617 results about "Response generation" patented technology

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention discloses a deep learning artificial intelligence security vulnerability detection platform, and relates to the technical field of intelligent detection, and the platform comprises an information processing module which collects heterogeneous data in real time, carries out the labeling, format unification and modal aggregation processing of the data, and generates a sample set; the feature learning module is used for performing feature unwrapping on the sample set by using a variational auto-encoder, extracting modal data features and potential space representation learning, and outputting a potential space vector; the response generation module is used for generating a vulnerability response strategy through a modal consistency verification and response template matching mechanism based on the vulnerability risk level vector in combination with a response generation engine; and the repair feedback module is used for executing automatic vulnerability repair operation in combination with federal reinforcement learning and Bayesian optimization, performing feedback optimization according to an execution result, and outputting the vulnerability repair operation and a feedback result. According to the method, the response strategy is combined with intelligent matching of the real-time risk level, so that the accuracy and adaptability of vulnerability repair are improved.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention relates to the technical field of intelligent customer service, and provides an AI intelligent customer service system based on a large model, and the system is characterized in that an unstructured text processed by a multi-source knowledge fusion subsystem is reconstructed into structured knowledge entries with a multi-dimensional label system, intention classification and context association, and the structured knowledge entries are dynamically integrated into an enterprise knowledge graph; the response generation subsystem is used for receiving the content queried by the user, analyzing the intention through a deep semantic understanding model in combination with an enterprise knowledge graph, and dynamically maintaining the context in combination with a multi-round dialogue state tracking technology; meanwhile, emotion dimension analysis is carried out on the content, and a personalized response is generated by fusing user service feedback; and the autonomous optimization subsystem dynamically adjusts the weight distribution of structured knowledge entries in the enterprise knowledge graph and the priority of process nodes in combination with an attribution analysis result, and triggers a knowledge updating and API process reconstruction mechanism. The method has self-learning and continuous optimization capabilities, and continuously improves the service quality and the user experience.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention provides a security event association analysis and question answering method and system and a medium, and the method comprises the following steps: context-aware query completion: receiving an original query input by a user, obtaining a historical record of a current dialogue, and forming a context enhanced query according to the original query and the historical record of the current dialogue; dynamic task identification: outputting a prediction task model according to the context enhancement query in combination with a dynamic task identification strategy; multi-dimensional retrieval: performing multi-dimensional retrieval on the prediction task model to obtain a final knowledge context packet; and knowledge-driven response generation: generating a knowledge-driven response based on the knowledge context packet, and outputting a final security analysis report after the generated content passes verification. According to the method, efficient, accurate and explainable intelligent association analysis of the multi-source heterogeneous security data is realized by constructing a multi-dimensional knowledge base, designing a dynamic task recognition mechanism and realizing context-aware query completion.

Systems and methods described herein relate to the use of generative artificial intelligence to facilitate rendering of user interface elements in a user interface associated with a digital assistant. A backend response is automatically generated in response to user input provided via the user interface associated with the digital assistant. Prompt data is generated. The prompt data includes an instruction to generate an intermediate representation of an output data structure supported by the digital assistant. The prompt data is provided to a generative machine learning model to obtain the intermediate representation. The intermediate representation is processed to obtain the output data structure. One or more user interface elements are rendered based on the output data structure. The one or more user interface elements present the response data via the user interface associated with the digital assistant.

Techniques for generating a natural language response to a user input of a dialog are described. A system receives a natural language user input of a dialog and determines dialog history data including a previous natural language user input of the dialog. Based on the first natural language user input and the dialog history data, the system generates at least a first question associated with the natural language user input. Based on the first natural language user input and the dialog history data, the system generates at least a first answer to the at least first question. Using the dialog history data, the first natural language question, and the first natural language answer, the system generates an output responsive to the natural language user input.

Systems and methods for enhanced conversational performance of large language models using adaptive retrieval-augmented generation are disclosed. A method may include: (1) receiving a query from a user; (2) retrieving a plurality of summaries of historical conversations from a database of historical conversation summaries similar to the query; (3) generating a first prompt comprising the query and the plurality of summaries; (4) submitting the first prompt to a first large language model (LLM); (5) receiving, from the first LLM, a first response; (6) presenting the first response to the user; (7) generating a second prompt for a summary of the query and the first response; (8) submitting the second prompt to a second LLM; and (9) saving a second response to the second prompt from the second LLM to the database of historical conversation summaries, wherein the second response comprises the summary.

