942 results about "User profile" patented technology

A system and method for real-time or near real-time multilingual language transformation in telecommunications environments using distributed artificial intelligence (Al). The system includes at least one processor configured to instantiate a plurality of Al agents, each corresponding to a user in a communication session, and to manage their operation via an Al-agent runtime adapter comprising a microkernel agent manager and a virtual machine layer. A language model abstraction layer enables access to language models and Al algorithm libraries through publish-subscribe interfaces. The system supports contextual transformation of speech based on user profile data such as sentiment, emotional tone, and cultural background. Execution can occur on telecommunication-class switches, edge devices, or general-purpose computing hardware. Al agents operate asynchronously, access shared or private memory, and produce personalized, bidirectional linguistic output. The architecture supports secure, scalable deployment across heterogeneous devices and networks.

A recommendation system is described, which identifies engaged fans for an artist and requests input from the engaged fans regarding a collaboration by the artist with at least one different artist. In implementations, engaged fans are identified as having user profiles on a media content platform that satisfy at least one threshold engagement criteria based on consumption of at least one media content item associated with the artist. The recommendation system presents a user interface that includes at least one prompt for feedback that enables engaged fans to recommend how the artist collaborate with others. In some implementations, the user interface includes controls that are selectable to define artist characteristics to feature in a collaboration and the recommendation system is configured to generate a synthesized collaboration by automatically combining different artists' characteristics using a trained machine learning model. Recommendations based on engaged fan feedback are then provided to artists.

An apparatus comprises a memory communicatively coupled to a processor. The memory is configured to store network interchange parameters and user profiles. The processor is configured to receive a data exchange request triggered by a user device. Further, the processor is configured to request, to a decentralized network, a token indicating that a user profile associated with the user device is entitled to access an entitlement, determine whether a data exchange network is available to exchange data in accordance with the network interchange parameters, identify a second data exchange network that is available in accordance with second network interchange parameters in response to determine that the data exchange network is unavailable, and request a data exchange output from the second data exchange network via the token. The processor is configured to receive the data exchange output from the second data exchange network.

Automatic detection and handling of security-related anomalies by utilizing machine learning and a large language model (LLM). A computerized method for detecting and handling security threats in an organizational network of an organization includes: (a) collecting event data that pertain to organizational users, organizational devices, and organizational resources of the organizational network; (b) constructing user profiles, device profiles, and resource profiles; (c) constructing Organizational Context information that pertains to organizational users, organizational devices, and organizational resources; (d) constructing a time-series of events, enriched with the Organizational Context information; (e) analyzing the time-series of events using a Machine Learning process that detects an anomalous event, and automatically generating an alert message pertaining to and describing the anomalous event.

Embodiments described herein relate to systems and methods for providing a recommendation panel for a graphical user interface of an issue tracking platform. The system and methods can include causing display of the recommendation panel in the issue-view graphical user interface. The recommendation panel can include a first section including a set of one or more selectable link objects, where each selectable link object associated with a respective content item identified for the request type; a second section including a link to a user profile of a subject matter expert user, where the subject matter expert user selected based on a subject matter determined using the issue data; and a third section including suggested action narrative. The suggested action narrative can be determined using a generative response received from the generative output engine in response to the prompt.

The invention relates to the technical field of artificial intelligence, in particular to a large model dialogue management method and device.The method comprises the steps that dialogue information of a user input dialogue initiated by a user to a large model is intercepted; obtaining user information corresponding to a user; filling the first cue word template based on dialogue information of a dialogue input by a user and user information to obtain a first cue word; sending the first cue word as first-round user input dialogue information to the large model, so that the large model generates large model output dialogue information for the first-round user input dialogue based on the first cue word; and in response to establishment of the update trigger condition, updating the user contour information based on the historical round dialogue of the current session and / or the dialogue content of the current round dialogue. According to the method, the structured user background information is provided for the large model at the beginning of the dialogue, so that the reply accuracy and pertinence of the large model are remarkably improved, and cross-session personalized interaction experience is realized.

Techniques for content generation using machine learning. A device may access textual content from a user device associated with a user profile, access content preferences associated with the user profile, and provide the textual content to a classification model to identify one or more topics of the textual content. The classification model may be trained to identify topics within text. The device may access, from a content repository, supplementary textual content associated with the one or more topics; form, based on the content preferences, an instruction prompt for a generative model; and provide the topics, the textual content, and the supplementary textual content to the generative model to obtain additional content. The device may identify, from the user profile, one or more additional user profiles having a relationship with the user profile; and provide the additional content to an external server.

