17984685. PROBABILISTIC PROCEDURE PLANNING FOR INSTRUCTIONAL VIDEOS simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
PROBABILISTIC PROCEDURE PLANNING FOR INSTRUCTIONAL VIDEOS
Organization Name
Inventor(s)
He Zhao of Richmond Hill CA (US)
Mikita Andreevich Dvornik of Toronto (CA)
Richard Wildes of Toronto (CA)
Konstantinos Derpanis of Toronto (CA)
Allan Douglas Jepson of Oakville (CA)
PROBABILISTIC PROCEDURE PLANNING FOR INSTRUCTIONAL VIDEOS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17984685 titled 'PROBABILISTIC PROCEDURE PLANNING FOR INSTRUCTIONAL VIDEOS
Simplified Explanation
The present disclosure describes a method and apparatus for probabilistic procedure planning using machine learning. Here are the key points:
- The method involves generating an action plan based on a user's request to achieve a specific end state.
- The action plan includes a series of intermediate actions between a start state and the end state.
- An input query matrix is constructed using various parameters such as the number of intermediate actions, start state, end state, positional encodings, and pseudo-random noise information.
- A machine learning transformer decoder is used to generate the action plan based on the input query matrix and a set of learnable vectors.
- The generated action plan provides a probability distribution of distinct action sequences that the user can perform to transform the start state to the end state.
- The action plan is then provided to the user for execution.
Potential applications of this technology:
- Robotic automation: This technology can be used to plan and generate action sequences for robots to perform specific tasks or achieve desired outcomes.
- Process optimization: It can be applied in industries to optimize procedures and workflows by generating efficient action plans.
- Personal assistants: Virtual assistants can utilize this technology to generate action plans for users to accomplish tasks or reach specific goals.
Problems solved by this technology:
- Complex planning: The method simplifies the process of generating action plans by using machine learning techniques, reducing the complexity and time required for manual planning.
- Uncertainty handling: By providing a probability distribution of action sequences, the method accounts for uncertainties and allows users to choose the most suitable plan based on their preferences or constraints.
Benefits of this technology:
- Efficiency: The automated generation of action plans saves time and effort compared to manual planning.
- Flexibility: The probability distribution allows users to select the most appropriate action sequence based on their needs and constraints.
- Adaptability: The machine learning approach enables the system to learn and improve over time, adapting to different scenarios and user preferences.
Original Abstract Submitted
The present disclosure provides methods and apparatuses for probabilistic procedure planning for generating a plan based on a goal relating to an end state. In some embodiments, a method includes receiving a request from a user to generate an action plan comprising T intermediate actions between a start state and the end state. The method further includes constructing an input query matrix based on T, the start state, the end state, positional encodings, and pseudo-random noise information. The method further includes generating, using a machine learning transformer decoder, the action plan based on the input query matrix and a plurality of learnable vectors. The method further includes providing the action plan to the user. The action plan indicates a probability distribution of a plurality of distinct action sequences, to be performed by the user, that transform the start state to the end state.
