9052 results about "User device" patented technology

A system can process an indication of user data, wherein the user data comprises a food item including at least one recipe having one or more ingredients, and a query, wherein the query is associated with at least one ingredient, and generate, based at least in part on the query, nutrition data associated with the at least one ingredient, wherein the nutrition data comprises preparation data, sourcing data, delivery data, and a list of nutrients for the at least one ingredient. The system can further estimate, based at least in part on the nutrition data associated with the least one ingredient, a nutritional score, then compare the estimated nutritional score to a threshold, and based on the compared nutritional score, generate one or more instructions to cause an indication of the nutritional score on a user device.

An example operation may include one or more of receiving a sequence of inputs from a user during a conversation between the user and a chatbot within a chat window of a software application, executing a large language model (LLM) on each input from the user to determine a next prompt to output via the chatbot, respectively, wherein each execution of the LLM includes a new chat input from the user and a most-recent state of the conversation between the user and the chatbot within the chat window, and displaying the next prompt within a chat window on a user device.

A computer-implemented method and system for privacy-preserving location verification in distributed networks comprises initializing a multi-modal biometric authentication system on a user device, generating cryptographic keys using a distributed key generation protocol, binding the cryptographic keys to biometric templates using a fuzzy vault scheme, constructing and broadcasting encrypted location beacons, and generating zero-knowledge proofs of location claims. The system includes user devices equipped with biometric sensors and verifier devices configured to validate location claims and maintain consensus in a blockchain network. The method implements real-time liveness detection for multiple biometric input types, executes fault-tolerant consensus algorithms with privacy preservation, and maintains a dual-scoring mechanism comprising device trust scores and user reputation scores. The system enables secure location verification while preserving user privacy through cryptographic protocols and biometric authentication in decentralized environments.

A method for developing a generative AI system may include constructing a plurality of generative AI systems, wherein constructing the generative AI systems includes executing at least one modeling blueprint; providing a plurality of queries to each of the generative AI systems, the queries being part of an evaluation dataset; during processing of the queries by each generative AI system, monitoring values of one or more quantitative metrics; providing, for display by a user device, data indicating the values of the quantitative metrics for each generative AI system; and providing, for display by the user device, a recommendation regarding use or non-use of at least one generative AI system included in the plurality of generative AI systems.

An example operation may include one or more of receiving content from a conversation between a user on a user device and a second user on a second user device that is connected to the user device via a network, identifying a topic of the conversation based on execution of a generative artificial intelligence (GenAI) model on the received content from the conversation, identifying a call list that is previously stored in memory that is associated with the topic of the conversation based on keywords included in the identified topic of the conversation, and adding an identifier of the user to the call list stored in memory.

An example operation may include one or more of receiving an identifier of a product from a digital wallet on a user device, identifying one or more payment cards stored within the digital wallet on the user device, determining benefits that will be obtained by using each of the one or more payment cards to purchase the product via execution of an LLM on the identifier of the product and a corpus of credit card documents, and displaying a chat message within a chat window on the user device with a description of the determined benefits that will be obtained.

An example operation may include one or more of storing a current portfolio of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest of the user based on execution of a generative artificial intelligence (GenAI) model based on the received contextual data of the user and the current portfolio of the user stored in memory, predicting a performance of the current portfolio with the identified asset of interest included therein at a future point in time, and displaying the predicted performance of the current portfolio with the identified asset of interest included therein on a user interface.

An example operation may include one or more of storing a data store of credit card documentation, conversing with a user via a chatbot within a chat window of a software application, wherein the conversing comprises receiving natural language inputs with requests for information about a payment card, identifying a benefit obtained by the user based on the conversation, executing a large language model (LLM) on the identified benefit and the data store of the credit card documentation to generate a letter of confirmation, and generating an electronic message with the letter of confirmation attached, and transmitting the electronic message to a user device.

An example operation may include one or more of storing a database of payment card data, receiving an identifier of a product from a digital wallet on a user device, identifying one or more payment cards stored within the digital wallet on the user device, determining the benefits that will be obtained by using each of the one or more payment cards to purchase the product via execution of an LLM on the identifier of the product and the database of payment card data; and displaying a chat message within a chat window on the user device with a description of the determined benefits that will be obtained.

An example operation may include one or more of displaying a report on a user interface of a software application on a user device, listening to a call between a user on the user device and a different user on a second user device that is connected to the user device via a network, executing a generative artificial intelligence (GenAI) model based on content that is heard during the call and content within the report displayed on the user interface to identify content within the displayed report that is discussed during the call, and modifying the displayed report to emphasize the identified content within the displayed report on the user interface.

