44 results about "Interaction history" patented technology

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors analyze user prompts using one or more natural language understanding techniques. One or more processors then enrich the user prompts by integrating contextual data from user interaction history and adapt the enriched user prompts to align with characteristics of Large Language Models (LLMs) and Application Programming Interfaces (API) requirements of the LLMs.

Techniques for ASR processing using language model (LM)-generated context are described. A LM is prompted to generate words that are relevant for / may be included in a future user input. The prompt to the LM can include words from user interaction history, dialog history, dialog topic, user preferences, etc. The information included in the prompt may focus on rare or unique words rather than words that the ASR model is already confident in recognizing. The techniques can be plugged into an existing / pretrained ASR model and can be used with any existing / pretrained LM, thus saving resources needed to implement and maintain the components.

The invention discloses a psychological health analysis method and apparatus, an electronic device and a storage medium, through the application, dynamic psychological assessment is generated through multi-modal data fusion analysis, and personalized scenarized dynamic interaction content is generated based on an assessment result and an interaction history, so that the psychological health analysis efficiency is improved. Meanwhile, continuous learning and correlation analysis are carried out on the psychological state of the user by utilizing a multi-dimensional memory architecture, so that the system can understand the change of the user state and adjust an interaction strategy like a human consultant, and therefore, the technical problems of insufficient emotional distraction and credibility of the user due to the adoption of a standardized reply mode in the prior art can be solved, and the user experience is improved. The technical effects of enhancing the emotion affinity of the system, establishing a continuous and credible interaction relationship and improving the durability of the psychological intervention effect are achieved.

A system and method for generating personalized wellness recommendations in real time, based on a user's emotional state, contextual situation, and interaction history. The system leverages a large language model (LLM) integrated with an emotion ontology based on the Wheel of Emotion to analyze both user-submitted input and historical behavior. In some embodiments, the system extracts emotional and situational cues from natural language input and recommends wellness practices that match specified preferences including emotional impact, guidance level, and duration. A fallback prioritization logic is applied to ensure relevant results even when exact matches are unavailable. The system may also generate a compassionate message aligned with the user's inferred emotional state and return structured output. In automated embodiments, the system selects a weighted mix of familiar and exploratory content. The architecture includes preference drift tracking, confidence-based filtering, and memory-optimized re-ranking to improve emotional targeting and user engagement.

We provide the system. [Solution] A data collection method for collecting customer interaction history, A data analysis means that analyzes the aforementioned dialogue history using natural language processing technology to extract customer interests and needs, A proposal generation means that generates personalized follow-up proposals based on the extracted customer interests and needs, A notification means for notifying the terminal of the generated follow-up proposal, A follow-up execution means that executes a follow-up proposal using the aforementioned terminal and records the results, A system including a learning means to improve the proposal generation means using the execution results.

In one embodiment, a method includes detecting activity of one or more agents that modifies a processing state of a first of multiple invoices, each invoice requesting approval. The activity is conducted by the one or more agents through interacting with the procurement processing system. The method includes updating, based on the detected activity, an invoice interaction history record associated with the first invoice in an invoice interaction history datastore communicatively coupled to the procurement processing system. The method includes generating one or more metrics regarding activity of the agents affecting a processing state of the invoices based on the updated invoice interaction history datastore. The method includes providing the one or more metrics for presentation to the procurement processing system.

The application provides a frozen base model personalized reply method and device based on dynamic activation guidance, acquires current user query information and interaction history state information; inputs the current user query information and the interaction history state information into a bypass network for personalized dynamic activation guidance, obtains a dynamic guidance vector output by the bypass network; inputs the current user query information and the dynamic guidance vector into a frozen base model, and obtains a personalized reply corresponding to the current user query information output by the frozen base model. The technical scheme of the application is based on the collaborative reasoning architecture of the lightweight bypass network and the frozen base model, inputs the current user query information and the dynamic guidance vector obtained by the bypass network into the frozen base model, injects an activation state based on the frozen base model, obtains the personalized reply corresponding to the current user query information, and improves the accuracy of the personalized reply.

The invention is applicable to the technical field of interactive video processing and information updating scheduling, and provides a real-time interactive enhancement system for electronic publications, which comprises a data acquisition module used for acquiring a user interactive historical record of a target unmanned vending machine and an initial updating period value of a target interactive video. Aiming at the interactive video updating problem in the unmanned vending scene, the method does not depend on an updating mode purely based on a commodity data change period in the prior art, but introduces user interaction feedback as a core decision basis. A correction factor is dynamically generated to adjust an initial updating period value by obtaining and analyzing user interaction historical records, recognizing wrong clicks, repeated switching of branch paths or interruption after interaction steps are not completed, and combining the occurrence frequency in a newest preset time window, so that the updating rhythm can truly reflect understanding and trust requirements of users.

