88 results about "Search history" patented technology

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating descriptions of digital components. In one aspect, a method includes receiving data indicating a query received from a client device of a user. An initial digital component is obtained. Search history data that includes a set of related past queries received from the user is obtained. Updated text related to the first resource is generated by conditioning a language model with one or more contextual inputs that cause the language model to generate one or more outputs that include the updated text, the one or more contextual inputs characterizing one or more of the first query, data related to the initial digital component, the sequence of related past queries, or one or more tasks to be performed by the language model. An updated digital component that depicts the updated text is generated and provided.

Techniques for generating and presenting search filter recommendations are disclosed. A system determines a composition of search filter tiles, including a relative number of machine learning-generated search filter tiles and non-machine learning-generated search filter tiles, based on recommendation criteria. The recommendation criteria include an amount of user search history data that exists. The system applies search filter recommendation policies to generate a set of search filter recommendations, including hard-coded search filters, search filter recommendations generated by a user-specific machine learning model, search filter recommendations generated by a machine learning model trained on data from multiple different users, and algorithm-based recommendations. The system generates different sets of search filter recommendations made up of different ratios of recommendations from different sources based on determining if search filter criteria are met.

An e-commerce precise advertisement putting method based on big data mining comprises the following steps: collecting user data: collecting multi-dimensional data of a user through an e-commerce platform, including but not limited to browsing history, search history, purchase record, social media interaction, geographical location information and the like; all data should be encrypted and desensitized to ensure data privacy security, modeling is performed on features such as interest, purchasing power and purchasing cycle of a user based on user data by applying clustering analysis, association rule mining and other big data analysis technologies, a dynamically updated user portrait is formed, and a personalized user tag is established on the basis, so that the user experience is improved. , such as price sensitivity, product preference and the like. A differential privacy technology is adopted to protect user data, and the personal privacy of a user is ensured not to be leaked through a noise injection and encryption algorithm. Even in the big data analysis process, the sensitive information of the user data cannot be restored, so that the trust of the user to the platform is greatly enhanced, and the risk of data leakage or abuse is avoided.

The system receives a voice query at an audio interface and converts the voice query to text. The system can determine pronunciation information during conversion and generate metadata that indicates a pronunciation of one or more words of the query, include phonetic information in the text query, or both. A query includes one or more entities that may be more accurately identified based on pronunciation. The system searches for information, content, or both among one or more databases based on the generated text query, pronunciation information, user profile information, search histories or trends, and optionally other information. The system identifies one or more entities or content items that match the text query, and retrieves the identified information to provide to the user.

An embodiment user terminal includes an image acquisition part, a user interface configured to display an image photographed through the image acquisition part, a position detection sensor configured to detect a position of the user terminal, and a controller configured to determine a recommended point of interest (POI) among a plurality of POIs located around the user terminal, based on operation information about each of the plurality of POIs, a user's search history for a POI, and the user's POI scrap, and to control the user interface to display an augmented reality (AR) image corresponding to the recommended POI by superimposing the AR image on the image photographed through the image acquisition part.

The invention discloses an intelligent anti-cancer science popularization content recommendation method and system based on patient big data, and the method comprises the following steps: collecting patient data, including static data and behavior data, the behavior data including search history, click record, browsing duration, activity participation record, and reading content; extracting user features from the patient data, and generating a user portrait; anti-cancer popular science contents in the platform are collected, and labels are added to the popular science anti-cancer contents by using a natural language processing technology and a machine learning algorithm; constructing a content recommendation engine for dynamically matching the user portrait with the tag; according to the matching result of the content recommendation engine, generating a personalized content recommendation list in combination with the current anti-cancer treatment stage of the patient; and transmitting the recommended content by using multiple channels according to the content recommendation list. According to the method, the user portrait is constructed through the multi-dimensional data of the patient, so that the pertinence and adaptability of recommendation can be effectively improved.

The purpose of the present invention is to provide an interactive system that allows addition of appropriate response content. An interactive system 100 includes a history DB 105b that stores search history information containing an acquired key, which is a keyword acquired from an input sentence acquired by user operation, a new candidate key, which is an unknown word, and a query sentence, which is response content retrieved using the acquired key. A query sentence generation unit 109 generates a new query sentence on the basis of the search history information, by using a search key contained in the query sentence and the new candidate key.