An example operation may include one or more of receiving a current status of a portfolio of a user and previous actions taken on the portfolio of the user over a predetermined period of time, determining a goal for the user based on execution of a generative artificial intelligence (GenAI) model on the current status of the portfolio of the user and the previous actions taken, receiving a request for the portfolio of the user via a user device, and in response to the request, generating a text-based description of the goal and displaying the text-based description of the goal with portfolio content from the portfolio of the user on a user interface of the user device.

An example operation includes one or more of establishing a communication session between a first user device and a second user device from among the plurality of user devices, training an artificial intelligence (AI) model to learn user interface preferences of a plurality of user devices during the communication session, receiving a description associated with the communication session, generating a plurality of windows of content and displaying the plurality of windows of content on a user interface of the first user device during the communication session based on execution of the AI model on the description associated with the communication session, and generating a second plurality of windows of content and displaying the second plurality of windows of content on a user interface of the second user device based on an execution of the AI model on the description associated with the communication session.

An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest that is not included in the portfolio of assets of the user based on execution of a generative artificial intelligence (GenAI) model on the portfolio of assets of the user and the received contextual data of the user, generating a different portfolio of assets based on the asset of interest that is not included in the portfolio of assets of the user, and displaying the different portfolio of assets via a user interface.

An example operation may include one or more of identifying a first topic from a call actively in progress, displaying a dashboard on a user device on the call, wherein the dashboard comprises content related to the first topic, receiving discussion data from the call, determining that a focus of the call has shifted from the first topic to a second topic, executing an artificial intelligence (AI) model on the second topic, dynamically generating dashboard content based on the execution, and displaying the dynamically generated dashboard content to the dashboard on the user device.

An inspection application may display, on a user device, a user interface including at least a portion of a vehicle inspection report including a plurality of inspection categories. The inspection application may configure the user device to obtain inspection information associated with a vehicle, the inspection information comprising photographs, videos, audio, and / or text. The inspection application and / or a network-accessible inspection assistant system may generate a prompt including at least a portion of the inspection information and information indicating potential vehicle defects. The prompt may be transmitted to a large language model that returns a response indicating any potential vehicle defects identified in the inspection information. The vehicle inspection report may then be updated to indicate the potential vehicle defects identified by the language model.

An aspect relates to determining if a request received from a user device is sufficiently similar to a cached request, wherein the request requesting an identification of streamable content meeting one or more criteria. The determination comprises comparing a vector corresponding to the received request with vectors of previously received, cached requests. If the vector corresponding to the received request is sufficiently similar to a first vector of a first previously received, cached request, then a previously generated response corresponding to the first previously received, cached request may be accessed and transmitted to the user device. If a sufficiently similar vector is not identified, then at least a portion of the received request may be transmitted, in association with an identification of content items in a library, to an artificial intelligence learning engine. The response from the artificial intelligence learning engine may be transmitted to the user device.

An Applicant Assistant System may receive, from a user device, a user input indicating a query or task associated with information displayed in a user interface. The device may determine one or more components associated with the user interface and access component metadata of the determined one or more components, the component metadata for each of the one or more components indicating one or more of a fact, a data link, or an action. A prompt comprising at least some of the component metadata, at least some context information, and an indication of one or more available response elements may be provided to a large language model (LLM). The LLM may response with one or more response elements that may be processed by the System, such as to cause updates to the user interface on the user device.

Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and / or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).

A system and method for providing a digital financial advisor using fine-tuned large language models is disclosed. The system includes a data advisor application, data fusion suite advisor engine, human advising engine, a knowledge base and large language model (LLM) fine-tuning engine to create specialized language models (SLMs) that mimic specific human financial advisors. Multiple digital avatars embodying the appearance and communication style of human advisors are generated. The system processes user and client profile data, along with advisor-specific information, to provide personalized financial advice. It handles client queries, escalating complex issues to human advisors when necessary. The digital advisor system communicates with profile datastores, user devices, and external data sources for comprehensive financial analysis. Continuous learning capabilities allow the system to improve its performance based on interactions and feedback, combining AI efficiency with personalized human-like advisory.

Methods, systems, and devices may assist in artificial intelligence (AI) or machine learning (ML) communications in 5G system, AI or ML traffic differentiation, AI or ML slice type, AI or ML triggering rules, AI or ML policy, AI or ML operations, or user equipment communication interface exposure.

Disclosed are systems and methods for identifying threat events in an enterprise network and managing detection rules and responses to the events. A threat intelligence computer system can receive information about a detected threat event including a phase of attack and a detected domain of the threat event, apply at least one tag to the detected event that associates the event with at least one of the rules triggered in response to detecting the event, evaluate the tagged rules against the information, flag the event as having an improvement opportunity, determine whether the rule tagged to the event is a candidate for improvement, generate, based on the determination, instructions for improving the rule, generate a prioritization scheme indicating an order to address the instructions to improve the rule amongst instructions to improve various threat detection rules, and generate and return output indicating the prioritization scheme for presentation at user devices.

