Dynamic input-sensitive validation of machine learning model outputs and methods and systems of the same
US12111747B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 10, 2024 |
| Grant date | Oct 8, 2024 |
| Priority date | — |
| Expiry date | May 10, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The systems and methods disclosed herein enable evaluation of machine learning model outputs within a virtual environment. The disclosed model validation platform enables testing of code generated for detection of malicious or anomalous outputs. For example, the model validation platform can construct a virtual machine isolated from the system and test model-generated code for validation of LLM-generated outputs. In some implementations, the model validation platform determines parameters of the virtual machine and/or associated validation test based on an evaluation of the machine learning model's output and/or the associated underlying prompt. For example, the parameters of the validation test depend on an evaluation of the user or the provided input (e.g., depending on the presence of sensitive data within the prompt). By doing so, the system enables dynamic evaluation of machine learning model outputs to improve the security and robustness of associated generated code.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.