the node instance during execution. The ownership overlay defines an ownership rule associated with the bounded executable run-time node. A stimulus and an associated context are received and, in response to the stimulus being received, execution of the processing logic of the run-time overlay is caused in accordance with the ownership rule of the ownership overlay.
An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various executable nodes that communicate with each other by way of messages. The executable graph-based model further includes a message node for each message associated with the overlay system. Each message node is associated with one or more time-series analytics overlay nodes that execute a corresponding set of time-series computation functions on the corresponding message node. A publisher overlay node associated with each time-series analytics overlay node may generate and publish a statistical insight based on an output of each corresponding time-series computation function. The processing circuitry may use the statistical insight to generate an analytics outcome.
This disclosure relates to a method and a system for managing extensions of a product. The method includes determining a set of extensions associated with the product. The set of extensions includes a set of existing extensions and a set of potential extensions. The method further includes receiving a user selection corresponding to an extension from the set of extensions. The method further includes comparing the extension with the set of existing extensions. The method further includes generating a boilerplate code assembly corresponding to the extension in response to comparing. The boilerplate code assembly is generated based on a definition language template, and a business logic.
G06F 9/44 - Dispositions pour exécuter des programmes spécifiques
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
This disclosure relates to a method and system for hyper-personalization of user experience. The method includes receiving a data request from a user device of a user upon accessing a page of an application. The page may include a plurality of experience blocks. Each of the plurality of experience blocks may include one or more states. Each of the one or more states may include a unique layout of GUI elements. The method further includes selecting a state from the one or more states for each of the plurality of experience blocks in a data model schema, based on a set of rules and a first user profile of the user associated with the first data request. Further, the method includes rendering the page with each of the plurality of experience blocks in the selected state on the user device to provide a hyper-personalized user experience to the user.
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 16/21 - Conception, administration ou maintenance des bases de données
69.
METHOD AND SYSTEM FOR KNOWLEDGE TRANSFER BETWEEN DIFFERENT ML MODEL ARCHITECTURES
This disclosure relates to a method and system for managing knowledge of a primary ML model. The method includes generating a set of class probabilities for an unlabelled dataset based on a labelling function. The unlabelled dataset may be associated with the primary ML model, and the primary ML model may employ a first ML model architecture. Further, the method includes transferring the unlabelled dataset and the associated set of class probabilities for training a secondary ML model based on a knowledge transfer technique. The secondary ML model may employ a second ML model architecture. It should be noted that the first ML model architecture is different from the second ML model architecture.
An overlay system provided, includes processing circuitry and storage circuitry that stores primary executable graph-based models and auxiliary executable graph-based models. Each primary executable graph-based model is mapped to one or more auxiliary executable graph-based models based on various rules. The processing circuitry receives a stimulus associated with the overlay system and identifies, based on the stimulus, a primary executable graph-based model and one or more rules. Further, one or more values associated with an auxiliary executable graph-based model that is mapped to the primary executable graph-based model are retrieved based on the one or more rules. One or more values associated with the primary executable graph-based model are populated based on the retrieved one or more values. The processing circuitry further executes an operation associated with the stimulus based on the primary executable graph-based model that is populated with the one or more values.
G06F 16/90 - Détails des fonctions des bases de données indépendantes des types de données cherchés
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/908 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model including a plurality of nodes, a plurality of rule overlay nodes, and a plurality of data analysis overlay nodes. The processing circuitry receives a stimulus indicative of a data analysis operation and identifies a first node, a rule overlay node associated with the first node, and a data analysis overlay node associated with the first node. The processing circuitry executes a set of rules associated with the rule overlay node on a composition of the first node to generate a set of outputs. The data analysis overlay node uses the set of outputs to determine whether a data analysis score associated with the first node exceeds a data analysis score threshold.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
72.
SYSTEMS AND METHODS FOR HYPERGRAPH BASED NEURAL NETWORKS
An artificial neural network (ANN) modelled as an overlay hypergraph comprising a plurality of hyperedges, a plurality of role nodes, and one or more overlay nodes. A hyperedge of the plurality of hyperedges represents an artificial neuron within the ANN and comprises a set of role nodes each of which representing a portion of a connective relationship within the ANN. A role node of the plurality of role nodes represents a connection between layers of the ANN and comprises a first connective relationship associated with a first hyperedge and a second connective relationship associated with a second hyperedge such that the role node functionally connects the first hyperedge and the second hyperedge. The one or more overlay nodes comprise processing logic operable to interact with at least one hyperedge or at least one role node coupled to the one or more overlay nodes.
