875 results about "Search engine" patented technology

The technology described herein relates to systems, methods, and computer storage media, among other things, for providing search query intent specificity. Embodiments may include identifying a search query performed using a search engine and generating a query vector for the search query by aggregating search result embeddings (e.g., item listing vectors) of search results from the search query. Further, in some embodiments, similarities (e.g., cosine similarities) between the query vector and the item listing vectors can be determined. As such, an intent specificity of the search query can be determined. Further, in some embodiments, the intent specificity can be used to train an intent specificity machine learning model for generating intent specificity scores for other search queries. Based on the intent specificity scores determined using the one or more trained intent specificity machine learning models, determinations can be made with respect to precision and recall, etc.

The invention discloses a multi-modal bill processing method based on dynamic knowledge enhancement. The multi-modal bill processing method comprises the following steps: S1, constructing a dynamic knowledge base containing an aging weight; s2, synchronously processing text, image and format features of the bill by adopting a multi-modal feature fusion network to generate a composite feature vector; s3, semantic-level, format-level and timeliness three-level fusion retrieval is carried out based on the composite feature vector, and a three-level fusion retrieval engine comprises dynamic weighted sorting with timeliness attenuation, a difference degree triggered artificial review mechanism and a policy sensitive slope adjustment algorithm; s4, setting a multi-expert cooperative verification system, wherein the multi-expert cooperative verification system comprises cooperative work of a rule engine, a large language model and a logical reasoning module; s5, implementing a dynamic knowledge updating mechanism, and automatically triggering incremental learning of the knowledge base when policy change or format update is detected; and S6, outputting structured data, and synchronously generating an auditing traceability chain containing a decision path. According to the method, the key field identification accuracy can be improved, and auditing traceability and non-perceptual increment updating in the whole process are realized.

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

The invention relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

This disclosure describes utilizing a generative document system to dynamically build and provide generative search result documents. The generative document system utilizes an aggregated framework that leverages one or more large generative models (LGMs). For example, the aggregated framework includes three stages where local processes are applied to generative outputs from LGMs, with each stage building upon the generative inputs from previous stages. The generative document system uses the aggregated framework to create generative search result documents based on search queries and their corresponding search result links. These generative search result documents provide interactive, intuitive, comprehensive, and flexible curation of answers that address the respective search queries.

An augmented search engine is provided. The augmented search engine receives an initial query for a search from a user. The augmented search engine determines a next search phase using a search state database. When the next search phase includes requesting additional user input, the augmented search engine generates a user prompt using the initial query, displays this prompt to the user, and stores the prompt in the search state database. Upon receiving user input in response to the prompt, the augmented search engine generates a search query using the initial query, the user prompt, and the user input and determines search results by querying search indexes with the search query and stores these results in the search state database. The augmented search engine generates a search summary using the initial query, the user prompt, the user input, and the search results, and displays this summary to the user.

In various examples, systems and techniques are provided that encapsulate indexing and query operations into an application programming interface (API) that automates and coordinates calls to various local and cloud-based services. When a user has a document(s) to add to a retrieval augmented generation (RAG) database, the API may offer to the user multiple document processing pipelines (DPPs) having pre-set indexing configurations. Similarly, when a user query is received, the API may generate calls to implement query processing that does not require the user to manually configure retrieval and processing of the embeddings. The API may further implement calls that locate a relevant embedding store and provide the stored embeddings, together with the query embeddings, to a search engine that identifies the most relevant matches. The API may then access the embedding-to-text indexing and identify relevant text segments and documents to a prompt generator.

The invention discloses an intelligent marketing system based on user portraits and behavior prediction, which belongs to the field of artificial intelligence, and comprises multi-source data acquisition and integration: a diversified data acquisition interface is configured, and user multi-type data can be acquired from multiple channels such as an e-commerce platform, social media, a search engine and an enterprise CRM system; comprising basic information, browsing and purchasing behavior records, social interaction information, search preferences and emotion-related text data. Data of different formats and sources are integrated into a unified data set by using preprocessing means such as data cleaning, duplicate removal and standardization; according to the invention, through accurate user portrait and behavior prediction, an enterprise can accurately grasp the demand preference of a target customer, accurate pushing of marketing information is realized, the hit rate and the conversion rate of marketing activities are greatly improved, and the marketing effect is effectively improved; according to user value and behavior prediction, marketing resources are reasonably allocated, resource waste is avoided, and enterprise marketing efficiency is improved.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript. The transcript may be post-processed which may include keyword extraction, topic extraction, feature extraction, event generation, trigger action, and / or search indexing. Conversation information may be presented based on the pre-processing, the transcribing, and the post-processing.