A system and method for field service assistance for telecommunications operations are described, which utilize a data acquisition module configured to receive multimodal data inputs including structured and unstructured data from field operations. A preprocessing module normalizes the multimodal data inputs to generate pre-processed data. A vectorization module transforms the pre-processed data into numerical vector representations using domain-specific embedding models trained on telecom equipment data, implementing convolutional neural networks for image feature extraction and transformer-based encoders for text vectorization. A contextual retrieval module retrieves contextually relevant historical data from a vector database by computing similarity metrics between current job vectors and stored job completion vectors. A response generation module processes the numerical vector representations and retrieved contextual data using an evolutionary algorithm engine to generate structured job summaries and real-time field recommendations.

Methods, systems, and apparatus, including computer-readable media, for determining and revealing interpretations of artificial intelligence models. In some implementations, a system receives a prompt from a user. The system obtains code or instructions generated by a artificial intelligence or machine learning (AI / ML) model, where the code or instructions specify criteria to retrieve data from a data source to respond to the prompt. The system generates a set of results from the data source based on the generated code or instructions, and obtains a response to the prompt that an AI / ML model generates using at least a portion of the set of results. The system also generates an interpretation statement that indicates how the prompt was interpreted by the one or more AI / ML models. The system provides output that includes (i) the response to the prompt and (i) the generated interpretation statement.

In some implementations, the techniques described herein relate to a method including: receiving a natural language question from a user; determining, using a large language model, whether the natural language question is a transactional question or an informational question; generating, using a first generative artificial intelligence (AI) model, a first response to the natural language question when the natural language question is an informational question; generating, using a transaction generative AI model, a second response to the natural language question when the natural language question is a transactional question; generating, using a sentiment-based response generator, a third response based on one of the first response or the second response and a sentiment of the natural language question; and presenting the third response to the user.

Some implementations relate to utilization of generative model(s) (e.g., large language model(s)) in selectively generating a comprehensive response for an input query, where the comprehensive response is generated based on multiple sub-query responses, and where the multiple sub-query responses are generated based on multiple sub-queries decomposed from the input query and corresponding tools for the sub-queries. Generating the comprehensive response based on the multiple sub-query responses integrates, into the comprehensive response, detailed information and / or actionable content that are responsive to the multiple sub-queries decomposed from the input query.

The invention discloses an intelligent agent digital image interaction generation method based on multi-modal perception, which comprises the following steps: collecting multi-modal input data of a user, and respectively carrying out preprocessing and feature extraction on the multi-modal input data; inputting to an improved efficient modal cross learning network, and carrying out multi-modal feature fusion processing; constructing a semantic intention map, introducing a time index edge weight and an emotion driving edge weight, and encoding the map by using a structure perception map neural network; a modal style vector is extracted through a cross-modal style contrast learning mechanism, and a personalized style coding vector is generated through a hierarchical nested structure; inputting a personalized regulation and control gating mechanism, and regulating and controlling the middle layer representation in the interaction strategy generation process by adopting a feature channel linear modulation method; inputting the representation vector into a behavior strategy generation module to generate a multi-modal behavior output sequence; and the sequence is output to drive the digital image to perform synchronous response, and natural response generation in the user interaction process is completed.

An example operation may include one or more of receiving interaction content from a interaction session between devices associated with an organization, identifying contextual attributes of one or more of the interaction content and the interaction session, matching the interaction content to a subset of vectors within a vector storage based on labels previously assigned to the subset of vectors, augmenting a machine learning (ML) model based on the subset of vectors to generate an augmented ML model, and generating a response for the interaction session based on execution of the augmented ML model on the interaction content and outputting the response to a device participating in the interaction session.

The invention discloses a tourism service agent system and method based on a multi-modal large model. The system comprises five parts: a user interaction interface, which is used for receiving a user request and returning a response; the agent center is used for performing intention understanding, task planning and response generation through a multi-modal large language model; and the tool calling module is used for executing specific API business operation. According to the intelligent tourism service system, the work of each module is coordinated through the intelligent agent center, a complete closed loop from user intention understanding to service execution is realized, accurate, reliable, whole-course and personalized intelligent tourism service can be provided, and the problems that a traditional tourism service system is single in function, inaccurate in information and lack of action ability are effectively solved.

