20 results about "Content type" patented technology

A method and system for enhancing information retrieval and understanding in language models when processing heterogeneous documents containing text, tables, figures, formulas, and images is described. The method involves traversing a document to identify specific types of content and using a language model to generate text descriptions of the identified content, including detailed interpretations. These descriptions are stored in a storage means, indexed by content type. During retrieval, the language model references these descriptions to enhance its understanding and analysis of the document, improving accuracy and contextual relevance. The system comprises a document traversing module, a language model, a storage means, and an information retrieval module, with a feedback loop for continuous improvement based on user feedback. This invention addresses limitations in current retrieval-augmented generation applications by improving the interpretation of complex technical content, thereby enhancing the performance of language models.

The invention provides a watermark embedding method, computer equipment and a computer readable storage medium, and the method comprises the steps: positioning a watermark position in a target text based on a preset watermark position positioning rule; based on the content type at the watermark position, determining a watermark setting type to which the watermark position belongs, adopting a watermark setting strategy corresponding to the watermark setting type to determine watermark information of the watermark position and an arrangement mode of the watermark information; and generating a cue word by using the target text, the watermark position, the watermark information and the arrangement mode of the watermark position and a preset watermark, taking the cue word as input information of the large oracle model, and outputting a watermark text corresponding to the target text through the large language model. According to the technical scheme, the hidden marking and copyright protection of the text content are realized through the deep cooperation of the multi-dimensional watermark setting strategy and the large language model.

The invention provides a project code processing method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, large models and the like. According to the specific implementation scheme, first prompt information is constructed based on a reference file of a project code; the first prompt information comprises to-be-generated content type information; and inputting the first prompt information into a large model to obtain output project code information generated for the content type information.

This application discloses a method, apparatus, storage medium, and computer device for visual environment detection. The method includes: in response to receiving twin model update information corresponding to a target park, generating a content area distribution map corresponding to each content type based on key events within a target time period, the frequency of occurrence of key events, the number of times the digital twin model corresponding to each sub-area is displayed, and key content tags under the content type; generating a prediction model corresponding to each area based on the different importance of each area in the content area distribution map, wherein each prediction model shares a common model module; determining a sequence of visual environment detection distribution maps under the content type corresponding to the content area distribution map using the prediction model set; generating a corresponding twin model based on the sequence of visual environment detection distribution maps; and in response to receiving twin model display information for at least one content type, overlaying and displaying the corresponding at least one twin model.

A system includes a hardware processor, and a memory storing a software code and at least one machine learning (ML) model trained to distinguish between a plurality of content types. The hardware processor executes the software code to receive a content file including data identifying a dataset contained by the content file as being a first content type of the plurality of content types; predict, using the at least one ML model and the dataset, based on at least one image parameter, a first probability that a content type of the dataset matches the first content type identified by the data; and determine, based on the first probability, that the content type of the dataset (i) is the first content type identified by the data, (ii) is not the first 10 content type identified by the data, or (iii) is of an indeterminate content type.

The invention relates to a model training method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining first object operation data and second object operation data; according to the content type of the second historical media content, determining intersection sample objects corresponding to at least one preset content type from the first sample object and the second sample object; based on the third object operation data and the fourth object operation data, performing operation continuity analysis on each preset content type to obtain target operation index data corresponding to each preset content type; and based on object operation data corresponding to at least one target content type in the first object operation data and the second object operation data, training a media content recommendation model corresponding to the target application to obtain a target recommendation model corresponding to the target application. According to the embodiment of the invention, the recommendation accuracy of the target recommendation model can be improved, and the recommendation effect of the target recommendation model is improved.

The invention discloses an online self-media interaction management system and method based on artificial intelligence, and belongs to the technical field of interaction management. After user authorization, extracting starting time, ending time and browsed content types of the user during historical online activities; constructing a browsing content category set and an activity information association library; calculating the duration ratio of the content type in the historical online activity, and constructing a content type-duration ratio coordinate system and a content ratio fluctuation curve; obtaining high-frequency content types corresponding to wave crests of the content proportion fluctuation curve, and constructing a high-frequency content type set; and calculating the occurrence probability of the high-frequency content type when the user performs the next online activity, presetting a threshold value, and analyzing and recommending the content type. According to the method, through combination of historical behavior analysis, time sequence modeling and intelligent recommendation technologies, in-depth insight of user behaviors and optimization of interactive experience are achieved, and interactivity and attraction of online self-media are effectively enhanced.

