1513 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 embodiment of the invention discloses a retrieval generation method and device based on a large language model and a knowledge graph. The problems that retrieval semantic relevance is weak, and recalled data lacks global knowledge can be solved. The method comprises the following steps: performing vector analysis on a plurality of preset domain documents, and constructing a vector database; constructing a knowledge graph according to the entity description information and the relationship description information extracted from the plurality of field documents, and storing the knowledge graph into a graph database; performing feature extraction on a target question input by a user to obtain a multi-dimensional keyword corresponding to the target question; retrieving the multi-dimensional keywords through a preset search engine, a vector database and a graph database to obtain a target retrieval result; and determining target input information according to the target retrieval result, and inputting the target input information into the target language model to obtain target output response information.

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.

A system that utilizes an AI image generator, a search engine, and an item design system is disclosed. A text description of an item is provided to an AI image generator, which creates an image based on the text description. In some embodiments, the image is updated based on a further text description of the item. The image may be provided to the search engine and the item design system. The search engine selects, from an item catalog, an item to recommend to a user based on a similarity of the image to items in the item catalog. For example, the search engine may apply a machine learning model to compare embeddings of the item image to embeddings of images of items in an item catalog. The item design system may recommend an item design based in part on the image generated by the AI image generator.

System, methods, and algorithms are disclosed to carry out real-time video scene parsing and indexing in conjunction with query-based retrieval of geographically distributed object-attribute relationships. A distributed video analytics query mechanism is disclosed that involves swarms of small deep neural networks at embedded-AI edge devices, which can quickly perform initial feature detection and extraction and also re-identification of features or object in a cooperative manner. Then, the high-volume edge inference may fall back to the query computing model in a cloud, which performs complementary large scale up processing and result generation. The final decision, labelling, and scene investigation may be done by humans after interpreting the query results. This approach can provide the benefit of low communication costs (edge to cloud) compared to continually offloading parallel streams of edge devices, such as video, to the cloud.

The invention provides a search engine-based standard substance and standard substance retrieval and sorting method, which relates to the technical field of data processing, and comprises the following steps: acquiring retrieval keywords input by a user, constructing a standard substance and standard substance knowledge base, mapping to a low-dimensional semantic space, calculating the semantic correlation between the retrieval keywords and the standard substance knowledge, and constructing a heterogeneous information network. Generating comprehensive relevancy measurement, and performing multi-granularity semantic matching to obtain candidate retrieval results; for structural features corresponding to the candidate retrieval results, adaptive feature weights are distributed, independent clustering is carried out, subspace clustering results are obtained, optimization is carried out, global optimal clustering results are obtained, high-order semantic association features are mined, cross-category semantic association is carried out, and high-quality retrieval results are generated; performing low-rank decomposition to obtain an initial comprehensive score, modeling a sorting problem as a Markov decision process, determining an action space and a state space, constructing a reward function, determining a sorting position, and obtaining an optimal sorting result.

Described herein are systems, methods, devices, and other techniques for comprehensive and automated evaluation of digital images generated from artificial intelligence (AI) models in order to promote accurate representations of real-world content. Prompts are received at the system that are then passed to both a search engine and a generative AI model. Synthesized digital images are obtained from the generative AI model. The top-matching image from the search engine is used as a verification of the ground truth of the synthesized digital images. A realism score is generated for each synthesized digital image that characterizes the accuracy of the synthesized digital image with reference to the verification image. The realism score can be used to assist and expedite the image selection process, as well as serve as input to fine-tune the performance of generative models.

A method includes storing at least one image performance score for each of a set of images, the set of images comprising a plurality of subsets of images, each subset corresponding with a different web page of a plurality of web pages, the at least one image performance score for an image indicating a likelihood that a user will interact with the image; determining a web page score for each of the plurality of web pages based on one or more image performance scores of the subset of images that corresponds with the web page; receiving a query comprising one or more keywords or images; selecting a set of web pages by applying a search engine machine learning model to the one or more keywords and the web page score for each of the plurality of web pages; and presenting the set of web pages at a computing device.

A large language model (LLM) is used to broaden a search for supporting material for a media project. The LLM is provided with contextual material from the media project and optional grounding material and generates a list of types of supporting material. The list is provided to a machine-learning-based encoder, which encodes the list items into embedding space vectors. A body of source material, which may multimodal, is also encoded into the embedding space. A search engine is used to locate items of source material having embedded space vectors closest to vectors corresponding to the list of types of supporting material. Located items are provided to a media editing application as suggested supporting material. The body of source material may include live external sources. A small language model fine-tuned with a training data set generated by a LLM may be used instead of the LLM.

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 provides an intelligent query semantic understanding method based on information geometry and Riemannian manifold, and the method comprises the steps: constructing a query semantic space based on Riemannian measurement, extracting query geometric features through a Fisher information matrix, and achieving the precise recognition of a query intention in combination with Wasserstein distance constraint and geodesic optimization. According to the method, an adaptive optimization mechanism is established, a parallelization calculation strategy is adopted, and multi-modal feature fusion and robustness enhancement are achieved. Experiments show that compared with the prior art, the query understanding accuracy, the processing time delay and the semantic representation compression rate of the method are well improved, and the semantic understanding performance in a complex query scene is remarkably improved. The method can be widely applied to the fields of search engines, intelligent questions and answers, knowledge maps and the like, and has important practical value.

