3690 results about "World Wide Web" patented technology

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

Computerized systems and methods for automated Access Permissions Review in an organization. A method includes: (a) receiving a textual description of an Access Review Policy; (b) constructing a trigger for automatically initiating an Access Review process; (c) automatically collecting organizational data that pertains to: (i) roles and positions of users, (ii) user-specific characteristics, (iii) email messages sent and received by users, (iv) description of events in which users had accessed elements of the Organizational Resource. Upon a triggering of the trigger, the method provides to a fine-tuned Large Language Model (LLM), the Access Review Policy and augmentation data from the organizational data, and a prompt instructing the LLM to generate a proposal to revoke, modify, add or maintain the Access Permissions of a particular user, and further instructing the LLM to generate output that describes reasoning and supporting evidence for the proposal.

The present disclosure provides for technology that addresses technological challenges arising in the field of identity verification. In particular, the present disclosure provides for out-of-band identity verification workflows that allow a first user to issue a verification challenge to a second user to confirm the identity of the second user. For example, the first user may receive a communication claiming to be from the second user. Rather than attempting to confirm the identity of the second user in-band, the first user initiates an out-of-band verification challenge to the second user.

Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, message content is detected and a set of candidate stickers is identified based on the message content. A search icon is dynamically replaced with a representation of respective ones of the set of candidate stickers. At a first point in time, the search icon represents a first candidate sticker of the set of candidate stickers. At a second point in time, the search icon represents a second candidate sticker of the set of candidate stickers.

Methods, systems, and computer storage media for providing brand-focused security administration in an item listing system are described. Using a brand-focused machine learning model, the brand-focused security administrator engine supports generating different comprehensive reports and analytics on brand-related security metrics, including brand compliance rates, infringement incidents, enforcement actions taken, and overall item listing system integrity. The brand-focused security administrator engine also supports visualizing the brand-focused security analytics results data in a manner that enhances usability and decision-making by presenting complex brand-focused security analytics results data in a clear, actionable format. In operation, brand-focused security data is accessed. The brand-focused security data is analyzed using a brand-focused machine learning model that is trained based on a multi-dimensional authenticity analysis dataset. Brand-focused security analytics results data is generated. The brand-focused security analytics results data is communicated to cause display of one or more visualizations based on the brand-focused security analytics results data.

An apparatus, including a computer including a database which stores a controllable healthcare record and information contained in a master records file, and a distributed ledger and Blockchain technology system. The apparatus facilitates a video call between a user device and a provider device. The computer, after processing information for updating the controllable healthcare record, processes information for identifying a plurality of electronic records for the individual and a second healthcare provider associated with each electronic record. The computer updates each of the plurality of electronic records, and generates a record update message. The computer transmits the record update message to each of a plurality of second provider devices associated with each second healthcare provider. Information regarding the update to the controllable healthcare record and the update to each of the second records is stored in the distributed ledger and Blockchain technology system.

Systems and methods for custom filtering of large language model content are provided. A request associated with a user device may be received at a platform associated with a secure network environment. The request may concern usage of an online service associated with a large language model (LLM). A user interface may be generated that includes an input field. Prompt data may be provided by the user device via the input field of the user interface. The prompt data may be determined to include sensitive data in accordance with a data model trained to recognize patterns associated with sensitive data. The received prompt data may be modified to remove the sensitive data. The modified prompt data may be routed over a communication network to the online service associated with the LLM.

A dynamic marketplace system leveraging store and warehouse mobility features a neuroevolution (NE) engine, an electronic device, and a request handler facilitating communication between the electronic device and the NE engine. The NE engine interfaces with a data storage system and an event handler receiving real-time event data from a public cloud services processor. An intentions handler interprets user intention data to predict user behavior. The NE engine, integrated with a processor, generates a predictive evolutionary model for the marketplace based on request, event, and intention data. An AI agent processor within the NE engine creates a recommendation model for mobile retail vendors, devises route plans, and deploys vendors to strategic locations.

A communications session with a user may be automated using a language model. The language model may be instructed to select a next action to be performed where the next action may include transmitting a responsive communication to the user or performing an API call. The prompt used to query the language model may include one or more of the following: a representation of text of the communications session, a list of available API calls, instructions to select a next action, a representation of API calls performed, or a representation of API call responses received. The language model may be sequentially queried to continue the communications session by transmitting responsive communications or performing API calls. In some implementations, a prompt template may be used to generate the prompt and a prompt template may be selected using text of the communications session.

