A method for optimizing chat-based conversations using machine learning is disclosed, comprising the use of machine learning to improve the likelihood of the chat-based conversation attaining a long-term goal or maintaining the engagement/interest level of an entity engaged in the conversation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for optimizing chat based communication between a first entity and a second entity using an electronic computing device, wherein the communication comprises a long-term goal, the method comprising: causing the electronic computing device to receive a first message from the first entity; causing the electronic computing device to receive a second message from the second entity; analyzing the second message for each of a plurality of predictors; performing a cluster analysis of the second message to determine the subject matter of the second message; analyzing the second message using a machine learning algorithm; assigning a short-term outcome score to the second message based on the results of the steps of analyzing the second message; assigning a long-term outcome score to the second message based on the results of the steps of analyzing the second message; determining a phase of the conversation based on the short-term outcome score, long-term outcome score, and cluster analysis; prescribing a micro-strategy to the first entity based on the short-term outcome score, wherein the micro-strategy comprises at least one change the first entity can make to at least one future message to improve a short-term outcome score for the next message of the second entity; prescribing a macro-strategy to the first entity based on the phase of the conversation, wherein the macro-strategy comprises at least one change the first entity can make to at least one future message to improve the likelihood of achieving the long-term goal. 2. The method of claim 1, wherein at least one of the short-term outcome score and the long-term outcome score further comprises at least one sub-score, wherein the at least one sub-score is based on a predictor. 3. The method of claim 1, wherein the short-term outcome score correlates to the engagement level of the second entity. 4. The method of claim 1, wherein the long-term outcome score correlates to the likelihood of attaining the long-term goal. 5. The method of claim 1, wherein the predictors are selected from a group comprising: frequency of the use of specific keywords; frequency of the use of specific phrases; use of punctuation, spelling, grammar, acronyms, and capitalization, wherein said use may be either proper or novel; frequency of the use of words, symbols, abbreviations or acronyms conveying emotion; polarity of sentiment of the message; magnitude of sentiment of the message; time delay between a remark by the first entity and a remark by the second entity; length of the second message; complexity of the second message; time interval between the first message and second message. 6. The method of claim 1, wherein the step of analyzing the second message using a machine learning algorithm comprises: data-mining a content database, said content database comprising a plurality of text-based conversations, each text-based conversation comprising a long-term goal; constructing a probabilistic model based on the content database; applying the probabilistic model to the current conversation; using the probabilistic model to determine a response that maximizes the probability of attaining the long-term goal; and wherein the macro-strategy comprises suggesting the response to the first entity. 7. The method of claim 1, wherein the step of prescribing a macro-strategy to the first entity comprises: creating a database of strategies for each phase of a conversation, each strategy indexed by phase of the conversation; determining the short-term outcome score of the second entity; determining the long-term outcome score of the second entity; identifying a phase of the conversation; using the phase of the conversation, short-term outcome score, and long-term outcome score to identify a macro-strategy in the database; suggesting the macro-strategy to the first entity. 8. The method of claim 4, further comprising: using the machine learning database to identify words, phrases, or tactics to assist the first entity in implementing the macro-strategy; guiding the first entity through the execution of the macro-strategy by the recommendation of said words, phrases, or tactics. 9. The method of claim 4, further comprising: after the communication concludes, determining whether or not the long-term goal has been attained; entering the communication into a content database; entering information about whether or not a suggested macro-strategy was used into a content database. 10. The method of claim 1, further comprising: analyzing the personality of the second entity to determine a personality type for the second entity; creating a database of suggested responses indexed by user personality type, subject matter, and phase of the conversation; wherein the macro-strategy comprises suggesting at least one response to the first entity, wherein the step of suggesting a response comprises: selecting a plurality of suggested responses appropriate for the personality type of the second entity, subject matter, and phase of the conversation; scoring each suggested response based on at least one of the following factors: length, emotive language, sentiment, spelling, grammar; selecting a response with the highest score; suggesting the response with the highest score to the first entity. 11. The method of claim 10, wherein the step of analyzing the personality of the second entity comprises at least one of the following: analyzing a writing sample of the second entity, extracting at least one predictor from the writing sample, determining a relationship between the at least one predictor and a writer's personality based on at least one past writing sample and at least one personality test result, and using the relationship to determine the second entity's personality; analyzing key factors of the conversation, wherein the key factors may be selected from the following: response time, spelling, grammar, use of symbols, use of acronyms or abbreviations, emotive language, use of emoji; analyzing a personality test taken by the second entity. 12. The method of claim 10, further comprising: if the first entity used the suggested response, determining the short-term outcome score after the suggested response; determining the long-term outcome score after the suggested response; analyzing any factors that may have led to the first entity's use of the suggested response; recording the effect of the suggested response on the long-term outcome score and the short-term outcome score in the database; if the first entity used a different response rather than the suggested response, determining the short-term outcome score after the different response; determining the long-term outcome score after the different response; analyzing any factors that may have led to the first entity's non-use of the suggested response; recording the effect of the non-use of the suggested response on the long-term outcome score and the short-term outcome score in the database. 13. The method of claim 1, wherein the step of prescribing a micro-strategy to the first entity comprises: creating a database of conversational rules, said conversational rules comprising at least one of the following: appropriate timing of the response, appropriate length of the response, appropriate content for the response, appropriate grammar, appropriate punctuation; analyzing the first message to determine if at least one of the conversational rules has been violated; if at least one conversational rule has been violated, performing one of the following actions: optimizing the conversation by informing the first entity of at least one conversational rule; suggesting a correction to the first entity; automatically correcting a message by the first entity. 14. The method of claim 1, further comprising at least one of the following: displaying the long-term outcome score for the first entity; displaying the short-term outcome score for the first entity; displaying the personality of the second entity to the first entity; displaying a macro strategy for the first entity; displaying a micro strategy for the first entity. 15. The method of claim 1, wherein the step of prescribing the micro-strategy further comprises: automatically changing at least one remark entered by the first entity in order to improve at least one of the following: the long-term outcome score, the short-term outcome score.