Microsoft technology licensing, llc (20240135113). MODEL CAPABILITY EXTRACTION simplified abstract

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 20240135113 titled 'MODEL CAPABILITY EXTRACTION

Simplified Explanation

The abstract of the patent application describes a method for indirectly querying models to determine their capabilities, using structured model inputs that may include natural language input from users. The output of the model is evaluated to estimate or determine the capability possessed by the model, even if the output is not directly responsive to the input.

• Explanation of the patent/innovation:

- Indirect querying of models to assess capabilities - Structured model inputs, potentially including natural language input - Evaluation of model output to determine capability

Potential applications of this technology: - Natural language processing systems - AI-powered virtual assistants - Data analysis and prediction models

Problems solved by this technology: - Efficient evaluation of model capabilities - Improved utilization of model potential - Enhanced user interaction with models

Benefits of this technology: - Better understanding of model capabilities - Enhanced user experience with models - Increased efficiency in model utilization

Potential commercial applications of this technology: - Customer service chatbots - Predictive analytics software - Virtual customer support agents

Possible prior art: - Existing methods for evaluating model capabilities - Previous research on natural language processing and model querying techniques

Unanswered questions: 1. How does this method compare to existing techniques for evaluating model capabilities? 2. What are the potential limitations or challenges associated with using natural language input for model querying?

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.