18491874. CONSTRUCTING ANSWERS TO QUERIES THROUGH USE OF A DEEP MODEL simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 CONSTRUCTING ANSWERS TO QUERIES THROUGH USE OF A DEEP MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONSTRUCTING ANSWERS TO QUERIES THROUGH USE OF A DEEP MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
CONSTRUCTING ANSWERS TO QUERIES THROUGH USE OF A DEEP MODEL
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Xiaojian Wu of Fremont CA (US)
Doran Chakraborty of San Jose CA (US)
Hyun-Ju Seo of Los Gatos CA (US)
Gangadharan Venkatasubramanian of Redmond WA (US)
CONSTRUCTING ANSWERS TO QUERIES THROUGH USE OF A DEEP MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18491874 titled 'CONSTRUCTING ANSWERS TO QUERIES THROUGH USE OF A DEEP MODEL
Simplified Explanation
The abstract describes a technology that uses a deep model to construct answers to queries in list form based on relevant webpage content received from a search engine.
- Deep model technology: Utilizes a deep model to analyze and process webpage content for constructing answers to queries.
- List format answer: Generates answers in list form, consisting of a header and list elements.
- Relevant webpage content: Receives content from webpages deemed relevant by a search engine for constructing answers.
- Query-based answer construction: Constructs answers upon receiving queries, utilizing the deep model to process and generate responses.
Potential Applications
This technology could be applied in search engines, chatbots, and question-answering systems to provide accurate and structured answers to user queries.
Problems Solved
1. Efficient answer generation: Helps in quickly generating answers to user queries by analyzing relevant webpage content. 2. Structured response format: Provides answers in a list format, making it easier for users to consume information.
Benefits
1. Improved user experience: Enhances user satisfaction by providing concise and relevant answers to queries. 2. Time-saving: Saves time for users by presenting information in a structured list format.
Potential Commercial Applications
"Enhancing User Experience with Structured Answers: Applications in Search Engines and Chatbots"
Possible Prior Art
There may be prior art related to deep learning models for answer generation in natural language processing tasks, as well as technologies for extracting information from webpages to provide answers to user queries.
Unanswered Questions
How does this technology handle multi-part queries?
This technology focuses on constructing answers in list form based on relevant webpage content, but it is unclear how it handles queries that require multi-part responses or complex information retrieval.
What is the scalability of this technology for handling a large volume of queries?
While the abstract mentions the construction of answers to queries, it does not address the scalability of the deep model technology in processing a high volume of queries simultaneously.
Original Abstract Submitted
Various technologies relating to constructing an answer to a query are described herein, wherein the answer is in list form. The answer includes a header and a list element. A deep model receives content of a webpage that is deemed relevant to the query by a search engine and constructs the answer to the webpage upon receipt of the query.
