Abstract: A computer-implemented auditory biometric method generates a playlist file including at least one audio file. The method includes receiving user biometric data and applying user biometric data to a trained auditory biometric model to generate a playlist file. The auditory biometric model may be trained using training data including a plurality of historic records associated with a plurality of historic users. The method may include transmitting a playlist message including the playlist file to a user computer device for execution by the user computer device.

Abstract: Systems and methods disclosed herein relate to fine-tuning machine learning (ML) chatbots for an enterprise. The systems and methods may use ML chatbots and/or generative ML to generate social media content for a user associated with an enterprise. The systems and methods may fine-tune a base ML model, and use the fine-tuned ML model for the ML chatbot. A user profile may indicate user attributes, and a fine-tuned ML model may be loaded for the ML chatbot based upon an identified user profile.

Abstract: An embodiment of the present invention is directed to automated trust discretionary distribution decisions. The innovative system comprises a computer server configured to perform the steps of: receiving, via an electronic input, a trust beneficiary cash distribution request relating to a trust instrument; responsive to the trust beneficiary request, obtaining trust details relating to the trust instrument; applying, via a computer server, a trust decision predictor to the distribution request to generate a trust decision wherein the trust decision predictor considers a set of decision factors comprising the trust beneficiary cash distribution request, beneficiary details, trust details and applicability of governing restrictions; presenting, via an electronic interface, the trust decision; automatically executing the trust decision; and applying feedback data to refine and standardize the trust decision predictor.

Abstract: A protection service management system may (1) detect a change in context information associated with a user having a protection service plan that protects an asset of the user, the context information being automatically determined without the user being required to manually provide the context information and including location information associated with the asset of the user; (2) determine a modification of the protection service plan of the user based upon the change in the context information; (3) transmit a prompt to a user device associate with the user, the prompt requesting approval of the modification; and/or (4) initiate the modification upon receiving approval of the modification via the user device.

Abstract: A computer-implemented auditory biometric method generates a playlist file including at least one audio file. The method includes receiving user biometric data and applying user biometric data to a trained auditory biometric model to generate a playlist file. The auditory biometric model may be trained using training data including a plurality of historic records associated with a plurality of historic users. The method may include transmitting a playlist message including the playlist file to a user computer device for execution by the user computer device.

Abstract: Apparatuses, systems and methods are disclosed for providing customer-specific information. The method comprises: (1) receiving, by one or more processors from a user device, a first prompt associated with a customer; (2) creating, by the one or more processors, a vector associated with the first prompt; (3) comparing, by the one or more processors, the vector associated with the first prompt with vectors associated with a plurality of documents associated with the customer to identify one or more candidate documents from the plurality of documents; (4) creating, by the one or more processors, a second prompt based upon the first prompt and the one or more candidate documents; (5) inputting, by the one or more processors, the second prompt into a chatbot to obtain a response to the second prompt; and/or (6) presenting, by the one or more processors, the response via the user device.

Abstract: Apparatuses, systems, and methods are provided for career development are provided. The method comprises: (1) receiving, by one or more processors from a user device, an information element associated with a user; (2) generating, by the one or more processors via a chatbot, career development suggestions based upon the information element associated with the user; and/or (3) presenting, by the one or more processors to the user via the user device, the career development suggestions. The chatbot may implement a trained model.

Abstract: Apparatuses, systems, and methods are provided for training employees are provided. The method comprises: (1) receiving, by one or more processors from a user device associated with an employee, an answer associated with a question; (2) generating, by the one or more processors via a chatbot, values associated with one or more metrics of the answer; and/or (3) performing, by the one or more processors, at least one of the following: (i) determining a skill level of the employee based upon the values associated with the one or more metrics of the answer; (ii) presenting, to the employee via the user device, a second question based upon the values associated with the one or more metrics of the answer; and/or (iii) presenting, to the employee via the user device, an explanation based upon the values associated with the one or more metrics of the answer.

Abstract: Systems and methods disclosed herein relate to fine-tuning machine learning (ML) chatbots for an enterprise. The systems and methods may use ML chatbots and/or generative ML to provide information to a user in a manner emulating the traits of a person. The systems and methods may fine-tune a base ML model, and use the fine-tuned ML model for the ML chatbot. The user may indicate a person associated with the fine-tuned ML model, and the fine-tuned ML model associated with the person may be loaded for the ML chatbot.

Abstract: Systems and methods disclosed herein relate to fine-tuning machine learning (ML) chatbots for an enterprise. The systems and methods may use ML chatbots and/or generative ML to provide information to a user based upon the user's interaction at a portal of an enterprise. The systems and methods may fine-tune a base ML model, and use the fine-tuned ML model for the ML chatbot. The user's interactions may be classified as a type of user activity. A fine-tuned ML model may be loaded as the ML chatbot based upon the type of user activity.

Abstract: An embodiment of the present invention is directed to automated trust discretionary distribution decisions. The innovative system comprises a computer server configured to perform the steps of: receiving, via an electronic input, a trust beneficiary cash distribution request relating to a trust instrument; responsive to the trust beneficiary request, obtaining trust details relating to the trust instrument; applying, via a computer server, a trust decision predictor to the distribution request to generate a trust decision wherein the trust decision predictor considers a set of decision factors comprising the trust beneficiary cash distribution request, beneficiary details, trust details and applicability of governing restrictions; presenting, via an electronic interface, the trust decision; automatically executing the trust decision; and applying feedback data to refine and standardize the trust decision predictor.

Abstract: An embodiment of the present invention is directed to automated trust discretionary distribution decisions. The innovative system comprises a computer server configured to perform the steps of: receiving, via an electronic input, a trust beneficiary cash distribution request relating to a trust instrument; responsive to the trust beneficiary request, obtaining trust details relating to the trust instrument; applying, via a computer server, a trust decision predictor to the distribution request to generate a trust decision wherein the trust decision predictor considers a set of decision factors comprising the trust beneficiary cash distribution request, beneficiary details, trust details and applicability of governing restrictions; presenting, via an electronic interface, the trust decision; automatically executing the trust decision; and applying feedback data to refine and standardize the trust decision predictor.