The invention discloses an animation design material library management method based on big data, and relates to the technical field of animation design, and the method comprises the following steps: obtaining all materials in a material library and attribute information of the materials, the attribute information specifically comprising resolution, format, style and use scene; classifying all the materials in the material library according to the attribute information of the materials, and outputting a classification result of the materials in the material library; acquiring historical user behavior data of the material library, wherein the historical user behavior data specifically comprises search history, download records, use scenes and recompilation behaviors; according to the steps 1-3, the problems that an existing material library is not clear in classification and insufficient in personalized recommendation are solved, efficient retrieval and personalized recommendation of materials are achieved through refined classification and labeling processing in combination with precise calculation of the user preference degree, the screening cost of a designer is reduced in the process, and the screening efficiency of the designer is improved. And the convenience and the use efficiency of material searching are obviously improved.

To provide a system.SOLUTION: A system includes: specifying means for specifying information useful for a user from a talk content of the user and a search history in which the user used an internet search tool, contained in various information sources which the user daily uses; notification means for notifying the user of the daily schedule of the user, based on the information specified by inputting specific prompt sentences into a generative AI model, and for notifying the user of the schedule which is information that is not registered in the schedule and which the user may have forgotten; and means for adjusting the timing of the notification and the content of the notification according to an emotion engine which recognizes the emotion of the user and the emotion.SELECTED DRAWING: Figure 1

Disclosed embodiments may include a system for fraud detection. The system may identify, using a web browser extension, that a user has navigated to a webpage on a user device. The system may receive, via the webpage, data associated with a transaction. Responsive to receiving the data, the system may retrieve search history data corresponding to a searching session associated with the data, and may identify a searching session path corresponding to the transaction. The system may determine, using a machine learning model (MLM) and based on the search history data and the searching session path, a likelihood of fraud associated with the data. The system may determine whether the likelihood exceeds a predetermined threshold. Responsive to determining the likelihood exceeds the predetermined threshold, the system may conduct one or more fraud prevention actions.

To provide an elevator system with which an elevator user can spend time usefully during riding an elevator.SOLUTION: An elevator system includes: video content storage means which stores multiple pieces of video content related to services provided via service providing system; an elevator car in which a camera and a display device are provided; elevator control means which controls operation of the car in accordance with call information registered by a user; and distribution control means which selects, when detecting that the user who has registered the call information is riding alone on the car, on the basis of history information indicating a history of searching of the user on an application or a website provided by the service providing system, video content that suits the taste of the user who registered the call information from the video content storage means, and distributes the video content to the display device.SELECTED DRAWING: Figure 1

The invention relates to the technical field of data asset management, and discloses a data asset intelligent retrieval and visualization display platform, which comprises a data asset intelligent retrieval and visualization system, and the data asset intelligent retrieval and visualization system comprises a central processing unit. The input end of the central processing unit is electrically connected with the output end of a data analysis and mining module; according to the intelligent retrieval and visual display platform for the data assets, a user can quickly position required data resources in manners of keyword search, label screening and the like, the data retrieval efficiency is greatly improved, meanwhile, an intelligent retrieval algorithm can provide personalized search results according to the search history and preference of the user, and the user experience is improved. According to the method and the system, the retrieval accuracy is further improved, the platform performs classified management on the data assets, and a corresponding label is marked on each data set, so that a user is helped to better understand the meaning and the purpose of the data, and a more accurate matching result can be provided during retrieval.

A method for recommending results to a user from a search query is provided. The method includes receiving, in a search engine, a search query for a media file from a user, identifying a style preference of the user associated with a one or more media file attributes, based on a user-related search history, selecting, from a database, a one or more media files based on the search query and the style preference of the user, determining a style preference score for the one or more media files based on the media file attributes, and recommending to the user a top ranked media file based on the style preference score. A system including a memory storing instructions and one or more processors to execute the instructions to cause the system to perform the above method is also provided.