An overlay system including a storage element and processing circuitry is provided. The storage element stores an executable graph-based model that includes various active nodes and various micro-overlay nodes. Further, each active node includes multiple node elements, where each micro-overlay node is associated with at least one node element, and extends the functionality of the corresponding node element. The processing circuitry receives a contextualized stimulus. Further, the processing circuitry identifies an active node, node elements of the active node associated with the processing of the contextualized stimulus, and one or more micro-overlay nodes associated with each identified node element. The processing circuitry executes an operation associated with the contextualized stimulus based on the identified node elements and one or more micro-overlay nodes associated with each identified node element.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
74.
RESOURCE UTILIZATION IN OVERLAY SYSTEMS USING PROJECTIONS
An overlay system including a storage element and processing circuitry is provided. The storage element stores an executable graph-based model. The processing circuitry receives a contextualized stimulus. Further, the processing circuitry identifies nodes associated with processing of the contextualized stimulus and determines an association between each identified node and a node layer present in the executable graph-based model. The processing circuitry loads the identified nodes, simultaneously, into the executable graph-based model based on the determined association such that one or more identified nodes are loaded into at least one node layer. The identified nodes loaded into the executable graph-based model form a projection. The processing circuitry further executes an operation associated with the contextualized stimulus based on the projection.
An overlay system is provided that includes processing circuitry and a storage element that stores an executable graph-based model including various active nodes, various history message nodes, and various history overlay nodes. Each active node is associated with one or more history overlay nodes that facilitate the creation and maintenance of one or more history message nodes associated therewith. The processing circuitry receives a contextualized stimulus and identifies an active node and one or more history message nodes in the executable graph-based model based on the context. The processing circuitry creates one or more history nodes (e.g., one or more historical versions of the corresponding active node) based on the one or more history message nodes. Further, the processing circuitry executes an operation associated with the stimulus based on the identified active node, the one or more history overlay nodes, and the created one or more history nodes.
An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various active nodes, various history nodes, and various history overlay nodes. Each active node is associated with one or more history nodes and one or more history overlay nodes. The one or more history overlay nodes facilitate the generation and maintenance of one or more history nodes (e.g., one or more historical versions of the corresponding active node). The processing circuitry receives a contextualized stimulus associated with the overlay system and identifies an active node and one or more associated history nodes, in the executable graph-based model based on the context. The processing circuitry further executes an operation associated with the stimulus based on the identified active node, the associated one or more history overlay nodes, and the identified one or more history nodes.
An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various executable nodes that communicate with each other by way of messages. The executable graph-based model further includes a message node for each message associated with the overlay system. Each message node is associated with one or more analytics overlay nodes that execute a corresponding set of analytics operations on composition of the corresponding message node to generate one or more analytic insights. The composition includes data and transactional information associated with the corresponding message node. A publisher overlay node associated with the one or more analytics overlay nodes may generate and publish an analytics outcome based on an output of the execution of the set of analytics operations. The analytics outcome is indicative of performance of the overlay system.
An overlay system is provided that includes a plurality of storage elements and processing circuitry coupled thereto. The plurality of storage elements store a plurality of executable graph-based models such that first and second storage elements store first and second executable graph-based models, respectively. Each executable graph-based model includes a plurality of nodes. The processing circuitry receives a stimulus to share a first node of the first executable graph-based model with a second node of the second executable graph-based model. The processing circuitry instantiates a tenant overlay node that is associated with the second node and includes a set of constraints to be adhered to by the second node while sharing the first node. The processing circuitry creates a sharing channel as a medium between the first and second storage elements. The sharing channel and the tenant overlay node enable sharing of the first node with the second node.
