Microsoft technology licensing, llc (20240185093). CONTEXTUAL-BASED KNOWLEDGE ENTITY SUGGESTIONS simplified abstract
Contents
CONTEXTUAL-BASED KNOWLEDGE ENTITY SUGGESTIONS
Organization Name
microsoft technology licensing, llc
Inventor(s)
Sebastian Johannes Blohm of Munich (DE)
Nikita Voronkov of Bothell WA (US)
Hadi Kotaich of Bellevue WA (US)
Dion Stephan Javellana Ong of New Haven CT (US)
CONTEXTUAL-BASED KNOWLEDGE ENTITY SUGGESTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185093 titled 'CONTEXTUAL-BASED KNOWLEDGE ENTITY SUGGESTIONS
The patent application describes a data processing system that allows users to tag topics from a knowledge base based on textual context input.
- Receiving textual context in a user interface element.
- Receiving an indicator after the textual context to tag a topic.
- Encoding the textual context using a machine-learning model to generate representations.
- Decoding the representations to generate tokens corresponding to topics and characters.
- Identifying recommended topics for display in a user interface element.
- Allowing users to select recommended topics for tagging.
Potential Applications: - Content tagging in social media platforms. - Automated topic suggestions in writing applications. - Enhancing search functionality by tagging topics in documents.
Problems Solved: - Streamlining the process of tagging topics in textual content. - Improving user experience by providing relevant topic suggestions. - Increasing efficiency in content organization and retrieval.
Benefits: - Saves time for users by automating topic tagging. - Enhances content visibility and searchability. - Improves accuracy in topic identification and tagging.
Commercial Applications: Title: Automated Topic Tagging System for Content Management This technology can be utilized in content management systems, social media platforms, and writing applications to streamline the process of tagging topics in textual content, improving user experience and efficiency in content organization.
Prior Art: No prior art information provided.
Frequently Updated Research: No information on frequently updated research related to this technology provided.
Questions about Automated Topic Tagging System for Content Management:
Question 1: How does the machine-learning model determine the representations of textual context? Answer: The machine-learning model processes the textual context to generate representations that reflect the meanings of the input text, allowing for accurate topic tagging.
Question 2: Can users customize the recommended topics displayed for tagging? Answer: The system identifies recommended topics based on the input text and knowledge base, but users may have the option to customize or add their own topics for tagging.
Original Abstract Submitted
a data processing system implements receiving a textual context inserted into a user interface element; receiving an indicator inserted into the user interface element after the textual context, the indicator indicating a desire to tag a topic from a plurality of topics included in a knowledge base; receiving one or more textual character inserted into the user interface element after the indicator; encoding, using a machine-learning (ml) model, the received textual context to generate at least one representation reflecting one or more meanings of the received textual context; decoding, using the ml model, the at least one representation to generate a plurality of tokens in response to the one or more meanings of the received textual context, the plurality of tokens corresponding with the at least one textual character and at least one of the topics of the plurality of topics; identifying one or more topics from the plurality of topics as recommended topics; and providing the identified recommended topics for display in a topic selection user interface element that enables selection of one recommended topic for insertion as the tag in the user interface element.