The invention discloses a robot visual identification decision control method based on deep learning, and relates to the technical field of control systems. Comprising the steps of constructing a thinking decision tree according to an input instruction and a real-time visual image, and obtaining a linear relationship between a middle-layer leaf node as a main body and a top-layer leaf node for realizing association between a current visual feature and a task intention. According to the method, deep fusion and unified representation of multi-source heterogeneous data are realized by constructing a multi-layer thinking decision tree structure and combining multi-modal feature fusion and a deep learning engine, and text instructions, visual images and other different modal information are effectively integrated by constructing a fusion search engine and a semantic alignment mechanism, so that the multi-source heterogeneous data fusion and unified representation are realized. Cooperative processing of accurate analysis of task intentions and environment perception is realized, and real-time response and decision accuracy of the service robot to complex tasks are improved.

A search engine for providing intelligent or conversational responses to user queries into a digital building portfolio are disclosed. In embodiments, a search engine receives a user query and query context. The query and query context are processed and used to search into a vector database and knowledge graph database that are generated from data in the digital building portfolio. The results of the search are used to retrieve relevant data from the digital building portfolio, which are then summarized and used to generate an appropriate response. Other embodiments may be described and / or claimed.

A method according to the present disclose may include presenting, on a graphical user interface (GUI), an interactive element; receiving, via the interactive element on the GUI, a research target and a type of research; autonomously retrieving, from a search engine, search results related to the research target; identifying, using a predictive machine learning model, at least one relevant portion of the search results, the at least one relevant portion comprising information related to the research target and responsive to the type of research; generating a prompt based on the type of research and the at least one relevant portion of the search results; and receiving, from a generative machine learning model in response to receipt of the generated prompt, a report indicative of the research target.

The invention provides an intelligent environment damage questionnaire generation and semantic analysis method and system, and the method comprises the steps: obtaining structured environment data through a government department interface, and capturing network public data in combination with an AI search engine; performing cleaning and normalization processing on the data, and constructing a standardized environment database; constructing a semantic model based on an NLP technology, dynamically generating a customized investigation problem according to an interview object type and an event scene classifier, and optimizing the priority of the problem; extracting a key entity from the interview text by adopting a pre-training model; mapping the entity to a preset pollution category through the environmental damage ontology library, and generating a dynamic causal association map; and verifying the integrity and consistency of the atlas by using a logic verification rule, and automatically outputting a structured survey report. According to the method, the defects that a traditional questionnaire is rigid in design, fragmented in information and insufficient in implicit association mining are overcome, the accuracy and efficiency of environmental damage investigation are remarkably improved, and intelligent support is provided for pollution traceability and responsibility definition.

The invention relates to an information retrieval method and device for hierarchical planning reinforcement learning based on retrieval enhancement. The method comprises the following steps: acquiring a natural language query request of a user; calling a large language model of hierarchical planning reinforcement learning training based on retrieval enhancement to generate semantic keywords; the high-level strategy splits the natural language query request into a series of sub-query requests; the low-level strategy generates a retrieval query according to the context of the current sub-query request, and obtains a result verification condition from an external knowledge base through an RAG model; calling a search engine to execute multiple rounds of fine-grained information retrieval to obtain a plurality of candidate retrieval results; and performing deep semantic matching analysis on the candidate retrieval results and the result verification conditions by utilizing the post-trained large language model to calculate a matching degree score, and screening the candidate results according to the matching degree to obtain a final information retrieval result. Compared with the prior art, the method has the advantages that high-quality content retrieval and result display of complex query problems of users in various scenes can be realized.

Methods and systems for interacting with users via a chatbot. A natural language query is received and processed by submitting a search query to a search engine. The search engine identifies relevant information including textual information and images for formulating a response. The identified information and query are submitted to a Large Language Model which generates a response displayed via the chatbot. The response may include textual information and relevant images. The system can extract text from images of documents and convert textual information into numerical vector representations for processing. Selectable options based on clustered relevant information can be provided to users for query refinement when appropriate. The chatbot interface enables natural language interactions while leveraging search capabilities and Artificial Intelligence to provide informative and helpful responses with both text and visual elements.

