442 results about "Dialog system" patented technology

The invention discloses a personalized care method, device and equipment based on an AI technology and a storage medium, and solves the technical problem that an artificial intelligence dialogue system lacks dynamic memory, multi-dimensional user portrait updating and personalized care strategies in the prior art. The method comprises the steps of collecting data and preprocessing the data; the method comprises the following steps: generating a dynamic user feature vector by constructing an LSTM-GRU hybrid neural network structure; dynamic evolution of a user portrait is realized through incremental learning and memory attenuation, and a user multi-dimensional portrait is constructed; constructing a memory storage hierarchical architecture; extracting and optimizing keywords based on a medium-term memory storage system, and optimizing and constructing an interest graph by utilizing a hierarchical clustering method according to a keyword weight enhancement formula; based on a memory storage hierarchical architecture, establishing a causal relationship and a sequential relationship between entity nodes and emotion nodes to construct a memory association knowledge graph; and establishing a trigger mechanism, performing emotion memory composite analysis based on the memory association knowledge graph, and generating a care strategy. The method can be widely applied to the artificial intelligence technology field.

An LLM is adapted to generate database queries that are compatible with a proprietary database of a security provider. Adapting the LLM includes evaluating performance of the LLM after initial prompt engineering / fine-tuning to ensure that generated database queries are valid (i.e., comport to the database schema and can be executed to return results). When the LLM performance is satisfactory, a dialogue system uses the LLM to generate database queries from user queries. The dialogue system determines intent of each user query, which informs whether the query is supported. Supported user queries are converted to database queries using the LLM and submitted to the database. The dialogue system leverages another language model to generate a summarized, natural language representation of the database query results and constructs a response from the summary. The dialogue system also checks for XSS and prompt injection before database queries are ultimately submitted to the database.

In various examples, systems and methods are disclosed relating to language model jailbreak detection using length-perplexity metrics. A system can identify a prompt for a language model—such as an LLM, VLM, etc.—and generate a perplexity score for the prompt. The system can determine, based at least on the perplexity score and a length of the prompt, that the prompt is indicative of a jailbreak attempt for the large language model. The system can restrict the prompt from input to the large language model—or block an output generated based on the prompt from being shared—responsive to determining that the prompt is indicative of the jailbreak attempt.

An intelligent task-type dialogue method based on a large language model, comprising: acquiring a dialogue with a user; identifying and parsing input information of the user to obtain an identification and parsing result; on the basis of the identification and parsing result, synchronously updating the state of a dialogue state tracker; on the basis of the intelligent guidance of the tracker, entering a proper dialogue scene node; executing a preset Action to complete a specific task; and on the basis of the current dialogue node context and dialogue historical data, generating a feedback message of a robot. In the method, by combining the large language model, rapid adaptation and efficient configuration of a scene can be achieved, and by dynamically loading the configuration information of a specific scene, a round of dialogue process can be efficiently pushed on the basis of preset process logic. Also disclosed are an intelligent task-type dialogue system based on a large language model, an electronic device, and a computer program product.

The invention relates to the technical field of artificial intelligence dialogue systems, in particular to an intention correction and optimization method and system for multi-round dialogue context memory. According to the method, joint semantic coding is carried out on user input and historical dialogues, key semantic elements are extracted to construct an intention evolution relation graph, context consistency verification is carried out on an initial intention recognition result, and an intention correction candidate set is generated when conflicts are detected; and dynamically adjusting the context coding weight of the historical dialogue based on the corrected intention recognition result. According to the method, the intention recognition accuracy and context coherence in multiple rounds of conversations are effectively improved, and the semantic migration risk is reduced.

The invention provides a dynamic tool integration method and system for an intention-driven AI dialogue system, and the method comprises the steps: S5, extracting resource occupation information from a transaction log according to a state persistence record, carrying out the resource release and temporary context cleaning of each heterogeneous component, and obtaining a released resource list; s6, searching a breakpoint state snapshot and a transaction log from the distributed database according to the released resource list, and reconstructing a tool chain execution flow historical state to obtain a fault context analysis result; and S8, according to the context recovery state and the semantic feature vector, updating a tool chain execution stream, and reallocating computing resources through a heterogeneous component coordination mechanism to obtain a task continuity confirmation result. According to the method, the recovery efficiency and the resource utilization rate of the tool chain in an abnormal scene are remarkably improved, and the task continuity and the system robustness in a high-concurrency environment are guaranteed.

