18618587. Generating Templated Documents Using Machine Learning Techniques simplified abstract (GOOGLE LLC)
Contents
- 1 Generating Templated Documents Using Machine Learning Techniques
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Generating Templated Documents Using Machine Learning Techniques - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Predictive Documentation Systems
- 1.11 Original Abstract Submitted
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 18618587 titled 'Generating Templated Documents Using Machine Learning Techniques
Simplified Explanation
The patent application describes systems and methods for predicting documentation associated with an encounter between attendees using machine learning models.
- Attendee data from previous visit notes is inputted into a neural network model to generate context vectors.
- The model outputs predicted visit notes based on the context vectors, providing expected information for subsequent visit notes.
Key Features and Innovation
- Utilizes machine learning models, specifically a neural network, to predict visit notes based on attendee data.
- Generates context vectors to describe attendee data and predict visit notes accurately.
Potential Applications
- Healthcare: Predicting patient visit notes for better documentation and treatment planning.
- Customer service: Anticipating customer needs and preferences based on past interactions.
- Event planning: Forecasting attendee requirements and preferences for better event management.
Problems Solved
- Improves efficiency in generating visit notes by automating the prediction process.
- Enhances the accuracy of visit notes by analyzing past data and generating context vectors.
Benefits
- Saves time for attendees and organizers by streamlining the documentation process.
- Improves the quality of visit notes by providing predicted information based on historical data.
Commercial Applications
Predictive Documentation Systems for Enhanced Attendee Encounters: Revolutionizing the way documentation is generated and utilized in various industries.
Questions about Predictive Documentation Systems
How does the neural network model generate context vectors?
The neural network processes attendee data to create context vectors that describe the information effectively.
What are the potential drawbacks of relying on machine learning models for predicting visit notes?
One potential drawback could be the need for continuous training and updating of the model to ensure accuracy and relevance over time.
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.
