Amazon patents layered AI to shrink user prediction models
Amazon describes a machine learning system split into two levels that predicts what products a user might buy. The top level looks at general user data to guide the bottom level, which analyzes specific past actions, allowing the system to run with less data and shorter training times.
In the filing’s own words
Embodiments of a user behavior inference system are disclosed that uses a multi-level machine learning (MLML) model to make inferences about user behaviors. The MLML model is organized in multiple levels, where an upper level processes part of the model input to generate control parameters for a lower level, and the lower level processes another part of the model input according to the control parameters to generate inference results. In embodiments, the upper level receives a user feature vector of a user and the lower level receives a behavior feature vector of the user. In embodiments, the model is trained to output a propensity score of the user for acquiring an item, and an item feature vector is included in the model input. Advantageously, the multi-level model architecture reduces the overall size of the model and the amount of training time and data needed to achieve a desired model performance.
What exactly they claim (claim 1)
1. A system, comprising: one or more computer devices that implement a user behavior inference system, configured to: execute a multi-level machine learning (MLML) model trained using one or more ML techniques to generate inferences about user behaviors, wherein the MLML model is organized in multiple levels according to a multi-level model architecture that includes an upper level and a lower level; receive (a) a user feature vector encoding user features of a user and (b) a behavior feature vector encoding a behavior sequence of the user; input the user feature vector to the upper level of the MLML model, wherein the upper level processes the user feature vector to output control parameters of the lower level without processing the behavior feature vector; input the behavior feature vector to the lower level of the MLML model, wherein the lower level of the MLML model processes the behavior feature vector according to the control parameters produced by the upper level of the MLML model based on the user feature vector, to generate a behavior inference about the user; and wherein the multi-level model architecture (a) limits the MLML model to a specified model size and (b) reduces an amount of training time or training data used to train the MLML model to a specified performance level.
Facts
- Company
- Amazon (Amazon Technologies, Inc.)
- Official title
- Multi-level model architecture for controlling the size of user behavior models
- Patent number
- US12749092B1
- Application number
- 18542458
- Filed
- Dec 15, 2023
- Granted
- Sep 29, 2026
- Inventors
- 4
- Claims
- 20
Classified under
G06Q 30/0254 Physics & computingMore from Amazon 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.