IP Library Granted Patent US 10,417,426
Granted Patent B2
US 10,417,426 · App. 15/691,098 · Granted Sep 17, 2019

Aggregating service data for transmission and risk analysis

Inventors: Lujia Chen (Shanghai, CN); Qingyue Zhou (Hangzhou, CN); Weiqiang Wang (Hangzhou, CN)
Assignee: Alibaba Group Holding Limited
G06F21/57G06F2221/034
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Quick Facts
Patent No.
US 10,417,426
App. No.
15/691,098
Granted
Sep 17, 2019
Kind
B2
Abstract

Methods, systems, and computer-readable storage media for risk identification of service data using operations of determining, by a client-side computing device, a first service data corresponding to a first operation behavior associated with a user input on the client-side computing device, determining, by the client-side computing device, a first variable corresponding to the first service data, the first variable including a first eigenvalue, retrieving, by the client-side computing device, a second eigenvalue corresponding to a second operation behavior that was performed at a second time before the first operation behavior, generating, by the client-side computing device, a decay value by processing the first time and the second time a decay function, generating, by the client-side computing device, an aggregated data by processing the first variable, the second eigenvalue, and the decay value using an aggregation function, and determining, by the one or more processors, a risk associated with the first operation by processing the aggregated data using a risk identification model.

Claims (40)

1. A computer-implemented method for risk identification of service data, the method being executed by one or more processors and comprising:

determining, by a client-side computing device, a first service data corresponding to a first operation behavior associated with a user input on the client-side computing device;

determining, by the client-side computing device, a first variable corresponding to the first service data, the first variable comprising a first eigenvalue;

retrieving, by the client-side computing device, a second eigenvalue corresponding to a second operation behavior that was performed at a second time before the first operation behavior;

generating, by the client-side computing device, a decay value by processing the first time and the second time using a decay function, the decay value being weighted based upon the second time;

generating, by the client-side computing device, an aggregated data by processing the first variable, the second eigenvalue, and the decay value using an aggregation function; and

determining, by the one or more processors, a risk associated with the first operation by processing the aggregated data using a risk identification model.

2. The method of claim 1 , further comprising, in response to generating the aggregated data, deleting the second eigenvalue.

3. The method of claim 1 , wherein the one or more processors are integrated in a server-side computing device.

4. The method of claim 3 , further comprising transmitting the aggregated data from the client-side computing device to the server-side computing device.

5. The method of claim 4 , wherein the aggregation function comprises a mathematical operation defining at least one of a summing operation, an identification of a maximum value, and an identification of non-repetitive results.

6. The method of claim 1 , wherein the decay function comprises a mathematical operation defining at least one of an exponential function, a logarithmic function, a trigonometric function, a moving window type function, and a polynomial type function.

7. The method of claim 1 , further comprising displaying the risk associated with the first operation to a user of the client-side computing device.

8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

determining, by a client-side computing device, a first service data corresponding to a first operation behavior associated with a user input on the client-side computing device;

determining, by the client-side computing device, a first variable corresponding to the first service data, the first variable comprising a first eigenvalue;

retrieving, by the client-side computing device, a second eigenvalue corresponding to a second operation behavior that was performed at a second time before the first operation behavior;

generating, by the client-side computing device, a decay value by processing the first time and the second time using a decay function, the decay value being weighted based upon the second time;

generating, by the client-side computing device, an aggregated data by processing the first variable, the second eigenvalue, and the decay value using an aggregation function; and

determining, by the one or more processors, a risk associated with the first operation by processing the aggregated data using a risk identification model.

9. The non-transitory, computer-readable medium of claim 8 , further comprising, in response to generating the aggregated data, deleting the second eigenvalue.

10. The non-transitory, computer-readable medium of claim 8 , wherein the one or more processors are integrated in a server-side computing device.

11. The method of claim 10 , further comprising transmitting the aggregated data from the client-side computing device to the server-side computing device.

12. The non-transitory, computer-readable medium of claim 11 , wherein the aggregation function comprises a mathematical operation defining at least one of a summing operation, an identification of a maximum value, and an identification of non-repetitive results.

13. The non-transitory, computer-readable medium of claim 8 , wherein the decay function comprises a mathematical operation defining at least one of an exponential function, a logarithmic function, a trigonometric function, a moving window type function, and a polynomial type function.

14. The non-transitory, computer-readable medium of claim 8 , further comprising displaying the risk associated with the first operation to a user of the client-side computing device.

15. A computer-implemented system, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing instructions that, when executed by the one or more computers, perform operations comprising:

determining, by a client-side computing device, a first service data corresponding to a first operation behavior associated with a user input on the client-side computing device;

determining, by the client-side computing device, a first variable corresponding to the first service data, the first variable comprising a first eigenvalue;

retrieving, by the client-side computing device, a second eigenvalue corresponding to a second operation behavior that was performed at a second time before the first operation behavior;

generating, by the client-side computing device, a decay value by processing the first time and the second time using a decay function, the decay value being weighted based upon the second time;

generating, by the client-side computing device, an aggregated data by processing the first variable, the second eigenvalue, and the decay value using an aggregation function; and

determining, by the one or more processors, a risk associated with the first operation by processing the aggregated data using a risk identification model.

16. The computer-implemented system of claim 15 , further comprising, in response to generating the aggregated data, deleting the second eigenvalue.

17. The computer-implemented system of claim 15 , wherein the one or more processors are integrated in a server-side computing device.

18. The computer-implemented system of claim 17 , further comprising transmitting the aggregated data from the client-side computing device to the server-side computing device.

19. The method of claim 18 , wherein the aggregation function comprises a mathematical operation defining at least one of a summing operation, an identification of a maximum value, and an identification of non-repetitive results.

20. The computer-implemented system of claim 19 , wherein the decay function comprises a mathematical operation defining at least one of an exponential function, a logarithmic function, a trigonometric function, a moving window type function, and a polynomial type function.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 10, 2020
From: ADVANTAGEOUS NEW TECHNOLOGIES CO., LTD.
To: ADVANCED NEW TECHNOLOGIES CO., LTD.
Reel/Frame 053754/0625 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 31, 2020
From: ALIBABA GROUP HOLDING LIMITED
To: ADVANTAGEOUS NEW TECHNOLOGIES CO., LTD.
Reel/Frame 053743/0464 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 13, 2017
From: CHEN, LUJIA; ZHOU, QINGYUE; WANG, WEIQIANG
To: ALIBABA GROUP HOLDING LIMITED
Reel/Frame 043849/0569 →
Priority Claims (1)
CN 2016 1 0796941 · Aug 31, 2016 · national
Continuity (1)
Related Publication 20180060587A1 · Mar 1, 2018