17849969. MULTIPLE STAGE KNOWLEDGE TRANSFER simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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MULTIPLE STAGE KNOWLEDGE TRANSFER

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Amit Dhurandhar of Yorktown Heights NY (US)

Tejaswini Pedapati of White Plains NY (US)

MULTIPLE STAGE KNOWLEDGE TRANSFER - A simplified explanation of the abstract

This abstract first appeared for US patent application 17849969 titled 'MULTIPLE STAGE KNOWLEDGE TRANSFER

Simplified Explanation

Abstract: An input model is received along with a set of requirements, which describe an output model to be trained. The output model is then trained based on the input model and at least one intermediate model.

Patent/Innovation Explanation:

  • An input model is provided along with a set of requirements.
  • The requirements specify the desired output model that needs to be trained.
  • The output model is trained using the input model as a basis.
  • The training process also incorporates at least one intermediate model to further enhance the output model.

Potential Applications:

  • Machine learning and artificial intelligence systems.
  • Data analysis and prediction models.
  • Image recognition and computer vision algorithms.
  • Natural language processing and speech recognition systems.

Problems Solved:

  • Efficiently training complex models by utilizing intermediate models.
  • Meeting specific requirements for the output model.
  • Improving the accuracy and performance of trained models.
  • Streamlining the training process for various applications.

Benefits:

  • Enhanced accuracy and performance of trained models.
  • Flexibility to meet specific requirements and desired output.
  • Improved efficiency in training complex models.
  • Potential for advancements in various fields such as AI, data analysis, and image recognition.


Original Abstract Submitted

An input model can be received, along with a set of requirements. The set of requirements may describe an output model to be trained. The output model can then be trained. The training of the output model can be based on the input model and based further on at least one intermediate model.

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