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

Simplified Explanation

The abstract of the patent application 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 capability possessed by the model.

• Explanation of the innovation:

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

• Potential applications of this technology:

- Natural language processing - Machine learning - Artificial intelligence

• Problems solved by this technology:

- Efficient evaluation of model capabilities - Improved utilization of models - Enhanced understanding of model performance

• Benefits of this technology:

- Better utilization of models - Enhanced model evaluation - Improved decision-making based on model capabilities

• Potential commercial applications of this technology:

- Chatbots - Virtual assistants - Data analysis tools

• Possible prior art:

- Previous methods of model evaluation - Existing natural language processing techniques

Questions:

1. How does this method of indirect querying differ from direct querying of models? - The method of indirect querying involves structured model input, including natural language input, to evaluate model capabilities, while direct querying typically involves querying the model directly with specific questions or commands.

2. What are the potential limitations of using natural language input for evaluating model capabilities? - Natural language input may introduce ambiguity or noise in the evaluation process, leading to potential inaccuracies in determining 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.