Disclosed herein, is a method and system for selecting a LLM for response generation to user queries. The method includes receiving a user query from a user device. The method includes determining, for the user query, a query type from a set of query types through a fine-tuned text classification model. The method includes retrieving a plurality of document embeddings based on the user query and the query type from a vector database through a semantic search technique. The method includes preparing a prompt using the user query and the plurality of document embeddings. The method includes inputting the prompt to an LLM selected from a set of LLMs based on the query type. The method includes generating, via the selected LLM, a response to the user query based on the prompt.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

There is provided a computer method, system and device comprising detecting an updated iteration of a document for query response generation; comparing the updated iteration to a prior iteration to identify chunks of the updated iteration of the document that differ from corresponding chunks of the prior iteration, the prior iteration for generating a set of synthetic questions and answers using an LLM. Responsive to identifying that a given chunk of the updated iteration differs from a corresponding chunk of the prior iteration, the method triggers generation, using the LLM, of a new set of synthetic questions associated with corresponding text in the given chunk defining a new set of synthetic responses, and wherein the new set of synthetic questions and responses replaces at least a subset of the set of synthetic questions and answers associated together by a mapping with the corresponding chunk of the prior iteration.

The invention discloses a multi-context semantic recognition and understanding method based on a large language model. The method comprises the following steps: S1, generating a semantic unit sequence; s2, constructing a context nested vector sequence; s3, constructing context priori representation, and generating a semantic representation sequence; s4, outputting a state vector of the semantic path by adopting a gating loop unit, obtaining a matching degree score according to a feedforward neural network, and determining a deliberate map tag and an alternative intention tag; s5, slot field extraction and semantic filling are completed, and a structured semantic task unit is generated; and S6, completing semantic recognition and service response closed loop. According to the method, by introducing a prefix regulation and control mechanism and a multi-context semantic modeling structure, the accuracy of intention recognition in multiple rounds of dialogues and the consistency of the context generated in response are remarkably improved, and the method is suitable for natural language understanding scenes of multi-language intelligent customer service, cross-context man-machine interaction and complex task driving.

Embodiments described herein provide A method of fine-tuning a neural network based model. In some embodiments, a system receives, via a data interface, a training dataset including a plurality of input samples. The system generates, via a pre-trained neural network based model, a first response based on a first input sample of the plurality of input samples, and a second response based on the first input sample. The system generates, via a trained reward model, a first reward score based on the first input sample and the first response, and a second reward score based on the first input sample and the second response. The system computes a loss function based on the first prompt, the first response, the second response, the first reward score, and the second reward score. The system updates parameters of the neural network based model based on the loss function.

The invention is suitable for the technical field of natural language processing, relates to a hierarchical memory and context awareness retrieval method of a large role model and a related product, and aims to solve the problems of limited model memory duration, insufficient retrieval correlation and insufficient personality consistency in a long dialogue. According to the invention, a short-term-middle-term-long-term three-level memory architecture is adopted, and a memory attenuation and migration mechanism is combined, so that dynamic metabolism of memory is realized; related memories are recalled accurately through a context semantics and role personality double-sensitive double-stage retrieval algorithm; relying on a personality-linked memory fusion and response generation strategy, the reply is ensured to fit personality setting; and a closed-loop adaptive learning mechanism of dialogue-memory-retrieval-generation-feedback is constructed, and the memory quality is continuously optimized. According to the method, the role large model can have the human-like continuous memory ability, the continuity, retrieval accuracy and personality consistency of long dialogues are remarkably improved, and the long-term personalized interaction requirements of scenes such as digital personality assistants and dialogue agents are met.

The invention relates to the technical field of data analysis, can be applied to agricultural disaster monitoring, financial science and technology and other business scenes, and discloses a multi-source monitoring analysis and decision-making method, device, equipment and medium, and the method comprises the steps: obtaining multi-source monitoring data, and carrying out the time-space multi-scale fusion to generate time-space fusion data, extracting difference features of the target object before and after the event based on the fusion data to identify a first type of events, predicting a second type of events based on dynamic time sequence features by using a space-time diagram network model, and constructing a knowledge graph to perform causal reasoning on two types of event results to generate a reasoning enhancement result; and generating comprehensive abnormal analysis information and a response decision according to a reasoning enhancement result. According to the method, spatial-temporal features of multi-source data are fused, a causal reasoning mechanism is introduced, the identification and prediction results are subjected to correlation analysis, dynamic interpretation and response generation of complex events are achieved, and therefore the anomaly identification precision and the real-time performance and interpretability of risk decision making are improved.

