Computer implementation methods, computer systems, and computer programs for modifying user interfaces.

Abstract

To provide a computer implemented method, a computer system, and a computer program for changing a user interface.SOLUTION: Attributes of a source object identified by a user in association with user input for storing the source object are determined. Attributes of one or more target storage locations are determined. A target storage location for storing the source object is predicted, along with a confidence value associated with prediction. The prediction is performed using a machine learning model that predicts the predicted target storage location and the associated confidence value based on the determined attributes of the source object. A plurality of usage patterns of the target storage location is determined. A user interface is modified based on the predicted target storage location.SELECTED DRAWING: Figure 5

Claims

1. A computer implementation method for modifying a user interface, In relation to user input for storing the source object, determine the attributes of the source object identified by the user. Determine the attributes of one or more target storage locations. Predicting a target storage location for storing the source object, and predicting a confidence value associated with the prediction, wherein a machine learning model predicts the predicted target storage location and the associated confidence value based on the determined attributes of the source object. Determining multiple usage patterns for target storage locations, and Modifying the user interface based on the predicted target storage location, wherein the modification includes providing a gravity effect between the source object and the predicted target storage location, the gravity effect being an increase or decrease in the system's response to a given amount of user input to the pointing device. Includes, In a drag-and-drop operation where a file represented by an icon is moved to a folder, When the movement of a graphic object follows a path toward a predicted target storage location, the system's response to a given amount of user input to the pointing device is translated within the user interface to increase its response. On the other hand, if the movement of a graphic object follows a path toward a less likely target storage location, the system's response to a given amount of user input to the pointing device is reduced within the user interface. The aforementioned computer implementation method.

2. The computer implementation method according to claim 1, wherein the determination of the multiple usage patterns of the target storage location is determined for one user and one location or one date and time of the user.

3. The computer implementation method according to claim 1, wherein determining the plurality of usage patterns of the target storage location for users is determined for one group of users.

4. The machine learning model predicts the predicted target storage location and associated confidence value based on the determined attributes of the source object. Based on the determined attributes of the one or more target storage locations, predict the predicted target storage location and the associated confidence value. It further includes, The source object includes sensitive data attributes, and the target storage location includes non-sensitive access attributes. The computer implementation method according to any one of claims 1 to 3.

5. The user interface is modified so that the gravity effect is perceived by the user. A computer implementation method according to any one of claims 1 to 4, further comprising:

6. Modifying the user interface based on the predicted target storage location, In the user interface, a line is rendered between the source object and the target storage location, wherein the line is weighted by its thickness according to the confidence level. The computer implementation method according to claim 1, including the method described in claim 1.

7. A computer system for verifying data written on tape, The computer system comprises one or more processors, one or more computer-readable memories, one or more tangible computer-readable storage media, and program instructions stored in at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories, and the computer system is In relation to user input for storing the source object, determine the attributes of the source object identified by the user. Determine the attributes of one or more target storage locations. Predicting a target storage location for storing the source object, and predicting a confidence value associated with the prediction, wherein a machine learning model predicts the predicted target storage location and the associated confidence value based on the determined attributes of the source object. Determining multiple usage patterns for target storage locations, and Modifying the user interface based on the predicted target storage location, wherein the modification includes providing a gravity effect between the source object and the predicted target storage location, the gravity effect being an increase or decrease in the system's response to a given amount of user input to a pointing device. A method including this can be performed, In a drag-and-drop operation where a file represented by an icon is moved to a folder, When the movement of a graphic object follows a path toward a predicted target storage location, the system's response to a given amount of user input to the pointing device is translated within the user interface to increase its response. On the other hand, if the movement of a graphic object follows a path toward a less likely target storage location, the system's response to a given amount of user input to the pointing device is reduced within the user interface. The aforementioned computer system.

8. The computer system according to claim 7, wherein determining the plurality of usage patterns of the target storage location is determined for one user and one location or one date and time of the user.

9. The computer system according to claim 7, wherein determining the plurality of usage patterns of the target storage location for a user is determined for one group of a plurality of users.

10. A machine learning model can predict the predicted target storage location and associated confidence value based on the determined attributes of the source object. Based on the determined attributes of the one or more target storage locations, predict the predicted target storage location and the associated confidence value. It further includes, The computer system according to any one of claims 7 to 9, wherein the source object includes a confidential data attribute and the target storage location includes a non-confidential access attribute.

11. The user interface is modified so that the gravity effect is perceived by the user. A computer system according to any one of claims 7 to 10, further comprising:

12. Modifying the user interface based on the predicted target storage location, The computer system according to claim 7, comprising visually highlighting the target storage location based on the confidence level of the prediction.

13. A computer program for verifying data written to tape, In relation to user input for storing the source object, determine the attributes of the source object identified by the user. Determine the attributes of one or more target storage locations. Predicting a target storage location for storing the source object, and predicting a confidence value associated with the prediction, wherein a machine learning model predicts the predicted target storage location and the associated confidence value based on the determined attributes of the source object. Determining multiple usage patterns for target storage locations, and Modifying the user interface based on the predicted target storage location, wherein the modification includes providing a gravity effect between the source object and the predicted target storage location, the gravity effect being an increase or decrease in the system's response to a given amount of user input to a pointing device. Make the processor execute it, In a drag-and-drop operation where a file represented by an icon is moved to a folder, When the movement of a graphic object follows a path toward a predicted target storage location, the system's response to a given amount of user input to the pointing device is translated within the user interface to increase its response. On the other hand, if the movement of a graphic object follows a path toward a less likely target storage location, the system's response to a given amount of user input to the pointing device is reduced within the user interface. The aforementioned computer program.

14. The computer program according to claim 13, wherein determining the plurality of usage patterns of the target storage location is determined for one user and one location or one date and time of the user.

15. A machine learning model can predict the predicted target storage location and associated confidence value based on the determined attributes of the source object. Based on the determined attributes of the one or more target storage locations, predict the predicted target storage location and the associated confidence value. It further includes, The source object includes sensitive data attributes, and the target storage location includes non-sensitive access attributes. The computer program according to claim 13 or 14.

16. The user interface is modified so that the gravity effect is perceived by the user. A computer program according to any one of claims 13 to 15, further comprising:

17. Modifying the user interface based on the predicted target storage location, In the user interface, a line is rendered between the source object and the target storage location, wherein the line is labeled according to a confidence level. The computer program according to claim 13, including the following:

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