This disclosure describes utilizing a generative document system to dynamically build and provide generative text documents using one or more generative artificial intelligence (AI) models. For example, the generative document system efficiently utilizes various systems and one or more generative AI models to determine intents and topics, curate topic sections, and generate a generative text document that includes a directed answer along with select curated topic sections for search queries. In various implementations, the generative document system performs additional actions that enhance the efficiency and accuracy of operations used to produce generative text documents. Additionally, in many cases, these generative text documents provide a foundation for providing an interactive, intuitive, wide-ranging, and flexible curation of answers to users that address the corresponding search queries.

The invention relates to the technical field of text generation, provides an NL2SQL method and system based on thinking reasoning, and aims to enable a model to interact with a database environment by introducing a reinforcement learning mechanism to obtain reward signal feedback. The system comprises a table field recall component and a structured query language generation component. The table field recall component fills table field information into a preset template to construct a structured text, then vectorization processing is carried out, the structured text is stored into an elastic search engine, a relevant table is searched according to user query, and finally specific fields are screened through a field selector model. And the structured query language generation component receives the screening result, generates a structured query language in combination with user query, and performs retrieval replacement on a filtering value. In the aspect of training, the method combines supervised fine-tuning cold start and reinforcement learning reasoning training, and the model performs reasoning and thinking according to an input context through trial and error learning. In the reinforcement learning training process, a reward function iteration optimization model based on task execution result correctness is adopted.

Systems, methods, devices, and computer readable storage media described herein provide techniques for intelligently interpreting and / or responding to search queries. In an aspect, a search query comprising a search term is received. A generative artificial intelligence (AI) model is utilized to generate a question based on the search term. Data semantically similar to the question is identified. The generative AI model is used to determine an answer to the question based on the question and the identified data. In a further aspect, a question-answer pair comprising the question and answer is generated. In an alternative further aspect, the answer is caused to be presented in a graphic user interface corresponding to a search engine. In a further aspect, the answer is determined during an active session with the search engine. In a further aspect, the search term and the question-answer pair are stored in a key-value store.

Disclosed is a computer-implemented method for constructing a domain-specific language learning model (LLM). The computer-implemented method includes a step of ingesting, from a server, a domain-focused dataset. The computer-implemented method includes a step of assimilating, from a search engine database, a real-time digital data stream. The computer-implemented method includes a step of integrating the domain-focused dataset, and the real-time digital data stream within an application logic layer to obtain an integrated dataset. The computer-implemented method includes a step of employing, by a processor, a transformer algorithm on the integrated dataset to extract a plurality of domain-specific textual insights. The computer-implemented method includes a step of utilizing, by the processor, said domain-specific textual insights to execute one or more business tasks. The computer-implemented method includes a step of presenting both the domain-specific textual insights and the executed business tasks on a user interface.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects (and core objects) for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

Techniques for determining whether retrieved content is sufficient for a language model to generate a response to an input are described. In some embodiments, a system may determine a retrieval complexity (RC) metric based on a question, a reference answer and document results retrieved from a retrieval component (e.g., a search engine, a knowledge search, etc.). The RC metric may be based on whether one of the document results corresponds to the reference answer and whether the reference answer can be determined from the set of the document results (e.g., using two or more document results). The RC metric can be used to determine when retrieved content is to be used by a language model for responding to an input corresponding to the question.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention provides a novel large model traditional Chinese medicine course resource intelligent question answering method and system, and the method comprises the steps: enabling a large model to convert a user question into a question vector, and carrying out the similarity retrieval matching in a Fast vector library, and constructing a first reference answer; performing entity relationship and different noun mining on the question by the large model, performing knowledge graph filtering, analyzing the real intention of the question asked by the user, generating a corresponding Cypher statement according to the filtered entity and relationship, executing the Cypher statement in Neo4j, and constructing a second reference answer; performing fine word segmentation on the questions by the large model by using a predefined word segmentation device, executing full-text search in a pre-constructed Elasticsearch engine index kernel, sorting search results according to a characteristic sorting strategy, and constructing a third reference answer; and the large model carries out knowledge summarization and generation according to the first, second and third reference answers, strictly screens data reference sources through a verification mechanism, and generates the most accurate comprehensive answer. Intelligent question answering can be performed on traditional Chinese medicine course resources.