An overlay system is provided that includes a primary storage element, a plurality of auxiliary storage elements, and processing circuitry. The primary storage element stores an executable graph-based model having a plurality of nodes. The processing circuitry receives a first stimulus indicative of a data splintering instruction. Based on the first stimulus, the processing circuitry identifies a first node in the executable graph-based model and executes a data splintering operation on the first node to divide the first node into a plurality of splinters. The processing circuitry, based on the first stimulus, instantiates a plurality of location overlay nodes and associates the plurality of location overlay nodes with the plurality of splinters. Based on the association, the processing circuitry stores each splinter of the plurality of splinters in an auxiliary storage element indicated by a corresponding location overlay node.
An executable inference hypergraph representing a rule-based model, the executable inference hypergraph comprising a first hyperedge associated with a first inference rule of the rule-based model and encapsulating a plurality of value nodes storing a plurality of values such that the plurality of values form a part of a set of terms used to evaluate the first inference rule, wherein the plurality of value nodes include at least one of the one or more value nodes of a graph-based model. The executable inference hypergraph further comprising a rule overlay node coupled to the first hyperedge thereby forming a first executable inference rule, wherein the rule overlay node comprises processing logic operable to evaluate the first inference rule using the set of terms encapsulated by the first hyperedge. The executable inference hypergraph is executed to determine an inference outcome.
A graph-based model comprises a plurality of entity nodes indicative of a plurality of entities within a dataset and a hierarchical structure of nodes. The hierarchical structure of nodes include a plurality of data nodes indicative of a plurality of data values associated with the plurality of entities and a plurality of context nodes coupled between the plurality of entity nodes and the plurality of data nodes. The plurality of context nodes define contextual relationships between the plurality of entity nodes and the plurality of data nodes. A query comprising a query value is received and a node within the hierarchical structure of nodes is identified based on the query value. A traversal path is determined from the node to a first entity node related to the node and a response to the query is generated based on the traversal path.
An overlay system including processing circuitry and storage element that stores various base nodes and various security overlay nodes, is provided. Each base node is associated with one or more security overlay nodes that control access to the information value contained in the base node. The processing circuitry receives a contextualized stimulus that indicates a requirement for associating two or more nodes. The processing circuitry identifies two base nodes required for processing the contextualized stimulus and executes an operation on the two identified base nodes to create an aggregated node. Thus, the aggregated node contains a higher information value than the two identified base nodes. Consequently, the processing circuitry dynamically associates, with the aggregated node, one or more security overlay nodes that have an equal or higher security level than the security levels of security overlay nodes associated with the two identified base nodes.
An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes node templates, node instances, and index nodes. Each node template is associated with one or more node instances and one or more index nodes. Further, each index node includes index records. The processing circuitry receives a contextualized stimulus associated with the overlay system and identifies one or more index records that include an index value indicated by the contextualized stimulus. Based on the identified one or more index records, the processing circuitry further identifies one or more node instances required for stimulus processing. Further, the processing circuitry executes an operation associated with the stimulus based on the identified one or more node instances and each node template associated with the identified one or more node instances.
An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various executable nodes. Each executable node includes a base node and a set of handler overlay nodes. Each handler overlay node subscribes to messages mapped to the corresponding executable node and processes the messages in conjunction with the corresponding base node. The processing circuitry receives a contextualized stimulus associated with the overlay system and executes an operation associated with the stimulus using one or more handler overlay nodes that are mapped to the stimulus, and one or more base nodes that are associated with the one or more handler overlay nodes, respectively.
A method for dynamic execution of sub-graphs within executable graph-based models is provided. Processing circuitry obtains an executable graph-based model comprising a plurality of sub-graphs and an overlay structure comprising processing logic associated with the plurality of sub-graphs. Each sub-graph defines a hierarchical structure of related nodes. The processing circuitry receives a stimulus and a context associated with the stimulus. In response to the stimulus being received and based on the context, the processing circuitry maps the stimulus to a first sub-graph of the executable graph-based model. The processing circuitry causes execution of processing logic within the overlay structure based on the mapping. The processing logic is associated with one or more nodes of the first sub-graph.
A method for dynamic execution of sub-graphs within executable graph-based models is provided. Processing circuitry obtains an executable graph-based model comprising a plurality of sub-graphs and an overlay structure comprising processing logic associated with the plurality of sub-graphs. Each sub-graph defines a hierarchical structure of related nodes. The processing circuitry receives a stimulus and a context associated with the stimulus. In response to the stimulus being received and based on the context, the processing circuitry maps the stimulus to a first sub-graph of the executable graph-based model. The processing circuitry causes execution of processing logic within the overlay structure based on the mapping. The processing logic is associated with one or more nodes of the first sub-graph.
