Google llc (20240193418). EFFICIENT MACHINE LEARNING TRAINING ON SPREADSHEET DATA simplified abstract
Contents
- 1 EFFICIENT MACHINE LEARNING TRAINING ON SPREADSHEET DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 EFFICIENT MACHINE LEARNING TRAINING ON SPREADSHEET DATA - 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 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Predictive Spreadsheet Technology
- 1.13 Original Abstract Submitted
EFFICIENT MACHINE LEARNING TRAINING ON SPREADSHEET DATA
Organization Name
Inventor(s)
Mathieu Claude Charles-Marie Guillame-bert of Bonstetten (CH)
EFFICIENT MACHINE LEARNING TRAINING ON SPREADSHEET DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193418 titled 'EFFICIENT MACHINE LEARNING TRAINING ON SPREADSHEET DATA
Simplified Explanation
The patent application describes methods, systems, and apparatus for generating and displaying outputs based on user inputs. It involves using an interactive spreadsheet on a user device, training a machine learning model to predict values for specified cells, and displaying the predicted values back to the user.
- The method involves displaying an interactive spreadsheet on a user device.
- Users input which cells they want predicted values for.
- A machine learning model is trained on the existing values in the spreadsheet.
- The model predicts values for the specified cells.
- The predicted values are displayed to the user.
Key Features and Innovation
- Utilizes an interactive spreadsheet for user input.
- Involves training a machine learning model to predict values.
- Provides users with predicted values for specified cells.
- Enhances user experience by automating value predictions in a spreadsheet.
Potential Applications
This technology could be used in financial forecasting, inventory management, and data analysis where predicting values in a spreadsheet is essential.
Problems Solved
- Streamlines the process of predicting values in a spreadsheet.
- Reduces manual effort in filling in predicted values.
- Improves accuracy and efficiency in data analysis tasks.
Benefits
- Saves time and effort for users.
- Increases accuracy in predicting values.
- Enhances user experience with interactive and predictive features.
Commercial Applications
- "Predictive Spreadsheet Technology for Enhanced Data Analysis" could be marketed to businesses for financial planning, inventory optimization, and decision-making processes.
Prior Art
There may be existing technologies related to predictive modeling in spreadsheets or machine learning applications for data analysis that could be relevant to this patent application.
Frequently Updated Research
Research on machine learning algorithms for predictive modeling in spreadsheets and advancements in interactive data analysis tools could be relevant to this technology.
Questions about Predictive Spreadsheet Technology
How does this technology improve the efficiency of data analysis?
This technology automates the process of predicting values in a spreadsheet, saving users time and effort in manual data entry tasks.
What are the potential commercial applications of this technology?
This technology could be applied in various industries for financial forecasting, inventory management, and other data analysis tasks to improve decision-making processes.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and displaying outputs conditioned on inputs. one of the methods includes displaying an interactive spreadsheet on a display of a user device; receiving an input from a user that identifies one or more cells to be filled in with respective predicted values; in response to receiving the input: training a machine learning model on the values in the cells of the interactive spreadsheet to predict respective values for the one or more identified cells; generating a respective predicted value for each of the identified cells using the trained machine learning model; and displaying the respective predicted values on the user device.
