Google patents method for training search AI on user session data
Google has filed a patent application describing a method for training neural networks using data from user interactions with a search engine. The system creates training sequences by arranging search queries, the resulting search results, and the user's subsequent actions into specific blocks. These sequences are then used to optimize the neural network's parameters during the training process.
In the filing’s own words
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using training data generated from user session data are described. In one aspect, a method includes obtaining session data that describes a user sessions. Each user session represents a corresponding interaction between a user and a search engine that occurs during a period of time. A training sequence is generated from the session data. For each user session, the training sequence includes subsequence blocks arranged one after another in a predetermined order. A neural network is trained, including learning parameter values of the neural network, by using the training sequences.
What exactly they claim (claim 1)
1. A method performed by one or more computers, the method comprising: obtaining session data that describes a plurality of user sessions, each user session representing a corresponding interaction between a user and a search engine that occurs during a period of time; generating, from the session data, a training sequence for each of the plurality of user sessions, the training sequence comprising a plurality of subsequence blocks arranged one after another in a predetermined order, each subsequence block comprising (i) a search query subsequence that represents a search query submitted by the user to the search engine, (ii) a search result subsequence that represents one or more resources identified by the search engine based on the search query, and (iii) a user action subsequence that represents one or more actions performed by the user in response to the one or more identified resources; and training a neural network including learning parameter values of the neural network by using the plurality of training sequences based on optimizing a pre-training objective function.
Facts
- Company
- Google (Google LLC)
- Official title
- TRAINING NEURAL NETWORKS ON SESSION DATA
- Publication number
- US20260300752A1
- Application number
- 19489090
- Filed
- Oct 10, 2024
- Published
- Oct 1, 2026
- Inventors
- 9
- Claims
- 25
Classified under
G06N 3/096 Physics & computingG06F 16/953 Physics & computingMore from Google in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.