Google patents method to estimate online model results using offline model
Google describes a method for creating an offline machine learning model that approximates the output of an existing online model. The offline model is trained by comparing its predictions to the online model's results, adjusting its internal weights to match the online output despite using fewer input parameters. This allows for faster evaluation of digital components without running the full online model.
In the filing’s own words
This specification relates to generating an offline model that approximates the output of an online model. Methods can include constructing a set of query-component pairs. Each query-component pair includes a query and a digital component. An offline model is trained to generate an output that approximates the output of an existing online model. The online model is configured to generate the online output using at least one input parameter that is not used by the offline model. In training, feature weights of the offline model are adjusted based on a difference between the offline outputs and corresponding online outputs that have been generated for digital components by the online model and using the at least one input parameter that is not used by the offline evaluation model. Distribution of digital components is adjusted based on offline output predictions generated after adjusting the feature weights of the offline model.
What exactly they claim (claim 1)
1. A method, comprising: obtaining a training set of digital components; obtaining a set of queries submitted over a past time period; constructing a set of query-component pairs, wherein each query-component pair in the set of query-component pairs includes a query from the set of queries and a digital component from the set of digital components; training an offline evaluation model to generate an offline output that approximates an online output of an existing online production model, wherein the existing online production model is configured to generate the online output using at least one input parameter that is not used by the offline evaluation model to generate the offline output, including: generating, using the offline evaluation model, the offline output for each query-component pair in the set of query-component pairs to obtain a first set of offline output predictions; and adjusting feature weights of the offline evaluation model based on a difference between the offline outputs in the set of offline output predictions and corresponding online outputs that have been generated for the set of digital components by the existing online model and using the at least one input parameter that is not used by the offline evaluation model; and adjusting a distribution of one or more digital components based on a second set of offline output predictions generated after adjusting the feature weights if the offline evaluation model.
Facts
- Company
- Google (GOOGLE LLC)
- Official title
- EFFICIENT ESTIMATION OF ONLINE MACHINE LEARNING MODEL RESULTS USING AN OFFLINE MACHINE LEARNING MODEL
- Publication number
- US20260320374A1
- Application number
- 18861640
- Filed
- Dec 27, 2023
- Published
- Oct 15, 2026
- Inventors
- 2
- Claims
- 19
Classified under
G06N 3/08 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.