The embodiment of the invention provides a large language model retrieval enhancement generation-oriented file processing method and device, and the method comprises the steps: carrying out the information extraction and semantic information enhancement of a file according to different file types, and constructing a meta-information structure of the file in combination with an enterprise business scene; dividing the document content into a plurality of structured blocks based on the meta-information structure, and labeling the title and context information of each block; when the file is uploaded, according to the parent directory material quantity and content change of the directory where the file is located, performing dirty marking on the directory; when a data request is received, whether a dirty mark exists in a related directory or not is judged, if yes, the directory is requested to be locked, a summary is extracted from files extracted from bottom to top through a large language model, and the dirty mark is cleared after the summary is cached.

The disclosed embodiments describe systems and methods for approving a transfer. User information may be accessed for a plurality of users in a group. Account information for a plurality of disparate electronic accounts associated with the group may be accessed. The disparate electronic accounts may be integrated and managed through a central entitlements engine. Permissions information associated with the user information and the account information may be accessed. A transfer request for an account in the plurality of disparate electronic accounts may be received from a first user device associated with a first user of the plurality of users. The transfer request may be sent to a second user device associated with a second user of the plurality of users. An electronic indication of an approval of the transfer request may be received. The permissions information may be modifiable by input from the second user device.

Aspects of a trustworthiness check of a video data stream are described. According to a first aspect, a unique identifier which identifies a media asset to which a portion of a video data stream to be checked on trustworthiness belongs, is included into the trustworthiness check. According to a second aspect, a certificate of a content provider for performing the trustworthiness check is retrieved from a track of editors stored at an external resource. A third aspect provides a method for identifying a portion of a video data stream to be checked on trustworthiness using a digital signature. According to a fourth aspect, a digital signature for checking a portion of a video data stream is retrieved from an external resource.

A task management system detects condition(s) are satisfied with respect to task record(s), and triggers action(s) at least in part by substituting information from the task record(s) into placeholder(s) of user-modifiable template(s) to prompt large language model(s) for generating narrative(s) about how the condition(s) were satisfied. The triggered action(s) retrieve information from identified task record(s), substitute value(s) from the record(s) into the placeholder(s) of the user-modifiable template(s), and trigger(s) call(s) to large language model(s) to generate task completion narrative(s) or other narrative(s) for the task record(s). The narrative(s) may be stored in narrative field(s) of the record(s) for use in displaying the narrative(s) on a user interface, email notification, or other message.

Aspects of a trustworthiness check of a video data stream are described. According to a first aspect, a unique identifier which identifies a media asset to which a portion of a video data stream to be checked on trustworthiness belongs, is included into the trustworthiness check. According to a second aspect, a certificate of a content provider for performing the trustworthiness check is retrieved from a track of editors stored at an external resource. A third aspect provides a method for identifying a portion of a video data stream to be checked on trustworthiness using a digital signature. According to a fourth aspect, a digital signature for checking a portion of a video data stream is retrieved from an external resource.

Systems and methods for selection of client connection type in a virtual meeting based on stored configuration information. In response to a request of a first participant of a virtual meeting platform to join a virtual meeting, a configuration data structure associated with the virtual meeting is accessed. The configuration data structure identifies roles for participants of the virtual meeting. A first role associated with the first participant within the virtual meeting is identified using the configuration data structure. One or more client connection types are assigned to the first participant based on the first role. The first participant is allowed to join the virtual meeting using one of the one or more client connection types.

The present disclosure is related to Intelligent Transport Systems (ITS), and in particular, to service dissemination basic services (SDBS) and / or collective perception service (CPS) of an ITS Station (ITS-S). Implementations of how the SDBS and / or CPS is arranged within the facilities layer of an ITS-S, different conditions for service dissemination messages (SDMs) and / or collective perception message (CPM) dissemination, and format and coding rules of the SDM / CPS generation are provided.

A method for API connectors includes receiving from a user account a first API request formatted for a source system and comprising an intent, selecting a first action corresponding to the intent, generating a first prompt based on the first action, providing the first prompt to a first large language model (LLM) and receiving a response having a structured description of the intent, using the structured description of the intent to select a second action, generating a second prompt based on the second action, providing the second prompt to a second LLM and receiving a response having a second API request formatted for a destination system, providing the second API request to the destination system, receiving from the destination system a response to the second API request, and providing the response to the second API request to the user account.

A method performed by a communication device (12) is disclosed. The communication device (12) stores, at the communication device (12), a self-organizing network, SON, report (16) associated with a non-public network, NPN, including storing the SON report (16) in association with an NPN identifier of the NPN with which the SON report (16) is associated. The communication device (12) also transmits a message to the serving network indicating that the SON report (16) is available at the communication device (12), if a serving network of the communication device (12) is an NPN identified by an NPN identifier which matches the NPN identifier associated with the SON report (16).

