18232144. GENERATIVE SUMMARIES FOR SEARCH RESULTS simplified abstract (Google LLC)

GENERATIVE SUMMARIES FOR SEARCH RESULTS

Organization Name

Google LLC

Inventor(s)

Matthew K. Gray of Reading MA (US)

John Blitzer of Mountain View CA (US)

Corinn Herrick of Mountain View CA (US)

Srinivasan Venkatachary of Sunnyvale CA (US)

Jayant Madhavan of San Francisco CA (US)

Sam Oates of Cambridge MA (US)

Phiroze Parakh of San Jose CA (US)

Aditya Shah of Mountain View CA (US)

Mahsan Rofouei of Menlo Park CA (US)

Ibrahim Badr of Zurich (CH)

GENERATIVE SUMMARIES FOR SEARCH RESULTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18232144 titled 'GENERATIVE SUMMARIES FOR SEARCH RESULTS

Simplified Explanation: The patent application discusses the use of a large language model (LLM) to generate natural language (NL) summaries in response to queries. Additional content can also be processed using the LLM to improve the accuracy and specificity of the summaries.

• Utilizing a large language model (LLM) in generating natural language (NL) summaries
• Processing additional content using the LLM to improve accuracy and specificity
• Mitigating inaccuracies and over/under-specification in NL summaries

Key Features and Innovation: - Integration of LLM in NL summary generation - Processing additional content for improved accuracy - Mitigating issues in NL summaries

Potential Applications: The technology can be applied in search engines, chatbots, and information retrieval systems to provide more accurate and specific summaries in response to user queries.

Problems Solved: - Inaccuracies in NL summaries - Over-specification and under-specification in NL summaries

Benefits: - Improved accuracy and specificity in NL summaries - Enhanced user experience in information retrieval systems

Commercial Applications: The technology can be used in search engine optimization tools, customer service chatbots, and content recommendation systems to provide more relevant and accurate information to users.

Questions about the Technology: 1. How does the use of a large language model improve the accuracy of natural language summaries? 2. What are the potential drawbacks of processing additional content using the LLM in NL summary generation?

Frequently Updated Research: Ongoing research in natural language processing and machine learning may lead to advancements in the use of large language models for generating NL summaries.

Original Abstract Submitted

At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.