A method, apparatus and system configured to provide automated dialog engagement through the use of at least one dialog playbook to guide a dialog conversation between an automated dialog engagement system and an automated dialog engagement system user. The automated dialog engagement system utilizes a large language model (LLM) in conjunction with the at least one dialog playbook to guide the dialog conversation. The LLM provides responses to the automated dialog system user when a response is not available from the at least one playbook.

The invention discloses a cascade-based multi-modal digital human real-time dialogue system and method, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize efficient, multi-modal and customizable digital human real-time dialogue. Comprising a voice recognition module used for converting user voice into text information through an industrial-grade voice recognition toolkit; the large voice model module is used for generating dialogue reply information according to the text output by the voice recognition module; the text-to-voice module is used for converting the reply information output by the large voice model module into voice information; the speaker generation module is used for generating a digital human speaking video through an accurate lip shape synchronization technology based on the voice information; and the front and back end interaction module is used for realizing streaming transmission of videos and user interaction.

The invention discloses a 3D point cloud large model dialogue security protection system and method based on reinforcement learning and a protection layer, a reinforcement learning security alignment module takes a GPT4Point framework as a base, constructs a point cloud-language joint embedding space, extracts global semantic features by using a multi-layer Transform architecture, realizes efficient alignment of 3D point cloud and text instructions, and improves the security of the 3D point cloud large model dialogue. According to the multi-modal protection layer architecture, lightweight Lama Guard serves as a core model, and cross-modal risk interception is achieved by analyzing geometric features of text entities and associated point clouds generated by a target model in real time. According to the invention, through fusion of reinforcement learning security alignment, a large model protection layer and a GPT4Point multi-mode framework, a multi-level security protection system oriented to a 3D point cloud large model dialogue system is constructed. According to the collaborative design of a reinforcement learning dynamic optimization strategy and a Lama Guard filtering mechanism, the robustness of the model under attack resistance and the adaptability of the model in a natural distortion scene are remarkably improved, and the unified architecture of GPT4Point provides efficient support for point cloud-language understanding and generation.

The invention relates to the field of medical information processing, and discloses a reinforcement learning and cooperation system based on Internet hospital medical dialogues and medical records, and the system comprises a complete dialogue chain obtaining module which is used for obtaining a complete dialogue chain of patient input-large model reply-doctor modification in each round; the feedback format construction module is used for constructing a structured feedback format based on the complete dialogue chain to serve as a training sample of reinforcement learning, and the structured feedback format comprises input content, original reply, doctor editing content, editing behavior labels and semantic difference; and the reinforcement learning optimization module is used for introducing a reinforcement learning algorithm PPO on the basis of the existing supervised fine tuning model, and executing reinforcement learning optimization oriented to the medical dialogue system based on the training sample. According to the method, the problems that in an existing medical inquiry dialogue system, the response lacks reasoning transparency, the model cannot be continuously optimized, doctor feedback is not utilized, and the system request pressure is uncontrollable can be solved.

The invention relates to the technical field of government affair entrepreneurship guidance service intelligent dialogue systems, in particular to a dialogue intention recognition and correction method based on a knowledge graph, and the method comprises the steps: obtaining a voice signal and text information inputted by a user, and carrying out the cross-modal fusion processing to generate a semantic vector; carrying out evolution modeling on the dialogue state by adopting a graph neural network, and outputting a dynamic dialogue state graph containing a time sequence relationship between a user entity and a business object; taking the semantic vector and the focus information as query conditions, executing dynamic subgraph sampling from the government affair knowledge graph, and generating an intention correction strategy vector; and finally, utilizing reinforcement learning to generate personalized guide statements and outputting the guide statements to the user. According to the method, through deep coupling of semantic understanding, state tracking, knowledge reasoning and decision optimization, real-time perception and active completion of dynamic updating of hidden nodes and policies of the government affair process are achieved, and the intention recognition accuracy and systematic adaptive capacity are effectively improved.

The invention relates to the technical field of intelligent customer service, and discloses an intelligent customer service automatic answering method and system. The method comprises the following steps: constructing a dialogue semantic vector according to a question input by a user and a historical dialogue context; calculating an activation probability corresponding to each field sub-expert model based on the dialogue semantic vector; determining a first core expert model and a second core expert model by using the activation probability, and calculating a corresponding first core expert weight and a corresponding second core expert weight; according to the first core expert weight and the second core expert weight, utilizing a first core expert model and a second core expert model to carry out collaborative reasoning on the dialogue semantic vector to obtain a fusion feature vector; according to the method, the continuity and semantic comprehension accuracy of multiple rounds of dialogues are improved, the expert scheduling accuracy and efficiency are improved, and then the accuracy and integrity of a dialogue system in problem processing in the professional field are improved.