The present disclosure provides a large model-based project code processing method and device and related equipment, relating to the technical field of artificial intelligence, in particular to the technical field of deep learning, large model, etc. The specific implementation scheme is: constructing a first prompt information based on the reference file of the project code; the first prompt information includes content type information to be generated; inputting the first prompt information into a large model to obtain output project code information generated for the content type information.

The embodiment of the invention provides a content recognition method and device, equipment, a readable storage medium and a program product, and relates to the fields of content recognition, maps and the like, and application scenes include but are not limited to content recognition scenes. The method comprises the following steps: acquiring to-be-identified content; performing content identification on the to-be-identified content to obtain a target label set corresponding to the to-be-identified content; based on the target label set, a preliminary recognition result of the to-be-recognized content is determined, and the preliminary recognition result represents that the content type of the to-be-recognized content is a non-target type, a target type or a suspected target type; if the preliminary recognition result represents that the content type of the to-be-recognized content is a non-target type or a target type, determining the preliminary recognition result as a target recognition result of the to-be-recognized content; and if the preliminary identification result represents that the content type of the to-be-identified content is a suspected target type, performing classification processing on the to-be-identified content through a trained category identification model to obtain a target identification result of the to-be-identified content.

The embodiment of the invention provides a content recommendation method and device and a recommendation network training method and device, and the method comprises the steps: obtaining object related information of a target object, and extracting an initial object feature based on the object related information, performing feature extraction on the initial object feature and the type feature of the content type corresponding to the expert core network based on a feature extraction module in each expert core network to obtain a first object feature of the target object corresponding to the content type; based on the correlation between the content feature of each candidate recommendation content and the type feature of each content type, weighting the first object feature, corresponding to each content type, of the target object to obtain the target object feature, corresponding to the candidate recommendation content, of the target object; and on the basis of target object features and the content features of the candidate recommended content, predicting the probability of interest of the target object in the candidate recommended content to determine a recommended content set of the target object. Based on the method, the accuracy of content recommendation is improved.

The present application provides a method and system for supporting online editing and automatic generation of user manuals, which relates to the technical field of software development. The method includes: obtaining user input data content; converting the data content into multiple data items, the data items including type values ​​and value values; judging whether the type type of the data item is a content type according to the type value; if the type type is determined to be a content type, converting the value value into a DOM tree; according to preset rules, converting the DOM tree into a Markdown document. If the type type is determined to be a title type, converting the value value into a Markdown document according to the title level of the data item. The present application can automatically convert documents into computer syntax to improve the efficiency of user manual generation.

The present invention discloses a content authority management method and device, and relates to the field of computer technology. A specific implementation of the method includes: analyzing content publishing needs, determining content types, obtaining content models corresponding to the content types and authority dimensions corresponding to the content models; in response to a content publishing operation, receiving content filled in the content model and authority items selected in at least one authority dimension, constructing an association relationship between the content and the authority items and storing them in an ES index; receiving a query instruction, obtaining user authority features in the query instruction, searching for content corresponding to the user authority features in the ES index and displaying them. This implementation can select and combine multiple different authority dimensions and authority items, and use ES technology to achieve efficient retrieval and access to content that meets user authority features, thereby achieving personalization and diversification of content retrieval.

A system is provided.SOLUTION: A system comprising: means for inputting content, a format, and a style of content desired to be generated by a user in text; means for analyzing the input information and specifying a type of content to be generated; means for selecting and initializing an optimal generative artificial intelligence model based on the specified type of content; means for generating content using the initialized generative artificial intelligence model; and means for providing the generated content to the user.SELECTED DRAWING: Figure 1

A device may identify standard parameters and real-time parameters associated with content of a content type, and may process the content type, the standard parameters, and the real-time parameters, with a parameter unification model, to generate derived parameters for the content. The device may process the derived parameters and the content type, with a multi-level linear regression machine learning model, to calculate a content score for the content, and may process the derived parameters and the content score, with a linear regression machine learning model, to calculate a quantity of f-NFTs to generate for the content and a divestment ratio. The device may create a unique reference to the content, and may create an NFT for the content based on the unique reference. The device may generate the quantity of f-NFTs for the content based on the NFT, and may provide the quantity of f-NFTs to a content exchange.