Mechanisms are provided to implement a machine learning framework that operates to register a plurality of machine learning algorithms used to train machine learning models to perform related tasks, and to index the machine learning algorithms to generate and store a machine learning algorithm metadata model for each machine learning algorithm. The machine learning framework receives a user specification of an analytics pipeline task for which a machine learning model is to be trained, and converts the user specification to machine learning algorithm search criteria used to search the index to identify matching machine learning algorithms having a corresponding machine learning algorithm metadata model that matches the machine learning algorithm search criteria. The machine learning framework outputs information describing the matching machine learning algorithms.

The invention discloses a general security risk monitoring method and device based on image scene recognition knowledge retrieval enhancement, computer equipment and a readable storage medium, and the method comprises the steps: firstly obtaining video data and extracting a video image, and then obtaining a scene recognition result through a pre-trained model and a multi-modal large model, the method comprises the following steps of: firstly, searching corresponding potential safety hazard rule knowledge by utilizing an RAG search engine, then carrying out potential hazard identification to obtain a result and a suggestion, and finally, carrying out query structured rewriting on related information based on a thinking chain to obtain a target safety risk monitoring result, thereby realizing efficient and accurate safety risk monitoring.

The invention relates to the technical field of artificial intelligence, in particular to a question answering system and method based on a large model and a search engine, comprising an input module, a search engine module, a crawler module, a large model module and an output module; the method has the beneficial effects that by combining the large language model and the search engine technology, webpage results searched by the user questions in the search engine are subjected to correlation screening and sorting, webpage text contents are crawled and spliced to be input into the large model, and answers are summarized and output, so that the accuracy and timeliness of answering the user questions by the question answering system are higher.

The invention provides an automatic SEO setting system and method in a station building CMS, and belongs to the technical field of station building CMS. According to the method, the AI technology is utilized, the keywords are deeply mined and screened, the keywords with high conversion potential and the long tail keywords are accurately recognized, the real demands of users are insighted, the competition situation of the keywords is intelligently evaluated, and the popularity change is predicted. In the aspect of content creation, a content outline, a first draft and a specific paragraph can be generated according to a target keyword and a user portrait, optimization suggestions can be provided in the aspects of readability, keyword layout and the like, and content personalized recommendation is achieved. Compared with a traditional SEO technology, the method has the advantages that the keyword screening efficiency and the long tail keyword coverage rate are improved, the matching degree of content and user requirements is optimized, ranking and flow of websites in a search engine are improved, and the method is efficient and accurate.

A text processing method includes obtaining a query text, invoking a search engine interface based on the query text to obtain a plurality of text search results corresponding to the query text, obtaining, from the plurality of text search results, a plurality of answer text segments matching the query text, determining a relevance between the query text and each of the plurality of answer text segments, determining one of the plurality of answer text segments that corresponds to a maximum relevance as a reference text of the query text, and invoking a language model based on the query text and the reference text to obtain a reply text of the query text.

A computer-implemented method estimates body dimensions and provides clothing recommendations using frontal and side images from a user device. The images undergo validation via a deep neural network, ensuring correct posture, visibility, and lighting. A convolutional neural network (CNN) extracts geometric features, while segmentation techniques remove background noise. A simplified 3D body model is generated, and key body dimensions—such as shoulders, chest, waist, and legs— are calculated using object detection models and a standardized height reference. The method then suggests clothing sizes by matching the user's measurements with predefined size charts and enables a virtual fitting room for trying on clothes. An auto-tagging system identifies clothing attributes and links them to an image search engine for recommendations. All images are encrypted and securely stored, with an option for permanent deletion.The method can be integrated into third-party applications via an SDK or API, allowing real-time body measurement and virtual fitting functionality.

The invention discloses a knowledge graph-based search engine relation chain recommendation method, which comprises the following steps of: receiving a relation chain recommendation request submitted by a user through a search engine to obtain initial query information; performing semantic analysis and intention recognition on the initial query information, and determining a corresponding entity type and a relationship type; calling a dynamic knowledge selection and evaluation mechanism according to the entity type and the relationship type, and dynamically screening the original knowledge graph to obtain effective knowledge sub-graphs; performing diffusion type reasoning based on a knowledge graph diffusion model to generate a candidate relation chain path set; evaluating the quality score of each candidate path in real time; and performing dynamic screening according to the path quality score, and outputting a relation chain path recommendation result. According to the method, accurate mining and dynamic optimization of the knowledge graph relation chain path are realized, and the accuracy of a recommendation result and the flexibility of user interaction are improved.