# US 11,907,094 B2 · System and method for automatically identifying an anomalous pattern

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- Number: US 11,907,094 B2
- Document ID: US11907094B2
- Status: Granted
- Kind: B2
- Application number: 17/868,407
- Filed: 2022-07-19
- Published: 2023-01-26
- Granted: 2024-02-20
- Earliest claimed: 2021-07-20
- Assignees: Flipkart Internet Private Limited
- Applicants: Flipkart Internet Private Limited
- Inventors: Richa Arora; Priyanshu Raj; Srinivas Deshpande; Ananda Matthur; Roshan Nair; Sasikanth Lenka; Praveen R S
- Web page: https://patentgrep.com/patent/US11907094B2

## CPC

- G06F 11/34: Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation {; Recording or statistical evaluation of user activity, e.g. usability assessment}
- G06Q 20/4016: {involving fraud or risk level assessment in transaction processing}
- G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

A system and method for automatically identifying an anomalous pattern. The method encompasses receiving, a stream of data. The method further comprises determining, a monitoring metric for at least one of one or more dimensions and one or more groups of dimensions associated with the stream of data, at a target time and at a benchmark time period. Further the method comprises identifying, the monitoring metric at the target time as an outlier to the monitoring metric at the benchmark time period based at least on a threshold value. The method further comprises automatically identifying, the anomalous pattern based at least on said identification of the monitoring metric for at least one of the dimension(s) and the group(s) of dimensions at the target time as the outlier to the monitoring metric for at least one of the dimension(s) and the group(s) of dimensions at the benchmark time period.

## Claims

### Claim 1

A method for automatically identifying an anomalous pattern, the method comprising:
receiving, at an input unit [102], a stream of data;
determining, by a processing unit [104], a monitoring metric for at least one of one or more dimensions and one or more groups of dimensions associated with the stream of data, at a target time;
determining, by the processing unit [104], the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions, at a benchmark time period;
identifying, by an identification unit [106], the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the target time as an outlier to the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the benchmark time period based at least on an event where the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the target time is greater than the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the benchmark time period at least by a threshold value; and
automatically identifying, by the identification unit [106], the anomalous pattern based at least on the identification of the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the target time as the outlier to the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the benchmark time period.

### Claim 2 (depends on 1)

The method as claimed in claim 1, the method comprises determining by the processing unit [104] a dataset based on at least one cohort, wherein each cohort from the at least one cohort comprises one or more input dimensions.

### Claim 3 (depends on 2)

The method as claimed in claim 2, wherein the monitoring metric is defined and monitored by the processing unit [104] based on the dataset.

### Claim 4 (depends on 3)

The method as claimed in claim 3, wherein the one or more dimensions and the one or more groups of dimensions are identified based on the dataset.

### Claim 5 (depends on 1)

The method as claimed in claim 1, the method further comprises determining, by the processing unit [104], a first ratio based on an iterative comparison of the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions determined at the target time with an average of the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions determined at the benchmark time period.

### Claim 6 (depends on 1)

The method as claimed in claim 1, the method further comprises:
determining, by the processing unit [104], the monitoring metric for at least one of one dimension above the one or more dimensions associated with the stream of data and one dimension above the one or more groups of dimensions associated with the stream of data, at the target time;
determining, by the processing unit [104], the monitoring metric for at least one of the one dimension above the one or more dimensions associated with the stream of data and the one dimension above the one or more groups of dimensions associated with the stream of data, at the benchmark time period;
comparing iteratively, by the processing unit [104], the monitoring metric for at least one of the one dimension above the one or more dimensions associated with the stream of data and the one dimension above the one or more groups of dimensions associated with the stream of data at the target time with an average of the monitoring metric for at least one of the one dimension above the one or more dimensions associated with the stream of data and the one dimension above the one or more groups of dimensions associated with the stream of data at the benchmark time period; and
determining, by the processing unit [104], a second ratio based on said iterative comparison.