The present application relates to the field of group portrait, disclose a kind of user portrait generation method, device, computer equipment and storage medium, its method includes: obtaining user interaction history record;According to user interaction history record generates user intent data, user intent data includes each user's flow node and node intent, a flow node corresponds to a node intent;According to user intent data constructs g×c two-dimensional matrix, wherein, g is total number of users, c is total number of node intent;Two-dimensional matrix is decomposed into first matrix and second matrix by non-negative matrix decomposition algorithm, first matrix is g×p matrix, second matrix is p×c matrix, and p is self-defined dimension;According to first matrix and second matrix, construct a number of group user portraits.The user portrait generated by the present application is highly matched with the intelligent customer service system, and the reply satisfaction of the intelligent customer service system can be improved.

[Problem] To provide an information processing device that can carry out interaction by using information obtained by compressing interaction history. [Solution] An information processing device according to the present invention is connected to: an external device that uses a first graph document in which a document structure is graphed; and a large-scale language model that generates the first graph document. The information processing device is characterized by having a control means that creates a first prompt on the basis of information including the first graph document, transmits the first prompt to the large-scale language model, acquires, from the large-scale language model, the first graph document that was updated on the basis of the first prompt, and transmits the acquired first graph document to the external device.

The embodiment of the invention provides a data processing method and device, equipment and a readable medium. The method comprises the steps of determining attribute information of a user in response to a return behavior of the user for a specified function of an application, and the attribute information of the user is determined based on interaction historical data of the user. And based on the attribute information, determining interaction content corresponding to the attribute information. And displaying the interactive content on an interactive interface of the application. According to the scheme, the interaction content is determined based on the attribute information of the user to execute display, the interaction content can better fit the requirements and habits of the user, and therefore the interaction quality of interaction with the user can be improved.

The invention discloses a new problem scene-oriented multi-modal large model knowledge tracking method, which is characterized in that modal level fusion is realized by adopting a perception coordination module, vision, text and context input are aligned, and rich problem characterization is constructed; problem-level fusion is realized through a cognitive integration module, multi-modal clues and learner interaction history are dynamically integrated, and a potential knowledge state is deduced under the condition of not depending on a question ID; and through an adaptive generalization module, representation is dynamically generated for unseen problems based on semantic similarity, and zero sample migration is realized. Compared with the prior art, the method has the advantages that zero sample knowledge without problems is tracked, so that the MKT-LLM can truly realize zero sample generalization to open and real world education content, and the limitation that an existing knowledge tracking method cannot be generalized to a new problem is effectively solved; the method can be widely applied to an intelligent education system, a self-adaptive learning platform and an interactive tutoring system, and provides technical support for personalized education in an open learning environment.

The invention provides an intention-driven NPC behavior and dialogue generation method. The method comprises the following steps: receiving an NPC behavior and dialogue generation request to trigger an NPC behavior and dialogue generation process; determining the current action intention of the NPC by analyzing the internal state of the NPC, the external environment perception information and the interaction historical data; judging whether the next purpose of the NPC is to generate a behavior or a conversation according to the current action intention of the NPC; if the next purpose of the NPC is to generate a behavior, generating an action generation field of the NPC, and if the next purpose of the NPC is to generate a dialogue, generating a dialogue generation field of the NPC; matching the action generation field or the dialogue generation field with a preset action data list, and if an unmatched field exists, correcting the unmatched field to obtain a final action generation field or a final dialogue generation field; generating an action instruction according to the final action generation field, and generating dialogue content according to the final dialogue generation field; and repeating the above steps until the NPC stops interaction.

To provide methods, systems, and computer program products for detecting online contextual evolution of linguistic terms.SOLUTION: A computer-implemented method disclosed herein comprises: detecting an increase in use of a linguistic term at a messaging source; determining that a plurality of linguistic terms related to the linguistic term are disproportionally related to the increase in use of the linguistic term at the messaging source; identifying a possible evolution of the linguistic term based on user reactions to the linguistic term at the messaging source in consideration of an interaction history; and classifying one or more messages as including an evolved use of the linguistic term.SELECTED DRAWING: Figure 4

A computing system is provided, including processing circuitry configured to cause an interaction interface for a trained generative model to be presented, in which the interaction interface is configured to communicate a portion of a user interaction history. The processing circuitry is further configured to receive, via the interaction interface, an input for the trained generative model to generate an output. The processing circuitry is further configured to send a command to create, via the trained generative model or another trained generative model, a whiteboard based on the user interaction history and receive the created whiteboard. The processing circuitry is further configured to generate a prompt based on the whiteboard and the instruction from the user and provide the prompt to the trained generative model. The processing circuitry is further configured to receive a response from the trained generative model and output the response via the interaction interface.