A locking control system and method include providing a base plate with an attachment interface and attaching at least one accessory to the base plate via the attachment interface. A user profile is provided that comprises one or more classes of authorization. Locking and unlocking of the at least one accessory to the base plate is controlled based on the user profile and / or locking and unlocking of the at least one accessory itself is controlled based on the user profile.

The invention discloses a consumer finance marketing strategy reaching model. According to the model, user behaviors and service data are acquired in real time through a data acquisition system, a dynamic user portrait is constructed by adopting a multi-head self-attention mechanism of a Transform model, a user behavior sequence is coded into a 512-dimensional dynamic interest vector, and the 512-dimensional dynamic interest vector and a 128-dimensional static attribute vector are fused to generate a comprehensive portrait. And training a strategy network based on a PPO reinforcement learning algorithm, modeling a marketing decision into a Markov decision process, and outputting a personalized strategy including a product type, a reach channel, an incentive limit and a reach opportunity. The system collects user feedback through full-link data burying points, maintains experience playback pools with the capacity of 1 million, carries out incremental learning every 4 hours, and adopts an elastic weight consolidation technology to avoid disastrous forgetting. A new strategy effect is verified through an A / B test, and automatic hyper-parameter tuning is carried out through Bayesian optimization. Practical application shows that the marketing conversion rate of the model is improved from 2.3% to 3.1%, the ROI is improved from 1.5 to 2.1, and the complaint rate is reduced by 40%.

The invention discloses a man-machine cooperation method. The method comprises the following steps: acquiring user session information and user portrait data; based on the user session information, obtaining user intention recognition information through a preset intention recognition model; based on the user intention identification information, obtaining user request complexity through a preset semantic analysis model; based on the user intention identification information, obtaining an AI response capability score through a preset knowledge base; based on the user session information and the user portrait data, obtaining user request sensitivity through a preset text sentiment analysis model; and on the basis of the AI response capability score, the user request complexity and the user request sensitivity, a decision result about whether manual operation needs to be switched is obtained through joint hierarchical decision. The AI response capability, the user request complexity and the user request sensitivity are comprehensively evaluated, the limitation of single index decision making is overcome, and manual decision making is more scientific and accurate.

Aspects of the subject disclosure may include, for example, receiving first data from a communication device over a mobile network, and determining that the first data is associated with a first user profile associated with the communication device. Further embodiments can include determining that the first user profile is associated with a first group of mobile network resources, and transmitting the first data across the mobile network utilizing the first group of mobile network resources. Other embodiments are disclosed.

The invention provides a user portrait construction method and system, and relates to the technical field of data processing, by extracting dynamic time sequence feature vectors capable of simultaneously representing short-term behavior intentions and long-term behavior habits of a user, the user portrait construction method and system can more comprehensively and accurately describe the user, and capture instant intentions and long-term preferences of the user at the same time, so that the user portrait construction efficiency is improved. The precision and timeliness of the portrait can be improved; and fusion weight adjustment is performed on the user feature vectors, and weighted fusion is performed on each user feature vector based on the adjusted fusion weight, so that when the user behavior suddenly changes, fusion weight adjustment can be performed in a targeted manner, the portrait failure risk caused by the user behavior suddenly changes is reduced, and the user experience is improved. The scene adaptability of the portrait can be improved, and the robustness of the scheme is enhanced.

A speech-processing system may be configured to provide certain endpoints (e.g., skills and / or applications) with data reflecting an explicitly selected user profile. When handling requests from a device configured to support a session context (e.g., a personal device), the skill may handle multiple requests in an interaction (e.g., a “session”) using the selected profile as the session context, even if the system recognizes a different user for one or more of the requests. Thus, if the system recognizes more than one user using a personal device during an interaction with the skill (e.g., playing a game or requesting songs), the skill may handle each request of the interaction according to the selected profile. For a device not configured to support a session person, however, (e.g., a communal device) the skill may handle each request according to the recognized profile regardless of an explicitly selected profile.

A first multi-party computation (MPC) system of an MPC cluster can receive, from an application on a client device, an inference request comprising a first share of a given user profile for a user of the application and a performance threshold. A set of nearest neighbors to the user profile can be identified by performing a secure MPC process using a trained machine learning model in collaboration with one or more second MPC systems. One or more nearest neighbors having a performance measure that satisfies the performance threshold can be selected from the set of nearest neighbors. The first MPC system can transmit data derived from the one or more nearest neighbors to the application.

