An approach is provided that receives, over a computer network, transaction data from a number of clients that are running an app. The approach generates association rules by inputting the transaction data to an association rule learning algorithm, such as an Apriori algorithm. Each association rule is based on a user transaction pattern and a desired result, and each association rule includes a generated confidence value that pertains to an expected performance of one of the steps included in the respective association rule. The app is then modified based on an analysis of the generated confidence values, with the app modification being directed towards improving one or more of the confidence values.
Please help me write a proper abstract based on the patent claims. CLAIM: 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 plurality of transaction data from a plurality of clients running an app; generating a 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 a generated confidence value that pertains to an expected performance of at least one step included in the respective association rule; and modifying the app based on an analysis of the generated confidence values, wherein the modification is directed towards improving at least one of the confidence values. 2. The method of claim 1 wherein the association rule learning algorithm is an Apriori algorithm. 3. The method of claim 1 further comprising: comparing one of the received transaction data with a plurality of transaction properties; and generating a set of training data to input to the association rule learning algorithm in response to the comparing revealing a matching transaction property. 4. The method of claim 3 wherein the generating the plurality of rules inputs the generated training data to the association rule learning algorithm. 5. The method of claim 1 further comprising: comparing the confidence values corresponding to the plurality of rules to a threshold; selecting one of the plurality of rules where the confidence value is below the threshold; analyzing a last step included in the selected rule, wherein the last step corresponds to the confidence value that is below the threshold; adjusting the analyzed last step by modifying the app so that the analyzed last step is improved; and releasing the modified app to the plurality of clients. 6. The method of claim 1 further comprising: storing the received plurality of transaction data as raw data; aggregating the raw data to form a set of training data; inputting the set of training data to the association rule learning algorithm; and identifying a set of client prediction results based on the confidence values that are associated with the at least one step of each of the association rules, wherein each of the at least one steps is a last step of the respective association rules. 7. The method of claim 1 further comprising: identifying, based on the generated confidence values that pertain to the at least one step included in each of the rules, an area of difficulty pertaining to a selected one of the steps; creating a set of assistance data pertaining to the selected step; and transmitting the set of assistance data to the clients running the app, wherein the assistance data is configured to be displayed when a client is about to perform the selected step. 8. An information handling system comprising: one or more processors; a memory coupled to at least one of the processors; a touch-enabled display screen accessible by at least one of the processors; and 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 plurality of transaction data from a plurality of clients running an app; generating a 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 a generated confidence value that pertains to an expected performance of at least one step included in the respective association rule; and modifying the app based on an analysis of the generated confidence values, wherein the modification is directed towards improving at least one of the confidence values. 9. The information handling system of claim 8 wherein the association rule learning algorithm is an Apriori algorithm. 10. The information handling system of claim 8 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 to input to the association rule learning algorithm in response to the comparing revealing a matching transaction property. 11. The information handling system of claim 10 wherein the generating the plurality of rules inputs the generated training data to the association rule learning algorithm. 12. The information handling system of claim 8 wherein the actions further comprise: comparing the confidence values corresponding to the plurality of rules to a threshold; selecting one of the plurality of rules where the confidence value is below the threshold; analyzing a last step included in the selected rule, wherein the last step corresponds to the confidence value that is below the threshold; adjusting the analyzed last step by modifying the app so that the analyzed last step is improved; and releasing the modified app to the plurality of clients. 13. The information handling system of claim 8 wherein the actions further comprise: storing the received plurality of transaction data as raw data; aggregating the raw data to form a set of training data; inputting the set of training data to the association rule learning algorithm; and identifying a set of client prediction results based on the confidence values that are associated with the at least one step of each of the association rules, wherein each of the at least one steps is a last step of the respective association rules. 14. The information handling system of claim 8 wherein the actions further comprise: identifying, based on the generated confidence values that pertain to the at least one step included in each of the rules, an area of difficulty pertaining to a selected one of the steps; creating a set of assistance data pertaining to the selected step; and transmitting the set of assistance data to the clients running the app, wherein the assistance data is configured to be displayed when a client is about to perform the selected step. 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 plurality of transaction data from a plurality of clients running an app; generating a 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 a generated confidence value that pertains to an expected performance of at least one step included in the respective association rule; and modifying the app based on an analysis of the generated confidence values, wherein the modification is directed towards improving at least one of the confidence values. 16. The computer program product of claim 15 wherein the association rule learning algorithm is an Apriori algorithm. 17. The computer program product of claim 15 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 to input to the association rule learning algorithm in response to the comparing revealing a matching transaction property. 18. The computer program product of claim 17 wherein the generating the plurality of rules inputs the generated training data to the association rule learning algorithm. 19. The computer program product of claim 15 wherein the actions further comprise: comparing the confidence values corresponding to the plurality of rules to a threshold; selecting one of the plurality of rules where the confidence value is below the threshold; analyzing a last step included in the selected rule, wherein the last step corresponds to the confidence value that is below the threshold; adjusting the analyzed last step by modifying the app so that the analyzed last step is improved; and releasing the modified app to the plurality of clients. 20. The computer program product of claim 15 wherein the actions further comprise: storing the received plurality of transaction data as raw data; aggregating the raw data to form a set of training data; inputting the set of training data to the association rule learning algorithm; and identifying a set of client prediction results based on the confidence values that are associated with the at least one step of each of the association rules, wherein each of the at least one steps is a last step of the respective association rules.