A computing system (10) is provided that includes processing circuitry (14) configured to render an interactive interface (84) for a trained generative model (80), where the interactive interface (84) is configured to communicate a portion of a user interaction history (40). The processing circuitry (14) is further configured to receive an input (92) via the interactive interface (84) for the trained generative model (80) to generate an output (94). The processing circuitry (14) is further configured to send a command to create a whiteboard (60) based on the user interaction history (40) via the trained generative model (80) or another trained generative model (81), and to receive the created whiteboard (60). The processing circuitry (14) is further configured to generate prompts (70) based on the whiteboard (60) and instructions from the user, and provide the prompts (70) to the trained generative model (80). The processing circuitry (14) is further configured to receive a response from the trained generative model (80) and output (94) the response via the interactive interface (84).

We provide the system. [Solution] A means of saving the basic information entered by the user, A means for generating a virtual partner based on user selection using a generated artificial intelligence model, A means for generating individual responses based on the user's interaction history, A means of performing functions to support daily life in conjunction with external services, A means for users to interact with a virtual partner while on a mobile device and receive life support, A system that includes this.

A computing system is provided, including processing circuitry configured to cause an interaction interface for a trained generative model to be presented, in which the interaction interface is configured to communicate a portion of a user interaction history. The processing circuitry is further configured to receive, via the interaction interface, an input for the trained generative model to generate an output. The processing circuitry is further configured to send a command to create, via the trained generative model or another trained generative model, a whiteboard based on the user interaction history and receive the created whiteboard. The processing circuitry is further configured to generate a prompt based on the whiteboard and the instruction from the user and provide the prompt to the trained generative model. The processing circuitry is further configured to receive a response from the trained generative model and output the response via the interaction interface.

This system provides an educational assessment method that efficiently evaluates children's intellectual abilities and developmental status based on their interaction history with AI, and then provides feedback on the results to parents and educators. [Solution] The system comprises a storage means for saving dialogue history, a summary generation means for generating a summary of the dialogue history after a predetermined period of time has elapsed, an analysis means for integrating and analyzing new dialogue history with the saved summaries, and an evaluation generation means for generating an educational evaluation based on the analysis results. The analysis means performs the analysis based on rubrics related to language patterns, knowledge, and interests. Rubrics can be set by parents or teachers. This makes it possible to efficiently conduct long-term learning evaluations and realize multifaceted educational evaluations.

Computer system (GUID) and computer-implemented method for a guidance session comprising measures like receive a user's request (req), select a guidance root node (r1, r2, rn) based on the user's request (req) by accessing a graph data base, wherein the graph data base provides guidance options (o1, . . . on) with links (l), group (grp) in an iterative manner guidance options (o) by type (ot) and put them into a queue (qu), dequeue (dequ) each option group (og2 / og3) and check each option's relevance (rel) in the context, provide (show) the option (o) to the user (u) and provide a first guiding question, store user specific interactions history in the context memory and / or perform option-specific actions.

The present disclosure provides a dynamic cognitive path modeling and prediction method, system, electronic device and storage medium; the method comprises the following steps: collecting learning interface interaction behavior of a learner in real time to generate an original interaction sequence containing multiple time-stamped ordered event tuples; streaming analysis is performed according to a preset behavior analysis rule library, the event tuples are aggregated / converted into semantic micro behaviors with cognitive psychology meaning to obtain a semantic micro behavior sequence; the semantic micro behavior sequence is input into a sequence modeling network to generate a dynamic cognitive path vector encoding the current complete interaction history at each time step; based on the vector, downstream tasks are processed through a prediction head to output real-time intervention signals or cognitive diagnosis reports. The technical scheme of the present disclosure models the fine-grained behavior of the learner during the problem-solving process, realizes dynamic tracking and forward-looking prediction of the cognitive state of the learner, and realizes active and timely personalized intervention.