The invention relates to the technical field of natural language processing, and discloses a natural language processing-based customer service intention recognition method, which comprises the following steps of: firstly, acquiring user text information from a customer service dialogue system, and performing preprocessing such as word segmentation, stop word removal and word form reduction to obtain a standardized text; then calculating semantic features, extracting intention frequency and change trend, judging an intention stage, and dynamically adjusting an intention recognition confidence threshold; adjusting intention classification model parameters through a classification control algorithm, and feeding back optimized word segmentation algorithm parameters according to accuracy; and finally, optimizing a control signal output time sequence, dynamically updating preset parameters, and realizing accurate identification. The system comprises a text processing module, an intention calculation module, a classification control module, an intention adjustment module and a dynamic optimization module. The method and system can improve the accuracy and efficiency of customer service intention recognition, adapt to intention state changes of different users, and have good application prospects.

The invention discloses an accompanying dialogue system and method based on real-time environment perception and knowledge graph enhancement. The system comprises a multi-mode real-time environment perception module, a knowledge graph construction and enhancement module, a natural language understanding and dialogue management module, a personalized recommendation and narration generation module and an immersive multi-mode interaction module. The method comprises the steps of multi-modal real-time environment perception and situation data generation; performing dynamic association, query and enhanced reasoning on the knowledge graph; natural language understanding and dialogue management, personalized recommendation and narrative generation, and immersive multi-modal interaction presentation and feedback reception. According to the method and the system, the concern point of the user, namely scenery or details, can be accurately positioned, and context information required by subsequent service intelligence is provided, so that the problems of poor environment perception ability, weak interaction immersion, dull knowledge service, lack of individuation, insufficient intelligent accompanying experience and the like in the existing tourism auxiliary technology are solved.

The invention provides a retrieval enhancement generation method and system in the dual-carbon field, and relates to the field of data processing. According to the method, multi-source unstructured data in the dual-carbon field is collected, after data preprocessing is carried out, a multi-granularity query problem set is formed, a dual-carbon field knowledge base is obtained, and a dual-carbon field-oriented embedding model CEMBING and a reordering model CReranker are constructed. The CEMBING model adopts a semantic partitioning method and a joint training strategy, so that semantic information of a dual-carbon field text can be effectively captured; the CReranker model adopts a negative example mining strategy and a triple loss function, candidate documents can be accurately sorted, the problems of knowledge limitation and insufficient timeliness of LLMs in the application of the dialogue system in the dual-carbon field are effectively solved, and the accuracy and efficiency of retrieval enhancement generation of the dialogue system in the dual-carbon field are improved.

To solve the problem that it is general to generate a response on the basis of single input and an ability for continuously tracking emotions and intentions of a user is limited, and thus, it is difficult to maintain a natural dialog in a conventional AI dialog system.SOLUTION: The present invention applies a technique named dead-reckoning for estimating a current position from a position and speed in the past in navigation and aviation to a dialog with AI, extracts important information from dialog data in the past of a user, and estimates current needs and emotions. Thus, the intentions of the user are understood, directivity of the dialog is defined, the emotions and degrees of interest are grasped as speed and force of the dialog, a psychological state of the user and context of the dialog are more accurately analyzed, and a response adaptable to the analyzed psychological state and context are generated. For that purpose, the emotions of the user are estimated, and directivity of the dialog is analyzed to generate a personalized response by a response generation unit to be constituted of a user emotion analysis unit, a dynamic dialog navigation analysis unit, a personalized response generation unit, and a learning and evolution unit.SELECTED DRAWING: Figure 3

The invention provides an intelligent text dialogue generation method and device based on artificial intelligence, and the method comprises the steps: carrying out the text semantic analysis of a question dialogue text inputted by a target user, and obtaining the text semantic information, user emotion information and business field information of the question dialogue text; performing matching in a preset business model library based on the business field information to obtain a target business model; inputting the text semantic information into a target business model to obtain an initial reply dialogue text output by the target business model; performing emotion adjustment on the initial reply dialogue text based on the user emotion information to obtain an intermediate reply dialogue text matched with the emotion tendency of the target user; and performing personalized optimization on the intermediate reply dialogue text based on the current dialogue situation and the user portrait of the target user to obtain a target reply dialogue text, and feeding back the target reply dialogue text to the target user. According to the method, the fluency, universality and adaptability of the dialogue system are improved, and the stickiness of a user to the dialogue system is improved.

A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention.