An online software platform (OSP) may store custom digital rules used for producing resources for relationship instances of certain primary entities with other entities. A custom rule may be created based on input from the primary entity that is intended to override a corresponding one of other stored digital rules (i.e., resource digital rules). The OSP automatically alerts the primary entity within a user interface or via other communications in response to the determination that an underlying change in data used to process primary entity data would affect a result of application of the custom digital rule, thus enabling such changes to be efficiently addressed and prevent incorrect results being output.

The embodiment of the invention provides an intelligent reply method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining a target question initiated by a target object, and obtaining user behavior data, user portrait data and current dialogue data of the target object; performing emergency degree evaluation on the target question according to the current dialogue data to obtain a reply emergency degree; performing priority evaluation on the target problem according to the user portrait data and the user behavior data to obtain a problem priority; determining reply demand prediction information according to the question category, the reply emergency degree, the question priority and the current dialogue data; screening out a target customer service system from preset candidate customer service systems according to the question complexity of the target question and the reply demand prediction information; and replying the target question through the target customer service system. The method can be applied to business systems needing a large amount of data, such as financial science and technology and health medical treatment, and can improve reply intelligence, accuracy and user experience.

Techniques for streaming generative machine learned model (or LLM) output to a virtual space are described herein. A system may receive a request to perform an action. The system may leverage LLMs to assist in performing aspects of the requested action. The system can generate input data to input into the LLM. When generating the input data, the system can identify sensitive data associated with the request. The system can modify the input data to mask and / or anonymize the sensitive data. The system can input the input data into an LLM trained to output a subset (e.g., less than all) of the response at a time. The system can add the output subset to a buffer and upon identifying the masked data in the buffer, the system can demask the sensitive data and output the sensitive data to the user profile in a streaming manner.

Provided is a process, comprising receiving, with a computer system, a natural language request that relates to a travel itinerary; obtaining, with the computer system, a user profile and one or more sets of constraints corresponding to the natural language request; determining, with the computer system, a travel itinerary template, based on the user profile and the one or more sets of constraints; populating, with the computer system, the travel itinerary template with a plurality of travel components based on the user profile and the one or more set of constraints; and presenting, with the computer system, at least some candidate results, the candidate results stored in memory.

An embodiment includes receiving, from a device, at an authentication service, a dynamic user identifier having a one-time password in an authentication message constructed to carry the dynamic user identifier in place of a pre-determined user identifier of a user. The embodiment locates in a profiles database, using a customized search query with a code based on the dynamic user identifier, a user profile. The embodiment receives at the authentication service, a secondary identification data of the user including a biometric information of the user. The embodiment validates the biometric information using the user profile and enables, when the validating is successful, the device to perform an operation. The enabling is not based on the authentication service validating an entirely static user identifier and is not based on the authentication service validating a manually typed password.

The invention relates to the technical field of management systems, and particularly discloses a three-dimensional industrial design material AI intelligent recommendation management system. Comprising a dynamic knowledge graph module, a multi-source perception input module, a neural symbol fusion engine, a situation self-adaptive output module, a self-evolution optimization module, a recommendation rule updating module, a cold start processing module and a multi-target decision module. The neural symbol fusion engine realizes symbol logical reasoning through Transform, the cold start processing module generates a user portrait through gradient embedding and generates virtual user features through cross-map feature propagation, the situation adaptive output module dynamically switches rendering modes according to a design stage, and the self-evolution optimization module performs meta-learning and concept drift detection; through multi-technology fusion and module cooperation, intelligence, precision and high efficiency of three-dimensional industrial design material recommendation are realized, the design efficiency is remarkably improved, and the project landing risk is reduced.

A system and method are disclosed for predicting recommendations for a user interface. The method includes generating a graphical user interface that receives a conversation-based input from at least one user of a client portal, receiving a user profile and a navigation history for the user, providing the conversation-based input from the user to a natural language processing engine, decoding using the natural language processing engine an intent and slots from the conversation-based input; and generating one or more recommendations based, at least in part, on the intent, the slots, the user profile, and the navigation history.

An apparatus for the generation of a medical report is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a user query. The memory instructs the processor to receive a user profile comprising a plurality of medical tests. The memory instructs the processor to generate testing data as a function of the plurality of medical tests using an encoder. The memory instructs the processor to generate textual data that is representative of the testing data using a querying transformer model (Q-former). The memory instructs the processor to generate a medical report as a function of the user query and the textual data using a report large language model (LLM).

