Google llc (20240242077). Generating Templated Documents Using Machine Learning Techniques simplified abstract
Contents
Generating Templated Documents Using Machine Learning Techniques
Organization Name
Inventor(s)
Ming Jack Po of Mountain View CA (US)
Christopher Co of Saratoga CA (US)
Katherine Chou of Palo Alto CA (US)
Generating Templated Documents Using Machine Learning Techniques - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242077 titled 'Generating Templated Documents Using Machine Learning Techniques
The abstract of the patent application describes systems and methods for predicting documentation associated with an encounter between attendees. Attendee data is used to generate predicted visit notes through a machine-learned note prediction model that includes a neural network.
- Attendee data from previous visits is obtained.
- The data is inputted into a machine-learned note prediction model with a neural network.
- The neural network generates context vectors descriptive of the attendee data.
- Predicted visit notes are generated based on the context vectors.
- The predicted visit note includes expected information for a subsequently generated visit note.
Potential Applications: - Healthcare: Predicting patient visit notes for better care coordination. - Customer Service: Anticipating customer needs based on past interactions. - Event Planning: Generating personalized event notes for attendees.
Problems Solved: - Streamlining documentation processes. - Improving efficiency in generating visit notes. - Enhancing personalized interactions with attendees.
Benefits: - Increased accuracy in predicting visit notes. - Time-saving for staff in creating documentation. - Enhanced attendee experience through personalized notes.
Commercial Applications: Predictive analytics software for healthcare facilities, customer service centers, and event planning companies to streamline documentation processes and improve customer interactions.
Questions about the technology: 1. How does the neural network generate context vectors from attendee data? 2. What are the potential limitations of using machine-learned note prediction models in real-world applications?
Frequently Updated Research: Stay informed about advancements in machine learning algorithms for note prediction and applications in various industries.
Original Abstract Submitted
systems and methods of predicting documentation associated with an encounter between attendees are provided. for instance, attendee data indicative of one or more previous visit notes associated with a first attendee can be obtained. the attendee data can be inputted into a machine-learned note prediction model that includes a neural network. the neural network can generate one or more context vectors descriptive of the attendee data. data indicative of a predicted visit note can be received as output of the machine-learned note prediction model based at least in part on the context vectors. the predicted visit note can include a set of predicted information expected to be included in a subsequently generated visit note associated with the first attendee.