The embodiment of the application provides a kind of enterprise content management data processing method and device, method includes: through three-tier architecture model, including library type layer, content type layer and content instance layer, respectively for defining business scenario, dynamically defining content type in library and storing specific content data, library type layer and content type layer are respectively associated with independent configuration table, and the dynamic expansion of business scenario and content type can be realized by inserting configuration data;Unified content storage table is constructed, and all content data is uniformly stored in single table by taking library type identifier and content type identifier as logical isolation label, without predefining data structure to support flexible content type;Meanwhile, a general processing interface decoupled from the library type and the content type is constructed, forming a highly reusable capability framework, enabling unified processing and reuse of all content data in different business scenarios, which can improve the efficiency of enterprise content management and reduce implementation costs.

The invention relates to a content generation optimization method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining condition information of target content output and generated by a user; obtaining a first generation content corresponding to the content type and a first evaluation value of the first generation content through a first part model in a preset collaborative architecture by taking the condition information as a generation target; under the condition that the first evaluation numerical value exceeds a set range, converting the first evaluation numerical value into a correction parameter, inputting the correction parameter into the first part model, continuing to execute the first part model in the preset collaborative architecture, and generating the first part model by taking the condition information as a generation target; and obtaining a first generated content corresponding to the content type and a first evaluation value of the first generated content until a second evaluation value of a second generated content output by the first partial model is within the set range.

The application discloses a content distribution method and device, electronic equipment and storage medium, comprising: obtaining to-be-distributed content, distribution account and content type corresponding to content published by the distribution account; determining distribution information of the distribution account on the content based on the content type and the number of the content published by the distribution account; collecting a reference account list and historical content published by each reference account in the reference account list in a historical time period from a content distribution system to which the distribution account belongs; calculating content originality of the distribution account based on the distribution information, similarity between the to-be-distributed content and each historical content and the account identifier of the distribution account; and distributing the to-be-distributed content when the calculated content originality is greater than a preset value, which can improve the efficiency of content review.

A system comprising at least one server computer, said at least one server computer configured to perform a method for activating editing functionality in a content management system (CMS), said method including the following operations: the CMS providing a content management application programming interface (CMA) enabling management of a content project, the content project having a content model defined by a plurality of content types, each content type defining one or more fields, said content types defining structures for content managed via the CMS; responsive to an application programming interface (API) call to the CMA, then assigning one or more annotations to one or more of the content types; wherein for the content types having annotations assigned thereto, the assigned annotations enable an editing functionality of an editing application interfaced with the CMS.

Processing historical browsing content with an electronic device which can generate a first user operation page of a target application; in response to a historical reference operation triggered via the first user operation page, generate a browsing record page including a historical content area, one historical content area corresponding to one content type, and each historical content area including first content description information; and in response to a presenting operation for first content description information selected from the historical content area, generate target historical browsing content corresponding to the selected first content description information, and therefore the first content description information of the historical browsing content under each content type may be provided in a centralized mode through a browsing record page, so that content desired to be browsed again may be found in a short time, improving the search efficiency for the historical browsing content.

The embodiments of the present application disclose a content recommendation method, apparatus, computer device, and computer-readable storage medium. The method comprises obtaining a data change message for recommended content in a content recommendation list, wherein the content recommendation list includes at least one recommended content sorted according to a preset rule; parsing the data change message to obtain the content type of the recommended content in the content recommendation list; obtaining a status list corresponding to the content recommendation list when the content type is a specified content type; performing corresponding adjustments to the recommended status of the recommended content in the content recommendation list to obtain an adjusted status list; and performing content recommendation processing on the content recommendation list based on the adjusted status list. This solution determines the content type of the recommended content based on the message generated by target content identification and adjusts the status list accordingly. This solution can de-recommend recommended content in the content recommendation list that belongs to the specified content type, thereby improving the effectiveness of content recommendation.