17969922. MODEL CAPABILITY EXTRACTION simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

MODEL CAPABILITY EXTRACTION

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Benjamin Goth Zorn of Woodinville WA (US)

Carina Suzana Negreanu of Cambridge (GB)

Neil Blunt Toronto of Cambridge (GB)

Brian Paul Slininger of Seattle WA (US)

Andrew Donald Gordon of Cambridge (GB)

Advait Sarkar of Cambridge (GB)

Elnaz Nouri of Seattle WA (US)

Vu Minh Le of Redmond WA (US)

Christian Leopold Bejamin Poelitz of London (GB)

Shraddha Govind Barke of La Jolla CA (US)

Sruti Srinivasa Ragavan of Oxford (GB)

MODEL CAPABILITY EXTRACTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17969922 titled 'MODEL CAPABILITY EXTRACTION

The abstract describes a method of indirectly querying models to determine their capabilities, using structured model input that may include natural language input from users. The output of the model is then evaluated to estimate or determine the model's capabilities, even if the output is not directly responsive to the input.

• This innovation allows for the more efficient utilization of models by assessing their capabilities through indirect queries.
• The method involves structured model input, potentially including natural language input, to evaluate the model's output.
• The output is then analyzed to estimate the capabilities possessed by the model, even if the output is not directly related to the input.
• This approach can help maximize the potential of models in various applications.
• By evaluating the output of models, users can better understand and utilize their capabilities.

Potential Applications: - This technology can be applied in various fields where models are used for decision-making or analysis. - It can be beneficial in natural language processing tasks where understanding model capabilities is crucial. - Industries such as healthcare, finance, and marketing could benefit from this method to assess model performance.

Problems Solved: - Provides a way to evaluate model capabilities indirectly. - Helps users understand the potential of models beyond their direct responses to input. - Enhances the utilization of models in different applications.

Benefits: - Improved assessment of model capabilities. - Better utilization of models for various tasks. - Enhanced understanding of model performance in real-world applications.

Commercial Applications: Title: Enhancing Model Capabilities Assessment for Improved Decision-Making This technology could be utilized in industries such as finance, healthcare, and marketing to assess model performance and enhance decision-making processes. By understanding the capabilities of models more effectively, businesses can make better-informed decisions and improve overall efficiency.

Questions about the Technology: 1. How does this method differ from direct querying of models for capabilities? - This method indirectly evaluates model capabilities through structured input and output analysis, whereas direct querying involves asking models specific questions to assess their capabilities. 2. What are the potential limitations of using natural language input in evaluating model capabilities? - Natural language input may introduce ambiguity or noise in the evaluation process, potentially affecting the accuracy of assessing model capabilities.

Original Abstract Submitted

The indirect querying of models to determine capabilities possessed by the model. Such indirect queries take the form of model input that potentially includes a natural language input user data. Such model input is structured such that the output of the model is either not natural language at all, or else is natural language that is not semantically responsive to the natural language input. Nevertheless, the output is evaluated to estimate or determine the capability possessed by the model. Thus, models may be more fully utilized to their better potential.