Patents
Literature
Patsnap Eureka AI that helps you search prior art, draft patents, and assess FTO risks, powered by patent and scientific literature data.

6 results about "Content Model" patented technology

A structured representation of a collection of items. In the Content Classification System, a content model includes an ontology comprised of a hierarchical taxonomy of content categories, content types, data properties, and annotation properties.

Systems, apparatuses, methods, and computer program products for generating responsive action programs

A method provided herein includes accessing, using a monitoring engine, a computing file provided to a second computing device via a computing file exchange platform. In some embodiments, the method includes identifying, using the computing file and a computing file content model, a content deficiency associated with the computing file. In some embodiments, the method includes generating, using the content deficiency and the computing file content model, a responsive action procedure. In some embodiments, the method includes generating a responsive action procedure link interface element. In some embodiments, the responsive action procedure link interface element includes a responsive action procedure link representation corresponding to the responsive action procedure. In some embodiments, the method includes causing the responsive action procedure link interface element to be rendered to an interface associated with the computing file exchange platform.
Owner:HAND HELD PRODS INC

Content model construction and unification engine execution method, system and apparatus

This invention relates to the field of B2B foreign trade website building technology, and in particular to a content model construction and unified engine execution method, system, and device. The method includes: the client configuring fields of the content model; the server constructing the fields into a tree-like field structure according to hierarchical relationships, storing the tree-like field structure as metadata, and assembling a model definition object based on the read metadata; the client recursively rendering the form interface based on the model definition object; the server generating data validation rules based on the configuration parameters in the metadata, and validating the form data submitted by the client based on the data validation rules; the server storing the validated form data according to the field attributes in the metadata, and performing state management, soft deletion, and sorting processing on the content data. This invention can solve the problems of high coupling between model and code, complex field nesting, difficulty in expansion, code redundancy, and difficulty in ensuring consistency of state across multiple modules in existing technologies.

A method for processing document abstract content based on XML fragmentation

The application discloses a kind of based on XML fragmentation literature abstract content processing method, including the literature being converted into XML format is divided into fragmented data unit, according to four kinds of mode of chapter, chapter, section and theme constitutes data content model, data content model is dynamically associated by key word semantic relation, extract fragmented data application unit in key word and subject content unit, content unit is dynamically reorganized according to literature abstract demand.The application has beneficial effect that metadata automatic indexing accuracy is >95%, text Xml automatic annotation accuracy is >90%, can effectively improve the prerequisite of target extraction accuracy under the condition of reducing the cost of text decomposition.
Owner:南方电网能源发展研究院有限责任公司

Machine learning modeling to identify sensitive data

Methods and systems herein identify and redact personally identifiable information. A PII sensitivity detection framework includes multiple layers where each layer corresponds to a computer model. The framework analyzes data stored within different data tables and predicts whether a data column includes PII. The first layer corresponds to an artificial intelligence model that analyzes each column metadata and predicts a first score indicative of a likelihood of PII. The second layer corresponds to a rule-based computer model that uses various rules to determine a second score indicative of a likelihood of PII for each column. The third layer corresponds to a column content model that analyzes content of each column using various natural language processing techniques to generate a third score indicative of a likelihood of PII. The framework masks data being presented to a user based on the scores generated via execution of one or more of the layers.
Owner:CITIBANK N A

Model training methods and apparatus, content generation methods and apparatus

ActiveCN117217333BEngineeringContent Model
This specification discloses a model training method and apparatus, and a content generation method and apparatus. The training method includes: generating first content that conforms to the content requirements of the original input information using a content model; and training a style model based on the first content and the original input information to further generate second predicted content that conforms to the style requirements based on the first content. The content generation method includes: generating target content that conforms to the content and style requirements of the input information using a content model and a style model, based on the input information.
Owner:ALIPAY (HANGZHOU) INFORMATION TECH CO LTD

A method for constructing a dynamic hierarchical zoning resource model of graphite

This invention discloses a method for constructing a dynamic graded and zoned graphite resource model, comprising the following steps: Step 1: Obtaining basic information data of the graphite resource system, including geographical location information of the graphite resource distribution area, including longitude and latitude; Step 2: Dividing the graphite resources into zones based on their location; Step 3: Obtaining the storage volume of graphite resources in different zones, establishing a database for storage, and further classifying the zones according to the existing content of graphite resources; Step 4: Establishing an existing content model based on the graphite resource content of the graded zones; Step 5: Obtaining the consumption volume of graphite resources in the graded zones over the past ten years, as well as the methods of graphite consumption, and establishing analysis models for different zones; Step 6: Coupled with the existing content model and the analysis model to obtain a dynamic model of graphite resource graded and zoned distribution, reflecting the content curves of graphite resources in different zones over the next ten years, as well as the methods of graphite consumption.
Owner:CHINA MINMETALS GRP (HEILONGJIANG) GRAPHITE IND CO LTD