Enhanced tech support based on customer feedback

1 . A method for improving an automated user troubleshooting system, the method comprising:

training an artificial intelligence (“AI”) engine by:

feeding first feed data to the untrained AI engine, the first feed data including feed parameters which are associated with one or more characteristics for leveraging to exclude potential issues;

analyzing, by the untrained AI engine, the first feed data;

associating, by the untrained AI engine, the feed parameters to the characteristics;

feeding first test data to the untrained AI engine, the first test data including test parameters which are unassociated with the characteristics;

analyzing, by the untrained AI engine the test data;

associating, by the untrained AI engine, the test parameters to the characteristics;

validating that the untrained AI engine correctly matched the test parameters to the characteristics;

feeding second feed data to the untrained AI engine, the second feed data including feed characteristics which are associated with one or more of a plurality of consecutive steps stored in a troubleshooting database;

analyzing, by the untrained AI engine, the second feed data;

associating, by the untrained AI engine, the feed characteristics to the one or more of the plurality of consecutive steps;

feeding second test data to the untrained AI engine, the second test data including test characteristics which are unassociated with the one or more of the plurality of consecutive steps;

analyzing, by the untrained AI engine, the second test data;

associating, by the untrained AI engine, the test characteristics to the one or more of the plurality of consecutive steps;

validating that the untrained AI engine correctly matched the test characteristics to the one or more of the plurality of consecutive steps; and

retraining the untrained AI engine until the validation steps reach a threshold of at least 95% correct matches;

receiving a user inquiry at the AI engine;

parsing the user inquiry to identify an issue category;

retrieving, from the troubleshooting database, a troubleshooting guide that:

relates to the issue category; and

includes the plurality of consecutive steps used to identify an issue;

using the AI engine to associate each of the plurality of steps with one or more of the characteristics;

using the AI engine to associate each characteristic with one or more parameters, the presence of the one or more parameters indicating the characteristic is present;

retrieving user data associated with the characteristics from a user database and/or user device;

determining, by the AI engine before execution of the plurality of steps, one or more of the plurality of consecutive steps to skip based on the retrieved user data, the determining comprising:

parsing the user data;

verifying whether one or more parameters of the one or more characteristics are present in the user data; and

skipping each step of the plurality of consecutive steps associated with the characteristic whose associated parameters were verified to be present in the parsed user data;

executing, by the AI engine, non-skipped consecutive steps to identify the issue, wherein executing the non-skipped consecutive steps involves executing fewer steps than executing the plurality of consecutive steps;

determining resolution steps for a resolution to the issue based on the execution of the non-skipped consecutive steps; and

executing the resolution steps, wherein the resolution steps include one or more of internet connectivity and internet cookie acceptance steps.

2 . The method of claim 1 further comprising upon completion of each non-skipped consecutive step prompting the user to indicate whether the respective non-skipped consecutive step was effective in identifying the issue.

3 . The method of claim 2 wherein a user response to the prompt upon completion of each non-skipped consecutive step is used to recursively train the AI engine.

4 . A system for providing an improved automated user troubleshooting experience, the system comprising:

a processor; and

a non-transitory computer-readable medium including instructions that when executed by the processor:

trains an artificial intelligence (“AI”) engine by:

feeding first feed data to the untrained AI engine, the first feed data including feed parameters which are associated with one or more characteristics for leveraging to exclude potential issues;

analyzing, by the untrained AI engine the first feed data;

associating, by the untrained AI engine, the feed parameters to the characteristics;

feeding first test data to the untrained AI engine, the first test data including test parameters which are unassociated with the characteristics;

analyzing, by the untrained AI engine the test data;

associating, by the untrained AI engine, the test parameters to the characteristics;

validating that the untrained AI engine correctly matched the test parameters to the characteristics;

feeding second feed data to the untrained AI engine, the second feed data including feed characteristics which are associated with one or more of a plurality of consecutive steps stored in a troubleshooting database;

analyzing, by the untrained AI engine, the second feed data;

associating, by the untrained AI engine, the feed characteristics to the one or more of the plurality of consecutive steps;

feeding second test data to the untrained AI engine, the second test data including test characteristics which are unassociated with the one or more of the plurality of consecutive steps;

analyzing, by the untrained AI engine, the second test data;

associating, by the untrained AI engine, the test characteristics to the one or more of the plurality of consecutive steps;

validating that the untrained AI engine correctly matched the test characteristics to the one or more of the plurality of consecutive steps; and

retraining the untrained AI engine until the validation steps reach a threshold of at least 95% correct matches;

receive a user inquiry at the AI engine;

parses the user inquiry to identify an issue category;

retrieves, from the troubleshooting database, a troubleshooting guide that:

relates to the issue category; and

includes the plurality of consecutive steps used to identify an issue;