The invention provides a dynamic cognitive path modeling and predicting method and system, electronic equipment and a storage medium. The method comprises the steps that learning interface interaction behaviors of a learner are collected in real time, and an original interaction sequence containing a multi-timestamp ordered event tuple is generated; performing streaming analysis according to a preset behavior analysis rule base, and aggregating / converting the event tuples into semantic microscopic behaviors with cognitive psychological meanings to obtain a semantic microscopic behavior sequence; inputting the sequence into a sequence modeling network, and generating a dynamic cognitive path vector for coding the current complete interaction history at each time step; and based on the vector, downstream tasks are processed through the prediction head, and a real-time intervention signal or a cognitive diagnostic report is output. According to the technical scheme, modeling is carried out on the fine-grained behaviors in the problem solving process of the learner, dynamic tracking and foresight prediction of the cognitive state of the learner are achieved, and active and timely personalized intervention is achieved.

This invention relates to the field of smart terminal and health monitoring technology, and discloses an age-friendly companion interaction method, system, device, electronic device, and storage medium. The method includes: acquiring environmental data and user activity data; a first processing flow that, in response to environmental data or received user interaction instructions, executes a dynamic control environmental purification strategy and / or presents and updates age-friendly interactive content; a second processing flow that, in response to user activity data and pre-stored user interaction history, executes the generation and output of personalized emotional companionship content; and a third processing flow that, based on user activity data, identifies predefined abnormal behavior patterns; and in response to abnormal behavior patterns, executes tiered voice inquiry and safety warnings. This invention realizes the entire process from multi-source perception to integrated intelligent response, and can proactively and synchronously maintain environmental health, provide emotional interaction, and ensure personal safety within a closed loop.

The present application belongs to the field of digital human interaction management, and relates to data analysis technology, and is used for solving the problem that the prior art cannot monitor and analyze the multi-round interaction state of digital human, in particular to a digital human interaction management system based on big data analysis, which comprises a voice interaction support module, a multi-round dialogue supervision module and an effectiveness optimization analysis module connected in sequence; the voice interaction support module is used for recognizing the voice information received by the digital human and converting it into text information, inputting the text information into a general big model and calling corresponding output instructions, and sending the output instructions to an execution module; the present application can reasonably extend and supplement the answers according to the user's follow-up questions, and effectively remember the interaction history and intention information of the user in the multi-round dialogue, establish a dialogue context association model, and ensure that the platform can accurately understand the evolution and progression of the user's intention in the multi-round dialogue process.

The invention discloses a multi-modal music recommendation method and device based on a large model, equipment and a medium. The method comprises the following steps: acquiring music interaction representation information and a user interaction behavior; carrying out abstract extraction on the music interaction representation information to obtain a multi-modal representation abstract; time division is carried out on the user interaction behavior based on the multi-modal representation abstract, a plurality of interaction time slices are obtained, and the interaction time slices correspond to the multi-modal representation abstract; and performing user recommendation prediction on the multi-modal representation abstract corresponding to each interaction time slice through a preset large language model to obtain user recommendation music. According to the embodiment of the invention, the multi-modal representation abstract is extracted, the interaction history, the social dialogue and the user identity data are fused into the slice preference description within the time granularity, and the music matched with the user is predicted through the large model, so that the music recommendation precision is effectively improved, and the user experience is enhanced.

The application discloses a human-computer interaction method, device and system. After obtaining human-computer interaction history information and input information of a current round of a user, a large language model is called to generate executable instructions matched with the interaction demand of the current round of the user. The instructions contain the calling of plug-in resources dependent on the response to the interaction demand of the current round of the user. The executable instructions are parsed and executed to call the plug-in according to the indication of the executable instructions, obtain the execution result of the plug-in, call the large language model, and generate reply information based on the human-computer interaction history information, the input information of the current round of the user and the execution result of the plug-in. The application realizes human-computer interaction based on the large language model and plug-in resources, and can fully utilize the super semantic understanding and multi-round dialogue capability of the large language model. The executable instructions for calling the plug-in resources are generated through the large language model, without the need of pre-customizing various types of business calling codes, thereby saving the code development cost and reducing the workload.

A computing system (10) is provided, comprising at least one processor (14) configured to: receive a user interaction history (32) of a user; extracting memory (34) from the user interaction history (32); integrating the memory (34) into a memory cluster (44); causing a hint interface (48) for the trained generative model (56) to be presented; receiving, via a hint interface (48), an instruction (52) for the trained generative model (56) from the user to generate an output; generating a prompt (50) based on the memory cluster (44) and an instruction (52) from the user; providing a cue (50) to the trained generative model (56); receiving (58) a response (58) from the trained generative model (56) in response to (58) the cue (50); and outputting the response (58) to the user.