INDIRECT LUGGAGE WEIGHT IDENTIFICATION

• US 20190318441A1
• Filed: 04/13/2018
• Published: 10/17/2019
• Est. Priority Date: 04/13/2018
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Gupta, Deepak, Raina Nambiar, Saraca, Sharma, Alok, Gupta, Namita
• Application Number:
  US15/952,276
• Publication Number:
  US 20190318441A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/0633 Workflow analysis
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management

Abstract

Embodiments of the present invention provides methods, computer program products, and a system for indirectly weighing a load. Embodiments of the present invention can be used to collect recordings of a user interacting with a load, analyzing the recordings based on baseline measurements and a variety of measurement techniques, and predicting a weight of the load based on the analysis without directly weighing the load. Embodiments of the present invention can be used to issue a notification to a user based on the weight of the load to inform the user the load is overweight, to allow the user to print luggage tickets, and to allow the user to pay for baggage fees.

Claims

1. A computer-implemented method comprising:

analyzing recordings of a user interacting with a load based on a baseline measurement and a set of measurement techniques;

predicting a weight of the load based on the analyzed recordings of the user interacting with the load; and

generating a recommendation that includes a series of actions and notifications to be displayed on a user interface based on the predicted weight.

2. The computer-implemented method of claim 1, wherein predicting the weight of the load based on the analyzed recordings of the user interacting with the load comprises:

generating a profile of the user;

generating a set of statistical distributions of user-load interactions corresponding to the set of measurement techniques;

matching a set of values to the generated set of statistical distributions of the user-load interactions, wherein the set of values are used to predict a weight and are based on the set of measurement techniques corresponding to the analyzed recordings; and

assigning a weight to the load based on the matched set of values.

3. The computer-implemented method of claim 1, further comprising:

recording the user interacting with one or more loads, wherein the recordings are continuously uploaded to a database of a transit server computer.

4. The computer-implemented method of claim 3, wherein recording the user interacting with one or more loads comprises:

continuously recording the user interacting with the one or more loads and storing the recording to a database corresponding to a home server computer;

continuously uploading the recordings of the database of the home server computer with a database of a mobile device corresponding to the user; and

downloading the continuously uploaded database of the mobile device corresponding to the user to a database of a transit server computer.

5. The computer-implemented method of claim 1, wherein the baseline measurement and the set of measurement techniques are based on a characteristic of a user, wherein the characteristic of a user includes posture, facial demeanor, and audible sounds produced by the user.

6. The computer-implemented method of claim 1, wherein the baseline measurement and the set of measurement techniques are based on a characteristic of a load, wherein the characteristic of a load includes material composition of the load, and a volume of the load.

7. The computer-implemented method of claim 1, wherein the generated recommendation is created for display on a user device, wherein the display of the generated recommendation includes an interface that specifies a screen for user identification, load identification, the predicted weight of a load corresponding to the user, and an error value corresponding to the determined weight of the load.

8. A computer program product comprising:

one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising;

program instructions to analyze recordings of a user interacting with a load based on a baseline measurement and a set of measurement techniques;

program instructions to predict a weight of the load based on the analyzed recordings of the user interacting with the load; and

program instructions to generate a recommendation that includes a series of actions and notifications to be displayed on a user interface based on the predicted weight.

9. The computer program product of claim 8, wherein the program instructions to predict the weight of the load based on the analyzed recordings of the user interacting with the load comprise:

program instructions to generate a profile of the user;

program instructions to generate a set of statistical distributions of user-load interactions corresponding to the set of measurement techniques;

program instructions to match a set of values to the generated set of statistical distributions of the user-load interactions, wherein the set of values are used to predict a weight and are based on the set of measurement techniques corresponding to the analyzed recordings; and

program instructions to assign a weight to the load based on the matched set of values.

10. The computer program product of claim 8, further comprising:

program instructions to record the user interacting with one or more loads, wherein the recordings are continuously uploaded to a database of a transit server computer.

11. The computer program product of claim 10, wherein the program instructions to record the user interacting with one or more loads comprise:

program instructions to continuously record the user interacting with the one or more loads and storing the recording to a database corresponding to a home server computer;

program instructions to continuously upload the recordings of the database of the home server computer with a database of a mobile device corresponding to the user; and

program instructions to download the continuously uploaded database of the mobile device corresponding to the user to a database of a transit server computer.

12. The computer program product of claim 8, wherein the baseline measurement and the set of measurement techniques are based on a characteristic of a user, wherein the characteristic of a user includes posture, facial demeanor, and audible sounds produced by the user.

13. The computer program product of claim 8, wherein the baseline measurement and the set of measurement techniques are based on a characteristic of a load, wherein the characteristic of a load includes material composition of the load, and a volume of the load.

14. The computer program product of claim 8, wherein the generated recommendation is created for display on a user device, wherein the display of the generated recommendation includes an interface that specifies a screen for user identification, load identification, the predicted weight of a load corresponding to the user, and an error value corresponding to the determined weight of the load.

15. A computer system comprising:

one or more computer processors;

one or more computer readable storage media; and

program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising;

program instructions to analyze recordings of a user interacting with a load based on a baseline measurement and a set of measurement techniques;

program instructions to predict a weight of the load based on the analyzed recordings of the user interacting with the load; and

program instructions to generate a recommendation that includes a series of actions and notifications to be displayed on a user interface based on the predicted weight.

16. The computer system of claim 15, wherein the program instructions to predict the weight of the load based on the analyzed recordings of the user interacting with the load comprise:

program instructions to generate a profile of the user;

program instructions to generate a set of statistical distributions of user-load interactions corresponding to the set of measurement techniques;

program instructions to match a set of values to the generated set of statistical distributions of the user-load interactions, wherein the set of values are used to predict a weight and are based on the set of measurement techniques corresponding to the analyzed recordings; and

program instructions to assign a weight to the load based on the matched set of values.

17. The computer system of claim 15, further comprising:

program instructions to record the user interacting with one or more loads, wherein the recordings are continuously uploaded to a database of a transit server computer.

18. The computer system of claim 17, wherein the program instructions to record the user interacting with one or more loads comprise:

program instructions to continuously record the user interacting with the one or more loads and storing the recording to a database corresponding to a home server computer;

program instructions to continuously upload the recordings of the database of the home server computer with a database of a mobile device corresponding to the user; and

program instructions to download the continuously uploaded database of the mobile device corresponding to the user to a database of a transit server computer.

19. The computer system of claim 15, wherein the baseline measurement and the set of measurement techniques are based on a characteristic of a user, wherein the characteristic of a user includes posture, facial demeanor, and audible sounds produced by the user.

20. The computer system of claim 15, wherein the generated recommendation is created for display on a user device, wherein the display of the generated recommendation includes an interface that specifies a screen for user identification, load identification, the predicted weight of a load corresponding to the user, and an error value corresponding to the determined weight of the load.