The invention discloses a front-end cache management method, system and device for a lightweight large-model dialogue state and a medium, belongs to the technical field of front-end cache management of a large-model dialogue system, and aims at solving the technical problem of how to overcome the defects that in a traditional scheme, long context cache is low in efficiency, storage redundancy and insufficient in dynamic semantic adaptation capacity, and the large-model dialogue state cannot be managed easily. In order to realize dialogue context volume compression, improve semantic similar request hit rate and reduce cross-end synchronization delay, the adopted technical scheme is as follows: data acquisition and preprocessing: capturing user interaction behaviors in real time through front-end burying points, and performing preprocessing operation on the acquired user behavior data; semantic normalization processing: performing embedded vector conversion and semantic clustering on the text input by the user to generate a unique semantic identifier and a context vector; querying and updating the multi-level cache; and dynamic collaborative updating: dynamically adjusting the cache based on the cache hit rate, the response delay and the user feedback, and optimizing the cache effect in real time.

A system uses a machine learning based model such as a neural network for transcribing audio inputs. The system receives a set of audio inputs representing utterances of a conversation. For each conversation, the system determines a dialogue state for each utterance. The system uses a hierarchical language model for transcribing audio inputs of an online conversation using the received conversations. The hierarchical language model includes a top-level language model and a plurality of lower-level language model. The training is performed by (1) training the top-level language model using sequences of corresponding dialogue state, each sequence of dialogue states for a conversation, and (2) for each dialogue state, training a lower-level language model using utterances having that dialogue state. The system executes the hierarchical language model to transcribe audio input of new conversations.

The invention discloses a multi-round dialogue system and method based on conversion from a natural language to an SQL, and relates to the technical field of artificial intelligence, and the system comprises a user interaction module which is used for supporting a user to input a query problem and displaying a corresponding SQL query result; the context processing module is used for screening the effective dialogue history of the current question and re-integrating the effective dialogue history into a complete dialogue; the ambiguity processing module is used for identifying an entity from a dialogue history to define a user intention, processing a fuzzy keyword in a current question and guiding the user to complement a query condition; the re-splicing module is used for reconstructing a complete dialogue according to a time sequence, eliminating ambiguity, generating a summary through a large model, and combining the summary with a current problem to construct a complete problem; the SQL generation module is used for performing semantic analysis on the complete problem and outputting an SQL query instruction with the highest matching degree; and the execution and rendering module is used for executing the instruction on the target database and returning a query result. The conversation processing capability can be improved.

The invention provides an intelligent question and answer method and system for an entrepreneurship tutor based on a dialogue system, and relates to the technical field of natural language process.The method comprises the steps that firstly, a target dialogue request which is input by a user and contains entrepreneurship guidance related question content is obtained, and semantic analysis is conducted to generate intention categories and context association features; then, matching associated nodes in a preset entrepreneurial knowledge graph based on intention categories, traversing adjacent paths of the associated nodes according to context association features, extracting a knowledge combination relationship, and finally, generating target reply content based on the knowledge combination relationship and returning the target reply content to a user side to complete question and answer interaction, so that the question intention of a user can be deeply understood; the knowledge structure in the field of entrepreneurship guidance is fully utilized, accurate, related and deep reply content is generated, and the performance of the intelligent question and answer system in the field of entrepreneurship guidance is remarkably improved.

Systems and methods are described to address shortcomings in a conventional conversation system via a novel technique utilizing artificial neural networks to train the conversation system whether or not to continue context. In some aspects, an interactive media guidance application determines a type of conversation continuity in a natural language conversation comprising first and second queries. The interactive media guidance application determines a first token in the first query and a second token in the second query. The interactive media guidance application identifies entity data for the first and second tokens. The interactive media guidance application retrieves, from a knowledge graph, graph connections between the entity data for the first and second tokens. The interactive media guidance application applies this data as inputs to an artificial neural network. The interactive media guidance application determines an output that indicates the type of conversation continuity between the first and second queries.

The invention relates to the field of man-machine interaction, and discloses a man-machine interaction method and system based on a large language model, and the method comprises the steps: extracting original semantic features from the current input of a user; obtaining a context representation of the current dialogue; extracting a historical emotional state sequence from the multi-round dialogue historical record; generating a current user emotion representation vector based on the context representation of the current dialogue and the historical emotion state sequence; constructing a hierarchical memory structure based on the current user emotion representation vector and the context representation of the current dialogue; adjusting the attention degree of the historical dialogue content stored in the semantic memory component according to the hierarchical memory structure; and generating a response sequence through a large language model based on the adjusted attention degree, the hierarchical memory structure and the context representation of the current conversation, and outputting the response sequence to the user. According to the technical scheme, the problems of stiff switching and dialogue breakage during emotion turning of a traditional dialogue system are solved.