uses the AI engine to associate each of the plurality of steps with one or more of the characteristics;

uses the AI engine to associate each characteristic with one or more parameters, the presence of the one or more parameters indicating the characteristic is present;

retrieves user data associated with the characteristics from a user database and/or user device;

determines, by the AI engine before execution of the plurality of steps, one or more of the plurality of consecutive steps to skip based on the retrieved user data, determining the plurality of consecutive steps to skip comprises:

parsing the user data;

verifying whether one or more parameters of the one or more characteristics are present in the user data; and

skipping each step of the plurality of consecutive steps associated with the characteristic whose associated parameters were verified to be present in the parsed user data;

executes, by the AI engine, non-skipped consecutive steps to identify the issue, wherein executing the non-skipped consecutive steps involves executing fewer steps than executing the plurality of consecutive steps;

determines resolution steps for a resolution to the issue based on the execution of the non-skipped consecutive steps; and

executes the resolution steps, wherein the resolution steps include one or more of internet connectivity and internet cookie acceptance steps.

5 . The method of claim 1 wherein upon verifying the presence of an intervening characteristic in the user data, preventing the skipping of one or more steps.

6 . The method of claim 1 further including repeating the training until the untrained AI engine produces only correct matches.

7 . The method of claim 4 further including repeating the training until the untrained AI engine produces only correct matches.

8 . The method of claim 4 further comprising upon completion of each non-skipped consecutive step prompting the user to indicate whether the respective non-skipped consecutive step was effective in identifying the issue.

9 . The method of claim 8 wherein a user response to the prompt upon completion of each non-skipped consecutive step is used to recursively train the AI engine.

10 . The method of claim 4 wherein upon verifying the presence of an intervening characteristic in the user data, preventing the skipping of one or more steps.

11 . A method for improving an automated user troubleshooting system, the method comprising:

training an artificial intelligence (“AI”) engine by:

feeding first feed data to the untrained AI engine, the first feed data including feed parameters which are associated with one or more characteristics for leveraging to exclude potential issues;

analyzing, by the untrained AI engine the first feed data;

associating, by the untrained AI engine, the feed parameters to the characteristics;

feeding first test data to the untrained AI engine, the first test data including test parameters which are unassociated with the characteristics;

analyzing, by the untrained AI engine the test data;

associating, by the untrained AI engine, the test parameters to the characteristics;

validating that the untrained AI engine correctly matched the test parameters to the characteristics;

feeding second feed data to the untrained AI engine, the second feed data including feed characteristics which are associated with one or more of a plurality of consecutive steps stored in a troubleshooting database;

analyzing, by the untrained AI engine, the second feed data;

associating, by the untrained AI engine, the feed characteristics to the one or more of the plurality of consecutive steps;

feeding second test data to the untrained AI engine, the second test data including test characteristics which are unassociated with the one or more of the plurality of consecutive steps;

analyzing, by the untrained AI engine, the second test data;

associating, by the untrained AI engine, the test characteristics to the one or more of the plurality of consecutive steps;

validating that the untrained AI engine correctly matched the test characteristics to the one or more of the plurality of consecutive steps; and

retraining the untrained AI engine until the validation steps reach a threshold of at least 95% correct matches;

receiving a user inquiry at the AI engine;

parsing the user inquiry to identify an issue category;

retrieving, from the troubleshooting database, a troubleshooting guide that:

relates to the issue category; and

includes the plurality of consecutive steps used to identify an issue;

using the AI engine to associate each of the plurality of steps with one or more of the characteristics;

using the AI engine to associate each characteristic with one or more parameters, the presence of the one or more parameters indicating the characteristic is present;

retrieving user data associated with the characteristics from a user database and/or user device;

determining, by the AI engine before execution of the plurality of steps, one or more of the plurality of consecutive steps to skip based on the retrieved user data, the determining comprising:

parsing the user data;

verifying whether one or more parameters of the one or more characteristics are present in the user data; and

skipping each step of the plurality of consecutive steps associated with the characteristic whose associated parameters were verified to be present in the parsed user data;

executing, by the AI engine, non-skipped consecutive steps to identify the issue, wherein executing the non-skipped consecutive steps involves executing fewer steps than executing the plurality of consecutive steps;

determining resolution steps for a resolution to the issue based on the execution of the non-skipped consecutive steps; and

executing the resolution steps, wherein the resolution steps include one or more of internet connectivity and internet cookie acceptance steps;

wherein:

at least one of the plurality of consecutive steps includes prompting the user for parameter related information; and

the parameter related information is used to verify the presence of the one or more characteristics associated with the step in which the prompt was executed.

12 . The method of claim 11 further including repeating the training until the untrained AI engine produces only correct matches.