The present disclosure is directed to enabling asynchronous analytics via an intercept device. The intercept device may intercept a request transmitted by a user device to a host server, the request indicating data of interest to be provided by the host server in a response; transmit the request to the host server; asynchronously extract analytic data from the request; transmit the analytic data to an analytics server for performance of analytics on the analytic data; receive, from the analytics server, a result of performance of the analytics on the analytic data; determine, based at least in part on analyzing the result, whether the response from the host server is to be modified; selectively effect modification of the response based at least in part on determining whether the response from the host server is to be modified; and transmit the response to the user device. Various other aspects are contemplated.

Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and / or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.

Disclosed in some examples are methods, systems, devices, and machine-readable medium for providing a consumer collections and servicing analytics platform and application to simplify data analyst search and analysis of data from disparate data sources. The platform and application ingest data into a centralized data repository, from a plurality of disparate data sources. The application transforms the ingested data into standardized data using one or more standardized formats and applies custom processing algorithms and data enrichment techniques to the standardized data to generate enriched data. The application automates manual data collection and reporting processes using the enriched data and causes a user interface to be displayed to the user device, the user interface including self-service access to the enriched data in the centralized data repository.

A method includes receiving a natural language query from a user that solicits a response from an assistant large language model (LLM), receiving a prompt composition including an instruction parameter that specifies a task for the assistant LLM to respond to user queries concisely, structuring a conciseness prompt by concatenating the prompt composition to the natural language query, and processing, using the assistant LLM, the conciseness prompt to generate a concise response to the natural language query. The method also includes providing, for output from a user device, the concise response to the natural language query.

Implementations relate to a method implemented by one or more processors, the method including: receiving an input prompt for a large language model (LLM); generating first LLM output that is usable to generate a set of user interface (UI) each associated with a corresponding sub-prompt of the input prompt; causing, based on the first LLM output, the set of UI elements to be rendered at a user device; receiving further user input based on user interactions with one or more of the UI elements of the set of UI elements; and in response to determining that one or more termination conditions are satisfied: generating a final response to the input prompt based on generating second LLM output that is usable to generate the final response; and causing the final response to be rendered at the user device.

A payment service system receives contextual information regarding an interaction between the payment service and a user device associated with a user. A propensity metric for the user is determined based at least in part on inputting the contextual information into a machine learning (ML) model. Based on the propensity metric, a user interface is dynamically configured to comprise user interface elements arranged in a layout personalized for the user, where a user interface element represents content particular to a service offered by the payment service. Based on receiving an interaction with the user interface element, a booklet is launched corresponding to the service with which the user interface element is associated.

Methods and systems are disclosed for analyzing media content in response to a trigger, identifying relevant objects displayed within the media content, and generating linkable interactive user elements related to the relevant object that are presented as part of an overlay image for display on a user device. An image and a media content item are accessed and analyzed, interactive user elements are generated and used in an overlay image allowing selection of interactive user elements as a hyperlink to relevant content.

The invention discloses a self-service printing method based on an Internet of Things cloud platform, and the method comprises the following steps: user identity authentication and dynamic authorization: enabling a user to use a dynamic two-dimensional code or a one-time password to complete identity authentication and printing authority distribution through the Internet of Things cloud platform; lightweight cloudization of the printing task: user equipment fragments and compresses a file, and only uploads an encryption task description containing printing parameters to a cloud platform; cloud platform task scheduling: the cloud platform detects the state of the Internet of Things printing device in real time, and distributes the printing task to the optimal device according to the device load and the geographic position; distributed data transmission and edge analysis: the cloud platform transmits task fragments to an edge node through an Internet of Things protocol, and the edge device completes file recombination and analysis; by introducing a zero-knowledge encryption technology, edge calculation and dynamic task scheduling, the problems of insufficient data privacy protection, low efficiency, poor security, resource waste and the like in the prior art are solved.

The invention provides a virtual human design and application platform, method and device based on artificial intelligence, and relates to the technical field of virtual digital humans. The method comprises the steps of performing local anonymization on multi-modal input data on user equipment, encoding generated anonymized multi-modal features to obtain a multi-modal feature vector, and inputting the multi-modal feature vector into an emotion calculation model to obtain a user emotion intensity quantized value; inputting the multi-modal feature vector into a context sensing model, and generating a user intention vector after context correction in combination with a knowledge graph; generating an updated personality parameter matrix according to the user emotion intensity quantized value and the user intention vector; and outputting voice waveform data, facial muscle motion parameters and skeleton joint coordinate data based on the personality parameter matrix, and driving the virtual digital human three-dimensional model to perform real-time rendering. According to the scheme, the naturalness, emotional resonance and long-term user retention rate of virtual digital human interaction can be improved, and user privacy data security is protected.