### Claim 7 (depends on 6)

The method as claimed in claim 6, wherein automatically identifying, by the identification unit [106], the anomalous pattern is further based on a comparison of the first ratio and the second ratio.

### Claim 8 (depends on 2)

The method as claimed in claim 2, the method comprises:
determining, by the processing unit [104], the monitoring metric for each vertical of the cohort at the target time;
determining, by the processing unit [104], the monitoring metric for the each vertical of the cohort at the benchmark time period;
comparing, by the processing unit [104], the monitoring metric for the each vertical of the cohort at the target time with an average of the monitoring metric for the each vertical of the cohort at the benchmark time period; and
determining, by the processing unit [104], a third ratio based on said comparison.

### Claim 9 (depends on 8)

The method as claimed in claim 8, wherein automatically identifying, by the identification unit [106], the anomalous pattern is based on a comparison of the first ratio and the third ratio.

### Claim 10

A system for automatically identifying an anomalous pattern, the system comprising:
an input unit [102], configured to receive, a stream of data;
a processing unit [104], configured to:
determine a monitoring metric for at least one of one or more dimensions and one or more groups of dimensions associated with the stream of data, at a target time; and
determine the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions, at a benchmark time period; and
an identification unit [106], configured to:
identify the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the target time as an outlier to the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the benchmark time period based at least on an event where the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the target time is greater than the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the benchmark time period at least by a threshold value; and
automatically identify the anomalous pattern based at least on the identification of the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the target time as the outlier to the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions at the benchmark time period.

### Claim 11 (depends on 10)

The system as claimed in claim 10, wherein the processing unit [104] is configured to determine a dataset based on at least one cohort, wherein each cohort from the at least one cohort comprises one or more input dimensions.

### Claim 12 (depends on 11)

The system as claimed in claim 11, wherein the monitoring metric is defined and monitored by the processing unit [104] based on the dataset.

### Claim 13 (depends on 12)

The system as claimed in claim 12, wherein the one or more dimensions and the one or more groups of dimensions are identified based on the dataset.

### Claim 14 (depends on 10)

The system as claimed in claim 10, wherein the processing unit [104] is further configured to determine a first ratio based on an iterative comparison of the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions determined at the target time with an average of the monitoring metric for at least one of the one or more dimensions and the one or more groups of dimensions determined at the benchmark time period.

### Claim 15 (depends on 10)

The system as claimed in claim 10, wherein the processing unit [104] is further configured to:
determine the monitoring metric for at least one of one dimension above the one or more dimensions associated with the stream of data and one dimension above the one or more groups of dimensions associated with the stream of data, at the target time;
determine the monitoring metric for at least one of the one dimension above the one or more dimensions associated with the stream of data and the one dimension above the one or more groups of dimensions associated with the stream of data, at the benchmark time period;
compare iteratively the monitoring metric for at least one of the one dimension above the one or more dimensions associated with the stream of data and the one dimension above the one or more groups of dimensions associated with the stream of data at the target time with an average of the monitoring metric for at least one of the one dimension above the one or more dimensions associated with the stream of data and the one dimension above the one or more groups of dimensions associated with the stream of data at the benchmark time period; and
determine a second ratio based on said iterative comparison.

### Claim 16 (depends on 15)

The system as claimed in claim 15, wherein the automatic identification of the anomalous pattern is further based on a comparison of the first ratio and the second ratio.

### Claim 17 (depends on 11)

The system as claimed in claim 11, wherein the processing unit [104] is further configured to:
determine the monitoring metric for each vertical of the cohort at the target time;
determine, the monitoring metric for the each vertical of the cohort at the benchmark time period;
compare the monitoring metric for the each vertical of the cohort at the target time with an average of the monitoring metric for the each vertical of the cohort at the benchmark time period; and
determine a third ratio based on said comparison.

### Claim 18 (depends on 17)

The system as claimed in claim 17, wherein the identification unit [106] is configured to automatically identify the anomalous pattern based on a comparison of the first ratio and the third ratio.