Using Learned Application Flow to Assist Users in Network Business Transaction Based Apps

• US 20180033073A1
• Filed: 07/28/2016
• Published: 02/01/2018
• Est. Priority Date: 07/28/2016
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ramachandran, Puthukode G., Sanchez, John C., Simpson, Sophia A., Wang, Zhennan
• Granted Patent: US 11,030,673 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing

Abstract

An approach is provided that receives transaction data from a client that is running an app. The received transaction data identifies a latest step performed by the client. The received transaction data is compared to rules that have been generated from previous inputs of transaction data to an association rule learning algorithm. Each of the rules include steps and a confidence value corresponding to a last step of each of the respective rules. The approach identifies, based on the comparison, some rules where the latest step performed by the client is the penultimate step of the rules. One of the rules is selected based on the confidence value of the respective rule. The process then transmits a message to the client to perform the last step of the selected rule.

Claims

1. A method implemented by an information handling system that includes a processor and a memory accessible by the processor, the method comprising:

receiving, over a computer network, a transaction data from a client running an app, wherein the received transaction data identifies a latest step performed by the client on the app;

comparing the received transaction data to a plurality of association rules generated from previous inputs of transaction data to an association rule learning algorithm, wherein each of the rules include one or more steps and a confidence value corresponding to a last step of each of the respective rules;

identifying, based on the comparison, a set of rules where the latest step performed by the client is a penultimate step of each of the set of rules;

selecting one of the set of rules based on the confidence value; and

transmitting, over the computer network, a message to the client to perform the last step of the selected rule.

2. The method of claim 1 further comprising:

generating the rules, wherein the generating comprises;

receiving, over a computer network, the previous inputs of transaction data from a plurality of clients running the app; and

generating the plurality of association rules by inputting the transaction data to an association rule learning algorithm, wherein each association rule is based on a user transaction pattern and a desired result, and wherein each association rule includes the confidence value that pertains to an expected performance of a last step included in the respective association rule.

3. The method of claim 2 further comprising:

comparing one of the received transaction data with a plurality of transaction properties; and

generating a set of training data in response to the comparing revealing a matching transaction property, wherein the set of training data is input to the association rule learning algorithm to generate the rules.

4. The method of claim 2 further comprising:

comparing the confidence values included in the plurality of rules to a threshold;

in response to the comparison revealing that a selected last step of one or more of the rules falls below the threshold;

creating an assistance data corresponding to one or more of the selected last steps; and

storing a step identifier of the selected last steps and the created assistance data in a data store.

5. The method of claim 4 further comprising:

retrieving, from the data store, any assistance data associated with the last step of the selected rule; and

transmitting the retrieved assistance data to the app, wherein the assistance data is configured to be displayed at the app.

6. The method of claim 1 further comprising:

retrieving a set of assistance data corresponding to the last step of the selected rule; and

including the assistance data in the message.

7. The method of claim 1 wherein the association rule learning algorithm is an Apriori algorithm.

8. An information handling system comprising:

one or more processors;

a memory coupled to at least one of the processors;

a set of computer program instructions stored in the memory and executed by at least one of the processors in order to perform actions comprising;

receiving, over a computer network, a transaction data from a client running an app, wherein the received transaction data identifies a latest step performed by the client on the app;

comparing the received transaction data to a plurality of association rules generated from previous inputs of transaction data to an association rule learning algorithm, wherein each of the rules include one or more steps and a confidence value corresponding to a last step of each of the respective rules;

identifying, based on the comparison, a set of rules where the latest step performed by the client is a penultimate step of each of the set of rules;

selecting one of the set of rules based on the confidence value; and

transmitting, over the computer network, a message to the client to perform the last step of the selected rule.

9. The information handling system of claim 8 wherein the actions further comprise:

generating the rules, wherein the generating comprises;

receiving, over a computer network, the previous inputs of transaction data from a plurality of clients running the app; and

generating the plurality of association rules by inputting the transaction data to an association rule learning algorithm, wherein each association rule is based on a user transaction pattern and a desired result, and wherein each association rule includes the confidence value that pertains to an expected performance of a last step included in the respective association rule.

10. The information handling system of claim 9 wherein the actions further comprise:

comparing one of the received transaction data with a plurality of transaction properties; and

generating a set of training data in response to the comparing revealing a matching transaction property, wherein the set of training data is input to the association rule learning algorithm to generate the rules.

11. The information handling system of claim 9 wherein the actions further comprise:

comparing the confidence values included in the plurality of rules to a threshold;

in response to the comparison revealing that a selected last step of one or more of the rules falls below the threshold;

creating an assistance data corresponding to one or more of the selected last steps; and

storing a step identifier of the selected last steps and the created assistance data in a data store.

12. The information handling system of claim 11 wherein the actions further comprise:

retrieving, from the data store, any assistance data associated with the last step of the selected rule; and

transmitting the retrieved assistance data to the app, wherein the assistance data is configured to be displayed at the app.

13. The information handling system of claim 8 wherein the actions further comprise:

retrieving a set of assistance data corresponding to the last step of the selected rule; and

including the assistance data in the message.

14. The information handling system of claim 8 wherein the association rule learning algorithm is an Apriori algorithm.

15. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, performs actions comprising:

receiving, over a computer network, a transaction data from a client running an app, wherein the received transaction data identifies a latest step performed by the client on the app;

comparing the received transaction data to a plurality of association rules generated from previous inputs of transaction data to an association rule learning algorithm, wherein each of the rules include one or more steps and a confidence value corresponding to a last step of each of the respective rules;

identifying, based on the comparison, a set of rules where the latest step performed by the client is a penultimate step of each of the set of rules;

selecting one of the set of rules based on the confidence value; and

transmitting, over the computer network, a message to the client to perform the last step of the selected rule.

16. The computer program product of claim 15 wherein the actions further comprise:

generating the rules, wherein the generating comprises;

receiving, over a computer network, the previous inputs of transaction data from a plurality of clients running the app; and

generating the plurality of association rules by inputting the transaction data to an association rule learning algorithm, wherein each association rule is based on a user transaction pattern and a desired result, and wherein each association rule includes the confidence value that pertains to an expected performance of a last step included in the respective association rule.

17. The computer program product of claim 16 wherein the actions further comprise:

comparing one of the received transaction data with a plurality of transaction properties; and

generating a set of training data in response to the comparing revealing a matching transaction property, wherein the set of training data is input to the association rule learning algorithm to generate the rules.

18. The computer program product of claim 16 wherein the actions further comprise:

comparing the confidence values included in the plurality of rules to a threshold;

in response to the comparison revealing that a selected last step of one or more of the rules falls below the threshold;

creating an assistance data corresponding to one or more of the selected last steps; and

storing a step identifier of the selected last steps and the created assistance data in a data store.

19. The computer program product of claim 18 wherein the actions further comprise:

retrieving, from the data store, any assistance data associated with the last step of the selected rule; and

transmitting the retrieved assistance data to the app, wherein the assistance data is configured to be displayed at the app.

20. The computer program product of claim 15 wherein the actions further comprise:

retrieving a set of assistance data corresponding to the last step of the selected rule; and

including the assistance data in the message.