Abstract

Systems and methods for facilitating collaboration utilizing video conferencing including receiving conference files related to a video conference, including an audio recording file of at least a portion of the video conference, a video recording file of at least a portion of the video conference, and a document file shared in an original file type during the video conference. A package file including the plurality of conference files is generated and saved to a package file database.

IPC Classes  ?

• G06F 16/176 - Support for shared access to filesFile sharing support
• G06F 16/18 - File system types
• G06F 16/182 - Distributed file systems
• G06F 40/169 - Annotation, e.g. comment data or footnotes
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

Systems and methods for implementing SLA management including receiving a service request, comparing it to an SLA database to identify an applicable contract. Upon identifying no applicable contract, defining an execution parameter set from a standard parameter set. Upon identifying an applicable contract, determining a service request required completion time and a completion probability from the required completion time, and comparing the completion probability to a completion risk threshold. If the completion probability is equal to or exceeds the completion risk threshold, defining the execution parameter set by applying a parameter set from the applicable contract for the service request. If the completion probability is less than the completion risk threshold, escalating the service request priority and defining the execution parameter set based on the parameter set from the applicable contract and escalating the service request priority. Allocating resources and executing the service request responsive to the execution parameter set.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• H04L 47/80 - Actions related to the user profile or the type of traffic

Abstract

Systems and methods for blockchain value transfers including receiving at the server a plurality of transaction requests, each transaction request including sending and receiving user account addresses on first and second blockchain networks, a transaction value in in-network tokens, and an API request, performing a balance check procedure on each transaction request including determining if the transaction value is greater than a permitted transaction amount of the sending user account address, and either refusing the transaction or adding the transaction to an aggregate transaction record. The permitted transaction is updated for the sending and receiving addresses to reflect the transaction and a transaction including calculating a net transaction amount is initiated.

IPC Classes  ?

• G06Q 20/38 - Payment protocolsDetails thereof
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system

Abstract

System and methods of facilitating code development including receiving a build request to build a program from one or more stored build code files, retrieving the build code file, retrieving a meta-file associated with the build code files, analyzing the meta-file to identify a validation, and determining at least one of compliance and non-compliance with the validation criterion. Upon determining compliance with the validation criterion, the program is built. Upon determining non-compliance with the validation criterion, at least one of transmitting an alert to the build user, terminating the build request, and triggering a review process is performed.

IPC Classes  ?

• G06F 16/176 - Support for shared access to filesFile sharing support
• G06F 16/18 - File system types
• G06F 16/182 - Distributed file systems
• G06F 40/169 - Annotation, e.g. comment data or footnotes
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

Systems and methods for implementing secure agent-to-agent communications within a service mesh architecture including deploying within a service mesh infrastructure, implementing service discovery mechanisms within the service mesh, establishing encrypted communication channels between the agents, and performing at least one of managing traffic routing implementing an authentication protocol for agent-to-agent communications, and applying traffic policies within the service mesh, collecting observability metrics for communications, monitoring agent health through the service mesh.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

Systems and methods for securing agent communications through input/output transformation including generating a secured input including an input normalizer or a context enricher, deploying an output adapter including an abstraction layer and an output formatter, generating the secured input by processing a received input by the input normalizer or enriching the normalized input by the context enricher, routing the secured input to one or more agents for processing, generating an abstracted output by abstracting agent output by the abstraction layer, generating the formatted output by formatting the abstracted output by the output formatter according to one or more security policies, and transmitting the formatted output to one or more external systems.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

Systems and methods for processing artificial intelligence user requests including receiving a user input from a user device, performing a routing analysis on the user input on multiple characteristics, making a routing decision based on the routing analysis to send the user input to one or both of an experiential reasoning agent model and an analytical reasoning model, routing the user input to at least one of the experiential reasoning agent model and the analytical reasoning model responsive to the routing decision, receiving one or more outputs from at least one of the experiential reasoning agent model and the analytical reasoning model, generating a final result by performing a result validation procedure on the one or more outputs, and transmitting the final result to the user device.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

Systems and methods for implementing domain-specific agent networks including configuring specialized agents optimized for retrieving information from a respective specific knowledge domain, receiving a user query, analyzing the user query to identify relevant knowledge domains, activating a subset of the specialized agents corresponding to the relevant knowledge domains, retrieving information by the subset of specialized agents, aggregating the retrieved information including information from a plurality of knowledge domains, providing the aggregated information to one or more h-LLMs, receiving a plurality of responses from the one or more h-LLMs, and generating a comprehensive response from the plurality of responses, the comprehensive response incorporating information from the plurality of knowledge domains.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

Systems and methods for in-memory processing of h-LLM data including receiving an input data stream, operating a data receiver operable to divide the input data stream into a plurality of data batches, processing the plurality of data batches using a processing layer, the processing layer comprising a plurality of h-LLMs operating at least partially in volatile memory, and producing a plurality of processed data batches from an output of the processing layer.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

Systems and methods of adaptive context partitioning in a retrieval-augmented generation system, including receiving a query from a user; forming a combined context by retrieving relevant documents from a document database based on the query, determining characteristics of the combined context, monitoring current system resources including processor availability and memory utilization, dynamically determining a partition size based on the current system resources and the one or more characteristics of the combined context, partitioning the combined context into context partitions according to the partition size, generating intermediate analysis results by processing each context partition using a mapper prompt large language models (LLMs), generating a final response by processing the intermediate analysis results using a reducer prompt through the LLMs, and transmitting the final response to the user.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

Systems and methods for implementing a sidecar pattern for an AI agent including providing a main large language model (LLM) agent within a container included by a pod in a container environment, attaching a plurality of sidecar services to the main LLM agent, including at least two of implementing a logging service, implementing a guardrails service, implementing a memory management service, and implementing an explanation generator service, and operating the plurality of sidecar services within a container included by the pod that includes the container within which the main LLM agent is provided.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

A method for automated document processing and task assignment including receiving an input document from a document source, performing a content analysis on the input document, extracting metadata from the input document, identifying an identified task type to be performed, generating standardized metadata by converting the metadata into a standardized JSON format, storing the standardized metadata in a database, determining a user assignment for the identified task type, the including an assigned user, generating an action item including the identified task type, the user assignment, and the standardized metadata, and adding the action item to a task management system for processing by the assigned user.