Techniques for generating and presenting search filter recommendations are disclosed. A system determines a composition of search filter tiles, including a relative number of machine learning-generated search filter tiles and non-machine learning-generated search filter tiles, based on recommendation criteria. The recommendation criteria include an amount of user search history data that exists. The system applies search filter recommendation policies to generate a set of search filter recommendations, including hard-coded search filters, search filter recommendations generated by a user-specific machine learning model, search filter recommendations generated by a machine learning model trained on data from multiple different users, and algorithm-based recommendations. The system generates different sets of search filter recommendations made up of different ratios of recommendations from different sources based on determining if search filter criteria are met.

An information processing apparatus includes a memory and at least one processor. The memory stores a learned model having learned correlation between character strings included in a search history of words and / or phrases. The processor inputs information of a search history of words and / or phrases by a target user to the learned model. The processor creates a word and / or a phrase for learning by the target user.

1. The name of the present design product: the area of search bar of the graphical user interface of the key word search of the electronic device. 2. The use of the present design product: for an electronic device. 3. The design points of the present design product: in the claimed part of the graphical user interface. 4. The picture or photo that best shows the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the graphical user interface is used for key word search, and multiple types of fields are selected in the talent search input box for search screening. 7. The human-computer interaction mode of the graphical user interface: the graphical user interface can be interacted by clicking, pressing or sliding the graphical user interface. 8. The change state description of the graphical user interface: design 1 front view is a key word search interface, and position related information can be searched here. When the search box is empty, click the search box to display the drop-down menu of search history. When the mouse is selected and stopped on any historical search data, the search condition of the data is displayed, design 1 front view becomes design 1 change state figure 1; click any historical search button in design 1 change state figure 1 to search, enter design 1 change state figure 2; click any term below to enter design 1 change state figure 3; the label can be one or more, if the content exceeds the size of the search box after selecting multiple labels, it is omitted, such as design 1 change state figure 4; mouse over any label in design 1 change state figure 4, a red × appears on the label, such as design 1 change state figure 5. Design 2 front view is a key word search interface, and position related information can be searched here. When the search box is empty, click the search box to display the drop-down menu of search history. When the search box input content, the drop-down menu displays the search type label and the matching content related to the search content below the label, design 2 front view becomes design 2 change state figure 1; click any search type label button in design 2 change state figure 1, the current input content of the search box is searched as the type corresponding to the label, enter design 2 change state figure 2; click any term below to enter design 2 change state figure 3; the label can be one or more, if the content exceeds the size of the search box after selecting multiple labels, it is omitted, such as design 2 change state figure 4; mouse over any label in design 2 change state figure 4, a red × appears on the label, such as design 2 change state figure 5. 9. Other matters requiring explanation Other matters requiring explanation: The design of the present design product is partially protected by the design office. The part covered by the gray shadow is not claimed, and the rest is claimed.

1. Name of the Design Product: Batch Banned Viewing Graphical User Interface for Electronic Devices. 2. Use of the Design Product: An electronic device. 3. Design Key Points of the Design Product: Lies in the graphical user interface. 4. Picture or Photo Most Indicating the Design Key Points: Variation State Diagram 4. 5. This design product is a flat product, and the left view, right view, top view, bottom view, and rear view are omitted. 6. Human-Computer Interaction Method of the Graphical User Interface: The graphical user interfaces shown in the front view and each variation state diagram are all batch banned viewing interfaces displayed in the electronic device. The front view shows the banned viewing list interface. At the top of the interface, the title of the banned viewing list is displayed. Below the title, the number of banned viewing list items added is shown, and there is a delete button on its right. Below, the banned viewing list is displayed. At the bottom of the interface, a batch banned viewing button is shown, with the text "Want to batch ban? Click here" displayed in the button; click the batch banned viewing button, and Variation State Diagram 1 is shown. On the left side at the top of the interface, a return button is shown. In the middle at the top, a search box is shown, with the prompt "Please enter the keywords included in the banned viewing album" inside. On the right, a search button is shown. On the left side below the search box, search history is shown, and on the right, multiple video cards randomly pushed by the system are shown; enter the album keywords in the search box in Variation State Diagram 1, and Variation State Diagram 2 is shown. A keyboard icon pops up at the bottom of the interface; after entering, click the search button in Variation State Diagram 2, and Variation State Diagram 3 is shown. A select all button is added on the left side of the search bar, and a batch banned viewing button is added on the right. Multiple video cards are pushed below the search bar according to the searched keywords, and there is a selection button in the lower right corner of each video card, which is in the unselected state by default after the search is completed; click the select all button in Variation State Diagram 3, and Variation State Diagram 4 is shown. All videos are in the selected state, and the user can also select some video cards according to needs; click the batch banned viewing button in Variation State Diagram 4, and Variation State Diagram 5 is shown. A batch banned viewing success floating window is shown in the middle of the interface; after the banned viewing is successful, it automatically jumps to Variation State Diagram 6, and the interface shows the banned viewing list, and the banned viewing list is increased in the list. The gray areas in the front view and each variation state diagram are the deleted content screens, and "XX" represents text or numbers. In the actual use process of the front view and each variation state diagram, the interface content shown can be correspondingly referred to each reference diagram.