A system and method for personalizing health information content provided to a user. The techniques include obtaining user profile data associated with the user in response to a query submitted by the user. The user profile data includes health data of the user and application use data by the user. The techniques further include expanding the query into a plurality of queries based on the health data of the user, performing semantic searches for the plurality of queries, ranking results from the performed semantic searches, aggregating the results from the performed semantic searches based on the ranking to generate an aggregated health information response, and providing the aggregated health information response to the user.

Various disclosed embodiments are directed to simulating a campaign journey, including simulating user responses at multiple touchpoints of the campaign journey. In other words, particular embodiments simulate how individuals from defined segments respond to and engage with various touchpoints of a campaign journey, which provides insights into their response behaviors and potential outcomes at each touchpoint. Based at least in part on a synthetic user profile, some embodiments simulate the campaign journey, such as simulating whether users respond to multiple touchpoints, where the prediction of a particular response at one touchpoint affects or influences the prediction of another subsequent response of a corresponding touchpoint in the campaign journey.

Method, systems and computer programs for content provision are provided. A requestor node generates an anonymized and compressed representation of the user profile using a user data embedding machine-learning model inputting user data of a user profile. The content retrieval platform receives the anonymized and compressed representation of the user profile and a content retrieval request and inputs the anonymized and compressed representation of the user profile and the content retrieval request to a content determination machine-learning model to determine content in response to the content retrieval request.

The invention relates to the technical field of AI interaction, in particular to a user portrait construction and personalized interaction system based on an AI dynamic prompt project, which comprises a scene analysis module used for recognizing an interaction scene and a core optimization target in the scene according to user input; the co-processing module is used for carrying out quantitative evaluation and sorting on the long-term features and the real-time features according to respective stability parameters of the long-term features and the real-time features and correlation parameters of the long-term features and the real-time features and the core optimization target, and generating a hierarchical feature set; the instruction generation module is used for adapting an instruction template according to the interaction scene and fusing the hierarchical feature set into the instruction template to generate a structured driving instruction; and the interaction execution module is used for executing the driving instruction to generate and output final personalized interaction content. According to the method, accurate balance of long and short term features of the user in personalized prompt generation can be realized through a scenarized dynamic weight distribution and feature conflict coordination mechanism.

In various embodiments, machine learning-based custom content generation for video streaming content systems and applications is provided. One or more of the embodiments described herein, among other things, provide for a machine learning / generative artificial intelligence (GAI)-based custom video generator model that may be used to synthesize targeted video streaming content (e.g., in real-time), based at least in part on user profile data from a user profile associated with a target user, and content data comprising an indication of one or more video content objectives directed at the target user. In some embodiments, a custom video content generator as disclosed herein may produce one or more prompts based on a set of content data and the user profile data, and apply the prompts to the video generator model to generate targeted video data that may be streamed to user equipment for presentation to the target user.

A method includes: providing identification information for a provider of supplemental content; retrieving information on the provider of supplemental content from the internet using the identification information to generate a content source profile; retrieving a user profile about a user; computing, with a user mindset prediction language model configured to determine a user's mindset while reviewing content, the user's mindset based on primary content presented to the user; selecting, by a supplemental source selection language model configured to select supplemental sources, the provider of supplemental content from a plurality of supplemental sources, based on the primary content; and generating in real-time, with a supplemental content generation language model configured to generate supplemental content, one or more pieces of supplemental content for the provider of supplemental content at one or more locations that are contextually consistent with the primary content.

Features described herein generally relate to providing explainable artificial intelligence (XAI) within extended reality. Particularly, after obtaining input data, including environmental information, user state information, and user profile information, an extended reality system determines virtual content to be presented to a user via a head-mounted device. The extended reality system also identified additional contextual factors associated with this virtual content, including system goals, user goals, a current state of the user, a user's location, a current time, and a current environment of the user. These additional contextual details are then used to determine XAI content that describes one or more aspects of the virtual content (such as how the virtual content was determined based on the input data). The virtual content and the XAI content are then presented to the user by the extended reality system.

The present invention relates to a digital key-based user profile providing vehicle comprising: a control unit; a BLE module communicating with a BLE module of a plurality of user terminals; a UWB module communicating with the UWB module of the plurality of user terminals; an NFC module communicating with an NFC module of the plurality of user terminals, wherein the control unit controls a BLE module of a specific user terminal and a UWB module of the specific user terminal to be connected to the BLE module and the UWB module if a digital key of the specific user terminal among the plurality of user terminals is authenticated, and controls the vehicle to activate only an account of the specific user terminal and release only security of a user profile related to the activated account of the specific user terminal if the BLE module of the specific user terminal and the UWB module of the specific user terminal are connected to the BLE module and the UWB module.