The invention relates to the technical field of block chain cross-chain transactions, in particular to an AI-based interpretable risk early warning system for block chain cross-chain transactions, which comprises a dynamic feature extraction module, a risk factor identification module, a decision logic construction module and an early warning response generation module. According to the method, the dynamic characteristics of the transaction behaviors are analyzed, the historical data and the external environment are combined, the potential risk factor distribution diagram and the risk weight mapping table are generated, the risk early warning information is generated by using the neural network model, and the reliability evaluation is introduced to improve the prediction reliability. According to the invention, the risk node and weight distribution in the cross-chain transaction can be accurately identified, the risk time range is defined, the early warning accuracy and timeliness are improved, the user is helped to avoid economic loss, and the stability of the cross-chain transaction system and the user experience are enhanced.

The invention provides a random access response generation method, a program product, electronic equipment and a storage medium, and is applied to the technical field of communication, and the random access response generation method comprises the steps that after a random access preamble sent by a terminal is detected, a random access response is generated, the random access response carries a timing advance field, and the timing advance field is sent to the terminal; the value of the timing advance field is configured as follows: a first part in a preset value range represents a positive timing advance, and a second part in the preset value range represents a negative timing advance; and sending the random access response to the terminal. According to the scheme, the timing advance field value in the random access response is divided into the first part representing the positive timing advance and the second part representing the negative timing advance, so that the base station can transmit the timing adjustment information with relatively high accuracy to the terminal according to the actual arrival condition of the random access preamble, and the user experience is improved. Therefore, the accuracy of the detected leading TA is improved.

Techniques for URL handling in Retrieval-Augmented Generation (RAG) systems are disclosed. A RAG system uses a generative AI response in the selection of links to include in a modified output from a RAG system. The links are based on URLs that are extracted from ingested documents. In response to a query, the system generates a response and then performs a matching operation to match a portion of the generated response to a URL in a URL-keyword mapping. The system finds a match between a portion of the generated response and a keyword that is associated with a URL, generates a hyperlink using the matched URL, and adds the hyperlink to the response.

A system manages conversation topics and uses them in an online conversation. The system generates metadata describing a set of conversation topics based on the conversation. The metadata describes a particular conversation topic comprises a summary of interactions relevant to the particular conversation topic. The system receives a natural language request from the user via the user interface. The system generates one or more prompts comprising the natural language request and metadata describing the set of conversation topics and request the machine learning based language model to generate a reply to the natural language request using a conversation topic relevant to the natural language request. The system provides the prompts to the machine learning based language model for execution and receives responses from the machine learning based language model. The system generates a reply based on the one or more responses.

The invention belongs to the technical field of intelligent interaction, particularly relates to an intelligent interaction method, system and device based on AI communication and a storage medium, and aims to solve the problems that in the prior art, AI scene coverage is narrow and is separated from a business process; the system comprises four modules: a context sensing and modeling module which collects multi-source context information in real time, and generates a three-dimensional context tensor through structured alignment and time sequence normalization; the intention analysis and reasoning module performs intention level decomposition by using a deep semantic model based on the tensor, and outputs an intention recognition result and probability distribution; the response generation and adaptation module is used for generating multi-mode responses such as texts, voices, images or equipment instructions in combination with intention and context tensors, and outputting the multi-mode responses after consistency verification; and the interactive feedback and optimization module collects user explicit and implicit feedback and is used for updating model parameters and strategy weights online to realize continuous optimization of the system.

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 relates to a multi-code fusion method and system, and solves the problem that a user needs to frequently switch different two-dimensional codes to adapt to various scenes in the interaction process with a city service system.The method comprises the steps that the system is started to analyze heterogeneous features of an original code, and a standardized feature set with a cryptographic identifier is generated; and performing hierarchical fusion to generate a fusion code prototype and a behavior credit chain, performing verification to form a formal fusion code, and calculating an initial credit entropy. Bidirectional verification is executed during terminal verification, and a minimum transaction voucher is generated after scene matching. If successful, starting the fusion code, recording the transaction and dynamically adjusting the authority; and if not, triggering a hierarchical security response, generating a security sub-voucher or switching to the original code for operation. The offline scene generates an offline code. The method has the following effects: through dynamic authority and credit management, on the premise of not sacrificing security, one fusion code is used for replacing a plurality of scene codes, and unification of convenience and security is realized.