Customizable data aggregating, data sorting, and data transformation system

• US 10,439,595 B2
• Filed: 05/25/2018
• Issued: 10/08/2019
• Est. Priority Date: 01/07/2016
• Status: Active Grant

• Current Assignee: Craig S. Montgomery, Spence K. Purnell
• Original Assignee: Craig S. Montgomery, Spence K. Purnell
• Inventors: Montgomery, Craig S., Purnell, Spence K.
• Primary Examiner(s): Mai, Tan V
• Application Number:
  US15/990,050
• Publication Number:
  US 20180278242A1
• Time in Patent Office: 501 Days
• Field of Search: 708300
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 17/17 Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method G06F17/18 takes precedence ; interpolation for numerical control G05B19/18
• CPC Class Codes: G06F 7/24 Sorting, i.e. extracting data from one or more carriers, rearranging the data in numerical or other ordered sequence, and rerecording the sorted data on the original carrier or on a different carrier or set of carriers sorting methods in generalG06F7/36 takes precedence
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: H03H 21/0012 Digital adaptive filters

Abstract

A customizable data aggregating, data sorting, and data transformation system is disclosed. In particular, the system may allow for the application of various filters to a sample of data corresponding to various measurables associated with objects. A mean and standard deviation for each of the measurables in the filtered sample of data may be calculated and may be utilized in determining z-scores for a first set of raw measurements corresponding to the measurables. Once the z-scores for the first set of raw measurements are determined, selected weights may be applied to each of the z-scores to determine a weighted z-score for each of the measurables in the first set. Each weighted z-score may then be aggregated to generate a score for an object associated with the first set. The score for the object may be utilized to rank the object relative to other objects in the filtered sample of data.

Claims

1. A system, comprising:

a memory that stores instructions; and

a processor that executes the instructions to perform operations, the operations comprising;

calculating a mean value for each of a plurality of measurable actions in a sample of data and a standard deviation value for each of the plurality of measurable actions in the sample of data, wherein the sample of data corresponds to a first subset of the plurality of measurable actions;

selecting a first set of raw measurements for a first set of measurable actions for a first object of a plurality of objects associated with the plurality of measureable actions, wherein the first set of measurable actions correspond to the first subset of the plurality of measureable actions;

calculating a z-score for each of the measurable actions in the first set of measurable actions, wherein the z-score is calculated based on the mean value, the standard deviation value, and the first set of raw measurements;

calculating, based on a weight to be applied to each z-score for each of the measurable actions in the first set of measurable actions, a weighted z-score for each of the measureable actions in the first set of measurable actions; and

aggregating each weighted z-score for each of the measurable actions in the first set of measurable actions to generate a first score for the first object.

2. The system of claim 1, wherein the operations further comprise applying a first filter to the sample of data corresponding to the plurality of measurable actions to generate a first filtered sample of data corresponding to the first subset of the plurality of measurable actions.

3. The system of claim 2, wherein the operations further comprise applying a second filter to the sample of data to generate a second filtered sample of data corresponding to a second subset of the plurality of measurable actions.

4. The system of claim 3, wherein the operations further comprise calculating a mean value for each of the plurality of measurable actions in the second filtered sample of data and a standard deviation value for each of the plurality of measurable actions in the second filtered sample of data.

5. The system of claim 4, wherein the operations further comprise selecting a second set of raw measurements for a second set of measurable actions for a second object of the plurality of objects, wherein the second set of measurable actions correspond to the second subset of the plurality of measureable actions.

6. The system of claim 5, wherein the operations further comprise calculating a z-score for each of the measurable actions in the second set of measurable actions, wherein the z-score for each of the measureable actions in the second set of measurable actions is calculated based on the mean value for each of the plurality of measurable actions in the second filtered sample of data, the standard deviation value for each of the plurality of measurable actions in the second filtered sample of data, and the second set of raw measurements.

7. The system of claim 6, wherein the operations further comprise calculating, based on a weight to be applied to each z-score for each of the measurable actions in the second set of measurable actions, a weighted z-score for each of the measureable actions in the second set of measurable action.

8. The system of claim 7, wherein the operations further comprise aggregating each weighted z-score for each of the measurable actions in the second set of measurable actions to generate a second score for the second object, and determining a ranking for the second object.

9. The system of claim 1, wherein the operations further comprise adjusting the weight to be applied to each z-score for each of the measurable actions in the first set of measurable actions.

10. The system of claim 9, wherein the operations further comprise adjusting the first score for the first object based on the adjusted weight.

11. The system of claim 1, wherein the operations further comprise comparing the first score for the first object to other scores for other objects in the plurality of objects to determine a ranking for the first object with respect to the other objects.

12. The system of claim 11, wherein the operations further comprise displaying, via a graphical user interface of an application executing in the system, the ranking for the first object and rankings for the other objects.

13. The system of claim 1, wherein the operations further comprise converting the first score into a percentile ranking for the first object.

14. A method, comprising:

calculating a mean value for each of a plurality of measurable actions in a sample of data and a standard deviation value for each of the plurality of measurable actions in the sample of data, wherein the sample of data corresponds to a first subset of the plurality of measurable actions;

selecting a first set of raw measurements for a first set of measurable actions for a first object of a plurality of objects associated with the plurality of measureable actions, wherein the first set of measurable actions correspond to the first subset of the plurality of measureable actions;

calculating a z-score for each of the measurable actions in the first set of measurable actions, wherein the z-score is calculated based on the mean value, the standard deviation value, and the first set of raw measurements;

calculating, based on a weight to be applied to each z-score for each of the measurable actions in the first set of measurable actions, a weighted z-score for each of the measureable actions in the first set of measurable actions; and

aggregating each weighted z-score for each of the measurable actions in the first set of measurable actions to generate a first score for the first object.

15. The method of claim 14, further comprising calculating a z-score for all objects associated with the sample of data.

16. The method of claim 15, further comprising comparing the first score for the first object to other scores for other objects in the plurality of objects to determine a ranking for the first object with respect to the other objects and adjusting the ranking for the first object as additional raw measurements are received for the first set of measureable actions for the first object over time.

17. The method of claim 14, further comprising adjusting the weight to be applied to each z-score for each of the measurable actions in the first set of measurable actions.

18. The method of claim 17, further comprising calculating, based on the adjusted weight, an adjusted weighted z-score for each of the measureable actions in the first set of measurable actions.

19. The method of claim 18, further comprising adjusting the first score based on the adjusted weighted z-score for each of the measureable actions in the first set of measurable actions.

20. A computer-readable device comprising instructions, which when loaded and executed by a processor, cause the processor to perform operations comprising:

calculating a mean value for each of a plurality of measurable actions in a sample of data and a standard deviation value for each of the plurality of measureable actions in the sample of data, wherein the sample of data corresponds to a first subset of the plurality of measurable actions;

selecting a first set of raw measurements for a first set of measurable actions for a first object of a plurality of objects associated with the plurality of measureable actions, wherein the first set of measurable actions correspond to the first subset of the plurality of measureable actions;

calculating a z-score for each of the measurable actions in the first set of measurable actions, wherein the z-score is calculated based on the mean value, the standard deviation value, and the first set of raw measurements;

calculating, based on a weight to be applied to each z-score for each of the measurable actions in the first set of measurable actions, a weighted z-score for each of the measureable actions in the first set of measurable actions; and

aggregating each weighted z-score for each of the measurable actions in the first set of measurable actions to generate a first score for the first object.