A system and method for chatbot and search engine integration comprising traversing a conversational flow of a chatbot, generating a data structure based on the conversational flow, embedding the data structure in a webpage, and directing a search engine to the webpage for the search engine to assign a webpage rank to the data structure. In some embodiments, this integration may allow search engine users to be redirected to a non-public website hosting the chatbot when that chatbot would not otherwise be identified by the search engine.

A search system may receive a search query for documents stored in a database associated with an organization. A search system may retrieve, from the database, search results (including responsive documents) that are responsive to the search query. A search system may initiate display of the search results on the user interface. A search system may transmit a prompt to a large language model, where the prompt includes the search query and information from at least a portion of the responsive documents. A search system may receive a model response with textual data that responds to the search query, where the textual data is generated by the large language model using at least the portion of the responsive documents. A search system may initiate display of the model response in a user interface.

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.

A computing system configured to perform artificial intelligence-powered search and evaluation functionalities is disclosed. The computer system can receive a natural language search input entered through a user interface, request a search engine to perform a search based on the natural language search input, receive search results returned by the search engine, and submit, in runtime, structured data contained in the search results to a large language model. The structured data includes a table including one or more records and a plurality of fields. The computing system can prompt, in runtime, the large language model to generate scores for the one or more records and present the structured data and the scores on 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.

A query is received from a client device. A large language model is prompted to generate a plurality of subtopics on the query and to generate a corresponding plurality of keywords for each of the plurality of subtopics. One or more search engines are utilized to perform a plurality of searches utilizing the plurality of subtopics and the corresponding plurality of keywords received from the large language model. A plurality of responses corresponding to the plurality of subtopics and the corresponding plurality of keywords is received from the one or more search engines. The plurality of responses is evaluated based on the corresponding plurality of keywords.

A system enables a user to query based on a user's gaze by receiving a query from the user and capturing, via an eye tracking system on a headset, the user's gaze location near an object in a local area. The system captures one or more images of the local area with the object and formats the images based in part on a region of interest in the one or more images that includes the object. The system generates a formatted query based in part on the query. The formatted query is provided to a search engine. Information describing the object determined from the one or more formatted images and information describing the query are used by the search engine to determine an answer to the query about the object. The system presents the answer to the query about the object.

The invention discloses a method and device for achieving intelligent AI webpage content obtaining based on LLM, a storage medium and a program product, and relates to the technical field of data processing. According to the method, the search instruction input by the user is received, the search instruction is input into the trained semantic analysis model, after the semantic analysis model converts the search instruction into the query parameter, the query parameter is sent to the search engine, then the webpage obtained by the search engine according to the query parameter is received, and the search result is obtained. The method comprises the following steps: acquiring a webpage, determining a target webpage from the webpage according to a preset screening rule, crawling an HTML (Hypertext Markup Language) source code of the target webpage based on a pre-acquired authorization result, inputting the HTML source code into a trained LLM model, and determining a target code label in the LLM model according to the HTML source code to obtain text content in the target code label. Wherein the LLM model has a strong language understanding capability and can dynamically adapt to the change of a webpage structure, so that the time cost of crawling webpage contents is reduced.

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.

A system generates text artifacts using a machine learned language model. The text artifacts may be provided to a search engine for providing to users along with search results. The system iteratively improves the set of text artifacts by performing the following steps. The system updates the prompt used to generate the text artifacts based on the performance of the text artifacts to obtain a new prompt. The system executes the machine learned language model using the new prompt to generate a new set of text artifacts. The system evaluates the new set of text artifacts to determine performance of each of the new set of text artifacts. These steps are repeatedly performed to improve the set of text artifacts.

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.

An information security method to detect, validate, source, and / or remediate propagated, maliciously generated, AI content is disclosed. Search-engine spider(s) to crawl the Internet to identify posted content, which is analyzed with signature-based detection, anomaly detection, and machine learning to identify suspect content, which is compared against validated content. A malicious-AI probability score is generated based on the results of the foregoing AI analysis and the content differences. Metadata corresponding to the suspect content is extracted. A malicious activity mapping is compiled from available data. Suspect content is attempted to be recreated by publicly available online AI bots to identify the AI engine that generated the malicious content. Metadata pertaining to the origination source that accessed the source AI bot. Metadata is used to trace the malicious content back to the originator. Proofs regarding the foregoing are generated. Notifications / demands may be generated. Countermeasures against future attacks may be deployed.

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 and systems. A system and method (e.g., a custom workflow actions system and related processes) for creating and using a custom action (e.g., custom code action). The custom code action may be created and added to a workflow. The workflow may be executed based on the occurrence of one or more events such that the new custom code action may be triggered as part of the workflow (e.g., resulting in an occurrence of a customized action corresponding with a custom instruction code).