A system is provided.SOLUTION: A system comprising: means for collecting data such as a photo, a moving image, a message, and a search history captured by a user terminal; means for transmitting the collected data to a server; means for analyzing the data received by the server and specifying an anniversary; means for transmitting a remind notification to the user terminal based on the specified anniversary; and means for proposing a present or making a reservation for a restaurant suitable for the anniversary.SELECTED DRAWING: Figure 1

The invention provides a device and a method for providing trend analysis and sales strategies based on global demand prediction. The electronic equipment comprises a memory and a processor connected to the memory, in the processor, search history information is received through a search word collection API, the API can collect search history records from a preset homepage operated by a target country / region, and the search history records are stored in the memory. And the recommended product and the recommended attribute value of the recommended product can be exported according to the search history information.

The application discloses a kind of strategy deduction method and device based on multi-agent, the application is by constructing character special vector database, integrate relevant background information, and by searching history behavior and injecting generation dynamic prompt word, guide large language model (LLM) Generation response in line with the background and past behavior pattern of specific interest party, to significantly improve the simulation reality of role behavior, so that each role agent based on its values and core position, by analyzing and linking the context of multiple rounds of interaction, autonomously make decisions and respond, to build the simulation of real interest party each party agent, then realize the accurate deduction of construction strategy, that is, the application can effectively guarantee the accuracy of artificial intelligence innovation ecological construction strategy deduction by high-fidelity role position simulation and dynamic evolution game deduction.

The present application discloses a robot control method and a robot. Based on referring to the search history record, the robot control method further improves the efficiency and accuracy of the robot in finding a predetermined target by means of the information of the photometric map and the height map. At the same time, it can also help the robot find a better position that is not easily found by the user among many positions, thereby improving the intelligence of the robot and the user experience.

A document processing method comprising: outputting information for displaying a search keyword input field for searching for, based on an input search keyword, document information stored in a storage unit, a type display region for selectably displaying (i.e., displaying in a selectable manner) a type decided based on the content of the document information, and a search keyword display region for displaying a search keyword based on a search history of the document information together with a number of search results of the document information searched for by the search keyword.

We provide the system. [Solution] A means of collecting and integrating information related to childcare facilities, educational institutions, and medical institutions, A means for processing user search queries based on the aforementioned information and extracting relevant information, A means of sending the extracted information to the user's terminal and displaying it to the user, A means of receiving user feedback and reflecting it in the database, A system that includes means to customize future search results based on the user's search history and feedback information.

The invention discloses a personalized teaching resource recommendation method and system based on Internet big data search analysis, and particularly relates to the technical field of resource recommendation, comprising the following steps: taking teaching resources covering knowledge points searched by a current user in an Internet teaching resource platform as related teaching resources, constructing a knowledge graph of the teaching resource knowledge points, and establishing a knowledge graph of the knowledge points of the teaching resources; the method comprises the following steps: acquiring a portrait of a current search user, determining similar users based on the portrait of the current search user, performing training by using historical data of the similar users through a collaborative training method, constructing an autoregression model of knowledge points, acquiring search historical data information of related teaching resources, defining direct click behaviors of the users, and acquiring search behavior information of the related teaching resources; according to the method, the search history data information and the search behavior information of the related teaching resources are comprehensively evaluated, the priority of the related teaching resources is quantified, the teaching resources suitable for the user can be recommended, and the content most related to the learning requirement of the user can be quickly screened out from a large number of teaching resources.

