Google llc (20240220735). GENERATIVE SUMMARIES FOR SEARCH RESULTS simplified abstract

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 20240220735 titled 'GENERATIVE SUMMARIES FOR SEARCH RESULTS

Simplified Explanation: The patent application discusses the use of a large language model (LLM) to generate natural language summaries in response to queries, with the potential to process additional content beyond the query itself.

Key Features and Innovation:

• Utilization of a large language model (LLM) in generating natural language summaries
• Processing of additional content beyond the query to improve summary accuracy
• Mitigation of over-specification and under-specification in the generated summaries

Potential Applications: The technology can be applied in various fields such as information retrieval, content summarization, and question-answering systems.

Problems Solved: The technology addresses issues related to inaccurate and overly detailed or vague natural language summaries.

Benefits:

• Improved accuracy in natural language summaries
• Enhanced efficiency in processing additional content
• Better user experience in query-based information retrieval systems

Commercial Applications: The technology can be utilized in search engines, chatbots, virtual assistants, and other natural language processing applications to enhance user interaction and information retrieval.

Prior Art: Readers can explore existing research on large language models, natural language processing, and information retrieval systems for related prior art.

Frequently Updated Research: Stay updated on advancements in large language models, natural language processing, and information retrieval technologies for further insights into this field.

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 limitations of processing additional content beyond the query in generating 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.