IPC Classes  ?

• G06F 16/176 - Support for shared access to filesFile sharing support
• G06F 16/18 - File system types
• G06F 16/182 - Distributed file systems
• G06F 40/169 - Annotation, e.g. comment data or footnotes
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

A system and method for creating a merged large language model (h-LLM) using a bagging approach including receiving input data at a computer system, creating a plurality of data subsets from the input data, training a plurality of h-LLMs, each h-LLM of the plurality of h-LLMs being trained on a respective data subset of the plurality of data subsets, creating a merged h-LLM by merging the plurality of h-LLMs, and outputting the merged h-LLM.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

A system and method for securing inter-process communications (IPCs) between generative AI and external tool servers, including intercepting IPCs having a requested operation, performing a security analysis on the IPCs for security threats, performing a permission validation for permissions for the requested operation of each IPC, and either approving or blocking the requested operation of each IPC based on the security analysis and the permission validation.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

A system and method of creating and operating LLM agent AI-powered digital twins including collecting multimodal data streams from user devices, processing the multimodal data through specialized pipelines, generating specialized AI models for language, audio, video, and image processing, and performing external tasks in the real-world, using tools, combining the specialized models into an ensemble architecture, operating the ensemble model in a tethered mode with user oversight, continuously updating the model based on user feedback and interaction patterns, validating model performance against predetermined thresholds, implementing autonomous operation guardrails, and transitioning to autonomous untethered operation upon meeting performance criteria.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/045 - Combinations of networks

Abstract

A method for improving performance of functions-as-a-service including receiving at a function delivery network a first function call from a first user and a second function call from a second user calling the first function within a first time window. The method further includes sending a first function call request to a cloud service responsive to the first and second function calls, performing at the cloud service a first function responsive to the first function call request producing a first function result, receiving at the function delivery network the first function result, and transmitting from the function delivery network the first function result to each of the first user and the second user.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

Abstract

A system with hierarchical chat functionality including a first-tier hierarchical data object, a plurality of second-tier hierarchical data objects linked to the first hierarchical tier data object, and a plurality of third-tier hierarchical data objects linked to a second-tier hierarchical data object of the plurality of second-tier hierarchical data objects. The system further includes first and second real-time text communication groups linked to at least one hierarchical data object and being accessible by the users associated with the linked hierarchical data object.

IPC Classes  ?

• G06F 16/176 - Support for shared access to filesFile sharing support
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
• G06F 16/18 - File system types
• G06F 16/182 - Distributed file systems
• G06F 40/169 - Annotation, e.g. comment data or footnotes

Abstract

A method for facilitating real-time collaboration of document mark-up and events correlation comprising receiving an inbound document from a user device, receiving an indication of an associated event of the plurality of events with which the inbound document is associated, storing the inbound document in the cloud document database, defining a stored inbound document, extracting information from the stored inbound document, defining extracted information, adding metadata to the stored inbound document associating the inbound document with the associated event responsive to the indication of the associated event and the extracted information, modifying the event spreadsheet responsive to the indication of the associated event, receiving a request to access the stored inbound document from a user device, and transmitting the stored inbound document to a browser application running on the user device.

IPC Classes  ?

• G06F 16/176 - Support for shared access to filesFile sharing support
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
• G06F 16/18 - File system types
• G06F 16/182 - Distributed file systems
• G06F 40/169 - Annotation, e.g. comment data or footnotes

Abstract

A method of collaborating in real-time via action creation comprising detecting an annotation on a document, parsing the annotation; generating an action record responsive to information identified from parsing the annotation, and recording a generated action record to the action database. Action records can be retrieved by users for which they are the assigned user. The annotation does not change the file content of the document.

IPC Classes  ?

• G06F 16/176 - Support for shared access to filesFile sharing support
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
• G06F 16/18 - File system types
• G06F 16/182 - Distributed file systems
• G06F 40/169 - Annotation, e.g. comment data or footnotes

Abstract

A method for improving the performance of functions-as-a-service including receiving a first function call including a first argument, performing a first function responsive to the first argument including by the first function call, producing a first function result, generating a first precomputation argument that differs from the first argument responsive to the first function call, executing a first persistent function responsive to the first precomputation argument, the persistent function including performing at least one precomputation operation, the at least one precomputation operation including performing the first function responsive to the first precomputation argument, producing a first precomputation result, storing the first precomputation result, receiving a second function call including a second argument, comparing the second argument to the first precomputation argument, and upon determining the second argument is identical to the first precomputation argument, transmitting the first precomputation result from a source of the second function call.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06F 16/23 - Updating
• G06F 15/173 - Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake

Abstract

A method of optimizing performance of and securing cloud storage and databases comprising analyzing data comprised by a data request by an agent application on a computerized device, the data request being generated by a client application and inserting a tag into the data request responsive to the analysis of the data comprised by the data request, the tag indicating storage requirements for at least one of security, access speed, or fault tolerance.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 16/95 - Retrieval from the web