A system is provided.SOLUTION: A system comprising: means for collecting a plurality of data, such as user behavior data, purchase history, search history, and the like; means for analyzing the data to identify a user's interests and preferences; means for feeding data into a generative AI model based on the analysis to generate optimized text and images for the user; and means for automatically creating a landing page by placing the generated text and images.SELECTED DRAWING: Figure 1

1. The name of the design product: conversation topic suggestion module of artificial intelligence conversation graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the protected part of the graphical user interface. 4. The picture or photo that best shows the design points: front view. 5. The use of the graphical user interface: for artificial intelligence conversation; the use of the protected part is to display recommended conversation topics that the user may be interested in in the form of voice bubbles based on the user's movement data and search history. 6. The human-computer interaction mode of the graphical user interface: after the user selects the artificial intelligence conversation mode by clicking or touching, the interface displays recommended conversation topics that the user may be interested in in the form of voice bubbles based on the user's movement data and search history, and when the user selects one of the voice bubbles, the function described in the selected voice bubble is executed. 7. Other circumstances that need to be explained: this design belongs to partial protection design, and the dotted line part does not belong to the protected content.

An object of the system according to the embodiment is to provide appropriate support to young people and students at an early stage.SOLUTION: A system includes an analysis part, a support part, a notification part, and a data cooperation part. The analysis unit analyzes an utterance or a search history of a user. The support unit provides active support based on the content detected by the analysis unit. The notification unit transmits an SOS notification to a counselor or an expert when it is difficult for the support unit to deal with the problem. The data cooperation unit acquires necessary data in cooperation with another data source.SELECTED DRAWING: Figure 1

1. Name of the product in this design: Graphical User Interface for Retrieving Records of Electronic Devices. 2. Purpose of this design: for running or operating procedures. 3. The key design feature of this product is its graphical user interface. 4. The image or photo that best illustrates the design points: Interface change state diagram 1. 5. Purpose of the graphical user interface: The graphical user interface of the product is the graphical user interface used when operating the record retrieval function of the Qingce Zhihai system. 6. Description of the changing states of the graphical user interface: The main view is the initial interface after the user logs into the system; clicking the "Search History" option in the main view will jump to the interface changing state diagram 1. 7. Other situations requiring explanation: Other explanation: The "×" sign in the view represents text and / or numbers.

The embodiment of the invention provides an electronic note optimization method and system based on a knowledge graph, in the optimization method, historical behavior data of a user in a note system is acquired, and the historical behavior data comprises note content, search history, collected notes and test scores; the historical behavior data and the user self-evaluation data are combined, a comprehensive evaluation model used for cutting a domain knowledge graph is constructed, and the domain knowledge graph is automatically generated based on the domain to which the current note text of the user belongs; and taking the clipped domain knowledge graph as a reference knowledge graph, and optimizing the current note text content of the user based on the reference knowledge graph. According to the electronic note optimization method provided by the embodiment of the invention, the technical problem that a user may miss semantic correlation but does not directly mention the note content of a word when searching the keyword due to the fact that a traditional electronic note system lacks mining of internal association of knowledge and cannot automatically identify and associate the hierarchy and causal relationship between concepts can be solved.

The present invention provides a server device, a learning assistance method, and a recording medium, which can immediately and easily confirm and effectively learn to what extent a sentence set by oneself as a learning object is searched by other users. When a user attribute of a summary object is set in a communication terminal, a user specifies a sentence and instructs "sentence search", a learning assistance server (10) searches the specified sentence with an encyclopedia, and determines whether the specified sentence is included in the top of a search ranking and whether the specified sentence is included in the top of an average search frequency ranking based on search history data of the summary object. When the specified sentence is included in the top of the search ranking, the server (10) displays a message (M1) notifying of the top of the search ranking in the summary object together with a search result of the specified sentence in the communication terminal (20), and when the specified sentence is included in the top of the average search frequency ranking, a message (M2) notifying of the top of the average search frequency ranking in the summary object is displayed.