A system comprises an executable graph-based model. The executable graph-based model comprises a first overlay node. The first overlay node comprises processing logic that is operable to interact with one or more associated nodes of the executable graph-based model. Further, the executable graph-based model comprises a first node that has the first overlay node associated therewith. The system further comprises a processing unit configured to receive a first stimulus associated with the first overlay node and, in response to the first stimulus being received, cause execution of said processing logic of the first overlay node. Execution of said processing logic of the first overlay node is based on the first node.
A system comprises an executable graph-based model. The executable graph-based model comprises a first overlay node. The first overlay node comprises processing logic that is operable to interact with one or more associated nodes of the executable graph-based model. Further, the executable graph-based model comprises a first node that has the first overlay node associated therewith. The system further comprises a processing unit configured to receive a first stimulus associated with the first overlay node and, in response to the first stimulus being received, cause execution of said processing logic of the first overlay node. Execution of said processing logic of the first overlay node is based on the first node.
A method discloses obtaining a node template comprising a predetermined node structure and rules governing generation of node instances based on the node template. Data elements are received. A run-time node is generated in response to the reception of the data elements. The run-time node comprises the node template and a node instance. The node instance comprises the data elements mapped to the node template based on the rules of the node template. A run-time overlay comprising an overlay template and an overlay instance comprising processing logic implementing at least one generic rule of the overlay template are obtained. The executable run-time node is generated. It comprises a composition of the run-time node and the run-time overlay such that the processing logic of the run-time overlay is operable to interact with the run time node during execution of the executable run-time node.
A system comprises an executable graph-based model. The executable graph-based model comprises a first overlay node. The first overlay node comprises processing logic that is operable to interact with one or more associated nodes of the executable graph-based model. Further, the executable graph-based model comprises a first node that has the first overlay node associated therewith. The system further comprises a processing unit configured to receive a first stimulus associated with the first overlay node and, in response to the first stimulus being received, cause execution of said processing logic of the first overlay node. Execution of said processing logic of the first overlay node is based on the first node.
A method for access management in graph-based model is provided. The graph-based model comprises a plurality of nodes and an overlay structure comprising processing logic. The overlay structure is associated with one or more nodes of the plurality of nodes. Processing circuitry determines a first node group of the graph-based model. The first node group comprises at least one node. The processing circuitry associates a first contract with the first node group such that the first contract is configured to act as a proxy for one or more nodes within the first node group in relation to requests from outside the first node group. The processing circuitry receives a stimulus and a context associated therewith. The stimulus is associated with the first contract. The processing circuitry maps the stimulus to the first contract to determine an access response. The processing circuitry processes the stimulus based on the access response.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Platform as a service (PAAS) featuring cloud-based software platforms for leveraging cloud solution blueprints and cloud assets to accelerate cloud-powered enterprise transformation and increase business value in the nature of software platforms for helping enterprise customers leverage the cloud ecosystem and accelerate speed to market, namely, software for building, expanding, scaling, converting, designing, evaluating, securing, and improving the efficiency and performance of cloud infrastructure, accessing and implementing cloud infrastructure frameworks, bots and computer code, and for ensuring regulatory and security compliance; platform as a service (PAAS) featuring cloud-based software platforms to help businesses leverage the cloud ecosystem to accelerate speed to market in the nature of software platforms for helping enterprise customers leverage the cloud ecosystem and accelerate speed to market, namely, software for building, expanding, scaling, converting, designing, evaluating, securing, and improving the efficiency and performance of cloud infrastructure, accessing and implementing cloud infrastructure frameworks, bots and computer code, and for ensuring regulatory and security compliance; software as a service (SAAS) services featuring cloud solution blueprints and cloud assets to accelerate cloud-powered enterprise transformation and increase business value in the nature of software platforms for helping enterprise customers leverage the cloud ecosystem and accelerate speed to market, namely, software for building, expanding, scaling, converting, designing, evaluating, securing, and improving the efficiency and performance of cloud infrastructure, accessing and implementing cloud infrastructure frameworks, bots, and computer code, and for ensuring regulatory and security compliance; cloud computing software and services, namely, providing cloud-based software for project management in the fields of cloud ecosystems and cloud infrastructure, business management, and operations management in the fields of cloud ecosystems and cloud infrastructure; consulting services in the field of cloud computing; computer software consulting; software design and development
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Platform as a service (PAAS) and professional IT services featuring artificial intelligence (AI) platforms for analyzing, processing, developing, accessing, integrating, and distributing electronic data, software code and design artefacts, namely user flow, wireframes and prototypes, for improving efficiency of the software development lifecycle, accelerating business growth, and creating revenue-generating opportunities
09 - Appareils et instruments scientifiques et électriques
Produits et services
(1) Software featuring artificial intelligence (AI) for analyzing, processing, developing, accessing, integrating, and distributing electronic data, software code and design artefacts, namely user flow, wireframes and prototypes, for improving efficiency of the software development lifecycle, accelerating business growth, and creating revenue-generating opportunities
95.