A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention. [FIG. 14(a)]

A dialogue system, comprising:an input, configured to receive input data from a user, wherein the input data comprises one or more of text data, speech data, image data and motion data; an output, configured to output data to the user; and one or more processors, configured to:obtain information identifying a skill and obtain information identifying a proficiency level of the user for the identified skill from stored proficiency level information; and execute at least one iteration of a coaching session, each iteration comprising performing one or more dialogue interactions, wherein each dialogue interaction comprises:receiving first input data from the user via the input;generating a first language model prompt and providing the first language model prompt to a language model, said first language model prompt comprising the first input data, the information identifying a skill, the information identifying a proficiency level of the user for the identified skill and a request to generate coaching information based on the first input data, the information identifying a skill and the information identifying a proficiency level; and generating first output data based on a first language model response to the first language model prompt and outputting, via the output, the first output data to the user;wherein the at least one iteration of the coaching session further comprises, after the one or more dialogue interactions:generating a second language model prompt and providing the second language model prompt to the language model, said second language model prompt comprising the information identifying a skill, the information identifying a proficiency level of the user for the identified skill, the first input data and the first output data, and a request to generate at least one proficiency update assessment based on the first input data, the first output data, the identified skill and the information identifying a proficiency level; generating second output data based on a second language model response to the second language model prompt and outputting, via the output, the second output data to the user; receiving second input data from the user via the input; determining a revised proficiency level of the user for the identified skill based on the second input data; andupdating the stored proficiency level information based on the revised proficiency level.

The invention relates to the technical field of speech recognition, in particular to an NLP-based AI assistant speech recognition dialogue system, which comprises an audio time sequence acquisition module, a spectrum interference suppression module, a semantic structure analysis module, a context association module and a semantic response generation module. According to the invention, through time sequence acquisition and spectrum analysis of environmental audio, user voice is effectively captured and analyzed, environmental noise interference is significantly reduced, the processing improves the definition of voice signals, accurate capture of voice data in a complex environment is ensured, and the spectrum stability is dynamically adjusted to allow the system to adapt to sudden noise change. According to the method, the processing adaptability is improved, deep semantic structure analysis enables a system to understand the emotion and word order structure of statements, more humanized responses are generated, historical interaction data are utilized in the generation of context matching information, the continuity and logicality of dialogues are improved, and dialogue assistants can better understand long-term intentions and requirements of users.

A computer-implemented method of controlling an output from a dialogue system, the method comprising:receiving, by way of an input, first input data relating to speech or text provided by a user;selecting a first state from a plurality of states of a deterministic model, at least some states of the plurality of states being associated with a corresponding portion of a language model prompt including an instruction to call a corresponding function;responsive to the first state being associated with a corresponding portion, generating a first language model prompt comprising at least part of the corresponding portion associated with the selected first state;providing the first language model prompt as input to a language model to generate a first language model output;determining whether to execute a function based on the first language model output;responsive to determining to execute a first function based on the first language model output, executing the determined first function to generate a first function output;selecting a second state from the plurality of states based on the first function output;responsive to the second state being associated with a corresponding portion of a language model prompt, generating a second language model prompt comprising at least part of the corresponding portion associated with the selected second state;providing the second language model prompt as input to the language model to generate a second language model output;determining whether to provide an output to the user based on the second language model output; andresponsive to determining to provide an output to the user based on the second language model output, outputting, by way of an output, speech or text to the user.

To provide a dialogue system capable of selecting commodities by a dialogue from a plurality of commodities.SOLUTION: A dialogue system includes: an intention estimation part for estimating an intention from voice information or text information, using an estimation model; a commodity selection part for selecting a commodity corresponding to the text information, using a commodity selection model, when the intention is commodity selection; a first text generation part for generating a first text, on the basis of a commodity selection result; and an output part for outputting the first text.SELECTED DRAWING: Figure 18

On the basis of the embodiments of the present disclosure, provided are a method and apparatus for dialog interaction, and a device and a storage medium. The method comprises: on the basis of a first user input indicative of a first task, using a first model to generate a request for a target entity, so as to instruct the target entity to provide information related to the first task; on the basis of a response from the target entity to the request, providing an execution result of the first task as a reply to the first user input; and on the basis of the response and the first user input, generating a knowledge record for storage in a knowledge base, wherein the knowledge base is used by the first model. Therefore, a task indicated by a user input can be executed by means of a target entity. An improvement in the dialog interaction capability of a dialog system is facilitated, thereby improving the accuracy and efficiency of the dialog system in providing a reply.