Amazon patents system that updates AI context while generating answers
This filing describes a method where a computer system watches the words an AI model generates in real-time. If the AI produces specific keywords or reaches a certain point, the system automatically fetches new, relevant information and feeds it back into the model. This allows the AI to adjust its answer mid-stream using fresh data instead of relying only on the initial instructions.
In the filing’s own words
Systems and methods are provided to dynamically adjust contextual data of a machine learning model during an inference process of the machine learning model. A system can identify one or more tokens generated by the machine learning model. The machine learning model may, during an inference process, generate the one or more tokens based on first contextual data. The system can determine that the one or more tokens satisfy one or more first parameters. The system can obtain second contextual data associated with the one or more tokens based on determining that the one or more tokens satisfy the one or more first parameters and can provide the second contextual data to the machine learning model. The machine learning model may generate an output based on the second contextual data and at least a portion of the first contextual data.
What exactly they claim (claim 1)
1. A system comprising: data processing hardware; and memory in communication with the data processing hardware, the memory storing instructions, wherein execution of the instructions by the data processing hardware causes the data processing hardware to: obtain, from a user computing device, an input, wherein the input comprises a request to generate an output using a machine learning model; provide, to a computing system, a prompt for a machine learning model, wherein the computing system implements the machine learning model according to the prompt and first contextual data associated with the machine learning model; obtain, in real time, a first data unit of a data stream, wherein the machine learning model generates the data stream as part of an inference process of the machine learning model based on implementation of the machine learning model according to the prompt and the first contextual data; determine that the first data unit satisfies one or more first parameters associated with adjustment of the first contextual data, wherein the one or more first parameters are based on one or more of a difference in contextual data, a time period, a number of data units, or data included within a particular data unit; obtain, from a data store external to the system, second contextual data associated with the first data unit based on determining that the first data unit satisfies the one or more first parameters; provide, to the computing system, the second contextual data; obtain, from the computing system, the output based on the second contextual data and filtered first contextual data, wherein the first contextual data is filtered to obtain the filtered first contextual data; and provide, to the user computing device, the output based on obtaining the input.
Facts
- Company
- Amazon (Amazon Technologies, Inc.)
- Official title
- DYNAMIC CONTEXT ADJUSTMENT DURING A MACHINE LEARNING MODEL INFERENCE PROCESS
- Publication number
- US20260300816A1
- Application number
- 19096274
- Filed
- Mar 31, 2025
- Published
- Oct 1, 2026
- Inventors
- 4
- Claims
- 24
Classified under
G06N 20/00 Physics & computingG06F 40/284 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.