Method and system for evaluating contract worthiness of performing artists
A method and system for evaluating contract-worthiness of performing artists is disclosed. The method includes receiving consolidated data corresponding to each of a set of performing artists. The method further includes processing the consolidated data by classifying a set of topics in the text data into a plurality of categories through a ML classification model, determining user engagement behavior segments based on sentiment scores associated with the text data, and determining a plurality KPIs based on the plurality of metrics, the plurality of categories, and the user engagement behavior segments. Further, the method includes calculating a contract worthiness score for the set of performing artists based on the plurality of KPIs using a trained ML contract worthiness scoring model and evaluating one or more of the set of performing artists for their contract worthiness based on the contract worthiness score and a threshold contract worthiness score.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
96.
METHOD AND SYSTEM FOR DEVELOPING AND MANAGING A PRODUCT
This disclosure relates to method and system for developing and managing a product. The method includes creating a product template including a plurality of data elements. The plurality of data elements is indicative of configurable items. The method further includes configuring one or more of the plurality of data elements in the product template to generate a product model. The method further includes configuring the product model using one or more of a plurality of rules, based on an industry specific domain knowledge, to obtain at least one product and to generate a set of instructions for the at least one product. The method further includes receiving one or more queries from a user device, corresponding to the at least one product. The method further includes processing the set of instructions in response to each of the one or more queries to create a set of executable actions.
A method for transforming data organized in tabular structure is disclosed. In some embodiments, the method includes assigning a score to each of a plurality of cells within a table based on an associated set of orthogonal features characterizing a set of data. The set of orthogonal features comprises visual features, syntactic features, and language-based features. The method further includes identifying for each of the plurality of cells, a cell type based on the assigned score. The method further includes determining a table type based on the cell type and the set of orthogonal features determined for each of the plurality of cells. The table type comprises one of a row-oriented table, a column-oriented table, or a composite table.
A method and system for generation of queries by machine learning (ML) models is provided. An ML model may generate a query based on reception of a data trigger. The query may be generated based on corresponding domain and a knowledge graph. The ML model may receive a response in an encoded format. The knowledge graph may evolve based on the response. A first subsequent query may be generated by the ML model based on the response and the evolved knowledge graph. The ML model may receive a response for the subsequent query in the encoded format. The ML model may determine whether the response culminates a current iteration. A second subsequent query may be generated by the ML model when the response does not culminate the current iteration. The current iteration may be terminated when it is determined that the response culminates the current iteration.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) services featuring software for improving the impact and efficiency of marketing operations, delivering real-time actionable insights, and capturing customer demand; Platform as a service (PAAS) featuring computer software platforms for improving the impact and efficiency of marketing operations, delivering real-time actionable insights, and capturing customer demand; Software design and development; computer software consulting; consulting services in the field of providing online, non-downloadable software and applications; software development consulting in the field of marketing; technical consulting in the field of artificial intelligence (AI) software customization
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) services featuring software for improving the impact and efficiency of marketing operations, delivering real-time actionable insights, and capturing customer demand; Platform as a service (PAAS) featuring computer software platforms for improving the impact and efficiency of marketing operations, delivering real-time actionable insights, and capturing customer demand; Software design and development; computer software consulting; consulting services in the field of providing online, non-downloadable software and applications; software development consulting in the field of marketing; technical consulting in the field of artificial intelligence (AI) software customization.
