SYSTEMS AND METHODS FOR SPAM BLOCKING USING ARTIFICIAL INTELLIGENCE IN A TIERED SOFTWARE FRAMEWORK

1 . A method for automatically facilitating spam blocking in a tiered software framework, the method comprising:

providing instructions to a machine learning module (MLM) to generate a threshold for classifying spam in messages generated in a tiered software framework, wherein:

the instructions comprise inputs, including at least one of:

government regulations related to spam;

carrier guidelines related to spam;

feedback on messages previously sent from the tiered software framework; and

messages previously flagged as spam, and

the instructions specify that false positives are messages falsely classified as containing spam, false negatives are messages falsely classified as not containing spam, and the threshold is to prevent false positives and allow false negatives;

receiving, from the MLM, at least one threshold according to the instructions;

parsing a message generated in the tiered software framework;

automatically performing a semantic search using natural language processing for regulated content in the parsed message by comparing semantics of text of the parsed message to the inputs to find matches;

assigning a score to the message based on any matches found; and

responsive to the score being higher than the threshold, blocking sending the message from the tiered software framework.

2 . The method of claim 1 , further comprising generating, by the MLM, keywords according to the inputs, wherein: semantic analysis of the content in the message includes:

identifying one or more of the keywords in the parsed message;

identifying a context of the identified keywords in the message; and

assigning the score based on the identified keywords in the context.

3 . The method of claim 2 , wherein generating the keywords comprises:

identifying a first set of keywords in at least one of the government regulations or the carrier guidelines;

adjusting the first set of keywords using learning algorithms based on at least one of the feedback or the messages previously flagged as spam.

4 . The method of claim 1 , further comprising:

collecting new feedback on the message; and

retraining the MLM based on the new feedback.

5 . The method of claim 1 , further comprising providing a tiered software framework comprising a plurality of tiers, wherein an account in one tier manages data of a subaccount in another tier.

6 . The method of claim 5 , wherein:

the message is generated in the subaccount, and

the feedback is from at least one of a network carrier, the account, or a recipient of the message.

7 . The method of claim 5 , further comprising: flagging the message as spam based on user information comprised in the data of the subaccount, the data provided in an intake form at the another tier.

8 . The method of claim 7 , wherein the feedback and the user information are accessible only to the subaccount and the account, and inaccessible to other subaccounts or accounts in the tiered software framework.

9 . Non-transitory computer-readable tangible media that includes instructions for execution, which when executed by a processor of a computing device, is operable to perform operations comprising:

providing instructions to a machine learning module (MLM) to generate a threshold for classifying spam in messages generated in a tiered software framework, wherein:

the instructions comprise inputs, including at least one of:

government regulations related to spam;

carrier guidelines related to spam;

feedback on messages previously sent from the tiered software framework; and

messages previously flagged as spam, and

the instructions specify that false positives are messages falsely classified as containing spam, false negatives are messages falsely classified as not containing spam, and the threshold is to prevent false positives and allow false negatives;

receiving, from the MLM, at least one threshold according to the instructions;

parsing a message generated in the tiered software framework;

automatically performing a semantic search using natural language processing for regulated content in the parsed message by comparing semantics of text of the parsed message to the inputs to find matches;

assigning a score to the message based on any matches found; and

responsive to the score being higher than the threshold, blocking sending the message from the tiered software framework.

10 . The non-transitory computer-readable tangible media of claim 9 , the operations further comprising generating, by the MLM, keywords according to the inputs, wherein: semantic analysis of the content in the message includes:

identifying one or more of the keywords in the parsed message;

identifying a context of the identified keywords in the message; and

assigning the score based on the identified keywords in the context.

11 . The non-transitory computer-readable tangible media of claim 10 , wherein generating the keywords comprises:

identifying a first set of keywords in at least one of the government regulations or the carrier guidelines;

adjusting the first set of keywords using learning algorithms based on at least one of the feedback or the messages previously flagged as spam.

12 . The non-transitory computer-readable tangible media of claim 9 , the operations further comprising:

collecting new feedback on the message; and

retraining the MLM based on the new feedback.

13 . The non-transitory computer-readable tangible media of claim 9 , the operations further comprising providing a tiered software framework comprising a plurality of tiers, wherein an account in one tier manages data of a subaccount in another tier.

14 . The non-transitory computer-readable tangible media of claim 13 , wherein:

the message is generated in the subaccount, and

the feedback is from at least one of a network carrier, the account, or a recipient of the message.

15 . An apparatus comprising:

a processing circuitry;

a memory storing data; and

a communication circuitry, wherein the processing circuitry executes instructions associated with the data, the processing circuitry is coupled to the communication circuitry and the memory, and the processing circuitry and the memory cooperate, such that the apparatus is configured for:

providing instructions to a machine learning module (MLM) to generate a threshold for classifying spam in messages generated in a tiered software framework, wherein:

the instructions comprise inputs, including at least one of:

government regulations related to spam;

carrier guidelines related to spam;

feedback on messages previously sent from the tiered software framework; and

messages previously flagged as spam, and

the instructions specify that false positives are messages falsely classified as containing spam, false negatives are messages falsely classified as not containing spam, and the threshold is to prevent false positives and allow false negatives;

receiving, from the MLM, at least one threshold according to the instructions;

parsing a message generated in the tiered software framework;

automatically performing a semantic search using natural language processing for regulated content in the parsed message by comparing semantics of text of the parsed message to the inputs to find matches;

assigning a score to the message based on any matches found; and

responsive to the score being higher than the threshold, blocking sending the message from the tiered software framework.

16 . The apparatus of claim 15 , further configured for generating, by the MLM, keywords according to the inputs, wherein: semantic analysis of the content in the message includes:

identifying one or more of the keywords in the parsed message;

identifying a context of the identified keywords in the message; and

assigning the score based on the identified keywords in the context.

17 . The apparatus of claim 16 , wherein generating the keywords comprises:

identifying a first set of keywords in at least one of the government regulations or the carrier guidelines;

adjusting the first set of keywords using learning algorithms based on at least one of the feedback or the messages previously flagged as spam.

18 . The apparatus of claim 15 , further configured for:

collecting new feedback on the message; and

retraining the MLM based on the new feedback.

19 . The apparatus of claim 15 , further configured for providing a tiered software framework comprising a plurality of tiers, wherein an account in one tier manages data of a subaccount in another tier.

20 . The apparatus of claim 19 , wherein:

the message is generated in the subaccount, and

the feedback is from at least one of a network carrier, the account, or a recipient of the message.