Aspects of the present disclosure relate to an online resource that can initiate a conversation between a user and an automated assistant. The online resource receives from the user a query including a plurality of sub-queries, determines a context for each of the sub-queries, identifies a plurality of queries from the user during the conversation between the user and the automated assistant, and obtains responses to the sub-queries from a plurality of selected agents. The online resource determines a similarity score for each response by comparing the response with at least the context for the corresponding sub-query, summarizes the responses based at least in part on the similarity scores, and generates an answer to the query by combining the response summaries based at least in part on an alignment between the response summaries and their corresponding sub-queries.

Operating effectively in an increasingly prolific Internet of Things environment, one needs the ability to message, whether device status, actions taken, recommendations for actions or other information to those with a need to know and support responses to actionable messages. Texts and e-mail are limited; they can neither provide other outputs, be targeted in intelligent fashion nor selectively relayed or contain execution instructions for target devices. The present invention is an Extended-Capability Messaging Appliance (ECMA) that communicates with other IoT devices that provides these capabilities and can include AI. ECMA and non-ECMA AI elements can constitute a Hybrid AI Backbone. The Hybrid AI Integrator receives results from multiple A elements and develops one or more conclusions. An ECMA may be configured as a Body-Area Network for individuals or a Business-Area Network for industry and other commercial applications with an AI Agent called BANDIT that processes requests from a user.

Aspects of a trustworthiness check of a video data stream are described. According to a first aspect, a unique identifier which identifies a media asset to which a portion of a video data stream to be checked on trustworthiness belongs, is included into the trustworthiness check. According to a second aspect, a certificate of a content provider for performing the trustworthiness check is retrieved from a track of editors stored at an external resource. A third aspect provides a method for identifying a portion of a video data stream to be checked on trustworthiness using a digital signature. According to a fourth aspect, a digital signature for checking a portion of a video data stream is retrieved from an external resource.

A computer receives an interaction request message. The computer transmits, to a user device, a relying party identifier associated with a relying party computer. The user device thereafter determines a list of credentials or identifiers thereof based on the relying party identifier. The computer receives the list of credentials or identifiers thereof from the user device. The computer conducts an interaction using a credential, identifier thereof, of the list of credentials or identifiers thereof.

A tokenization system receives a request for redaction of sensitive textual content in a document, identifies a portion of the document as the sensitive textual content, and edits the document, including replacing the sensitive textual content thus identified with tokens, each token having a token value and a pattern that identifies a start and an end of the token value. The editing produces a transformed version of the document with the tokens and without the sensitive textual content. The tokenization system may then communicates the transformed version of the document with the tokens and without the sensitive textual content to the client computing system, an automated recognition service, or a redaction plug-in to a frontend application.

The invention provides a resume information extraction method and device, electronic equipment and a storage medium, and relates to the technical field of automatic information. The method comprises the steps of obtaining a to-be-processed resume file; analyzing the resume file through a pre-trained resume information extraction model to obtain a file format and content characteristics of the resume file; determining a target extraction tool of the resume file according to the file format and the content features; and performing information extraction on the resume file through the target extraction tool to obtain resume text content. The resume information extraction model only needs to extract the file format and the content feature of the resume file, since the file format and the content feature of the resume file are easier to identify, a large amount of data is not needed for model training, and different extraction tools can be utilized to perform information extraction on different types of resumes, so that the extraction efficiency is improved. And the accuracy of information extraction is improved.

Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.

The described system aims to reduce or eliminate inaccuracies and hallucinations in responses generated by a machine learning model when processing user queries. The data platform parses and categorizes the text within data files to create structured textual representations. The user submits multiple prompts which are collectively assessed to refine and modify the initial queries.The modified query is used to identifying segments of data files that are most relevant to the query. These relevant portions are then compiled into a Retrieval-Augmented Generation (RAG) context block. This RAG context block is fed into a prompt response machine learning model, which processes the enriched information to generate a well-informed and accurate response to the user's query. Finally, this response is displayed back to the user through the chat interface, completing a cycle that enhances the reliability and relevance of machine-generated answers.

Described are systems and processes for identifying relevant media content for users in response to natural language requests. The media content may be formed as a playlist of music. Requests may be sent to various models to obtain results, such as a large language model (LLM) and a local search model. Results may be selected from one or both models. The results may be used to fetch media content and provide user interfaces with a playlist with the media content to a user that submitted the request. User interfaces may include selectable search terms suggested for the user and animations during processing of the request.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling data access. One of the methods includes receiving, from a user device associated with an account, a request for data; accessing data from one or more data sources that includes, for each of a plurality of data types, corresponding data values; selecting, from the plurality of data types and using permissions data for the account, one or more data types that the account has permission to access; and providing, to the user device and for each of the one or more data types that the account has permission to access, the corresponding data values.