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: A computer system is provided and is programmed to: (1) receive an audible caller statement originating from a caller being routed to the representative; (2) parse the audible caller statement; (3) identify one or more key words from the parsed audible caller statement; (4) search an information database for one or more items of information based upon the one or more key words; (5) identify at least one of the one or more items of information to present to the representative; and (6) transmit the at least one identified items of information to be presented to the representative.

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 disclosed for improving quality of a property improvement recommendation generated by a chatbot. The method comprises: (1) receiving, from a user, a first prompt associated with a property; (2) analyzing the first prompt to generate a second prompt, wherein generating the second prompt includes: (i) retrieving one or more datasets based upon a distance between a feature vector of the first prompt and feature vectors of the one or more datasets, wherein the one or more datasets include data associated with the property; and (ii) generating the second prompt based upon the first prompt and the one or more datasets; (3) transmitting, to the chatbot, the second prompt to generate a response, the response comprising the property improvement recommendation; and (4) presenting the response to the user.

Abstract: Techniques are described herein for marking virtual objects. An exemplary system includes one or more processors; and a non-transitory computer-readable medium coupled to the one or more processors and storing instructions thereon. The instructions, when executed by the one or more processors, cause the one or more processors to: (1) receive a transaction listing from a first entity, the transaction listing including (i) a digital asset that is associated with a virtual object in a virtual environment and (ii) a status of the digital asset indicating an active association with the first entity, (2) generate a transaction based upon the transaction listing, the transaction including an indication of the virtual object, (3) record the transaction in a distributed ledger, and (4) responsive to recording the transaction in the distributed ledger, cause the status of the digital asset to be displayed when the virtual object is viewed in the virtual environment.

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: Techniques are described herein for marking virtual objects. An exemplary system includes one or more processors; and a non-transitory computer-readable medium coupled to the one or more processors and storing instructions thereon. The instructions, when executed by the one or more processors, cause the one or more processors to: (1) receive a transaction listing from a first entity, the transaction listing including (i) a digital asset that is associated with a virtual object in a virtual environment and (ii) a status of the digital asset indicating an active association with the first entity, (2) generate a transaction based upon the transaction listing, the transaction including an indication of the virtual object, (3) record the transaction in a distributed ledger, and (4) responsive to recording the transaction in the distributed ledger, cause the status of the digital asset to be displayed when the virtual object is viewed in the virtual environment.

Abstract: Apparatuses, systems and methods are disclosed for providing property improvement recommendations. The method comprises: (1) receiving, by one or more processors from a user, a first prompt associated with a property; (2) generating, by the one or more processors, a second prompt based upon the first prompt, data associated with the property and one or more property metrics associated with the property; (3) transmitting, by the one or more processors to a chatbot, the second prompt to generate a response, the response comprising a recommended action associated with the property; and/or (4) presenting, by the one or more processors, the response to the user.

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: 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: A user analytics computing device for processing mobile device telematics data and generating user offerings responsive to the mobile device telematics data is provided. The user analytics computing device comprises at least one processor programmed to generate an operator model for a user based upon historical telematics data, an output of the operator model to determine whether the user is operating a vehicle. The user analytics computing device is further programmed to input telematics data into the operator model, and in response to determining the user is operating the vehicle, generate a driver profile based upon the telematics data and the device mode data, generate, based upon the driver profile, a user offering, and transmit, to the mobile device of the user, the user offering.

Abstract: A computer system is provided and is programmed to: (1) receive an audible caller statement originating from a caller being routed to the representative; (2) parse the audible caller statement; (3) identify one or more key words from the parsed audible caller statement; (4) search an information database for one or more items of information based upon the one or more key words; (5) identify at least one of the one or more items of information to present to the representative; and (6) transmit the at least one identified items of information to be presented to the representative.

Abstract: Blockchain-based systems and methods for managing digital currency associated with a company or organization are disclosed. The digital currency may be used to reward or incentivize behaviors of employees, clients, and/or prospective clients or customers of a company, organization, or other entity. A blockchain computing device in accordance with the present disclosure may include at least one memory and at least one processor in communication with the at least one memory. The processor may be programmed to, in response to a predetermined criterion being met, automatically generate a reward token transaction, broadcast the reward token transaction to a blockchain network, validate the reward token transaction, and generate and store a new block on the blockchain network with the reward token transaction.

Abstract: Techniques are described herein for minting digital assets associated with user achievements in a distributed network. An exemplary system includes one or more processors; and a non-transitory computer-readable medium coupled to the one or more processors and storing instructions thereon. The instructions, when executed by the one or more processors, cause the one or more processors to: (1) receive an update from a platform application on a user computing device, the update including (i) an association indication between a first entity of the user computing device and a second entity and (ii) an achievement of the first entity; (2) generate a transaction based upon the update, the transaction including a digital asset associated with the update; (3) record the transaction in a distributed ledger; and (4) responsive to recording the transaction in the distributed ledger, transmit a notification to the user computing device indicating the digital asset.

Abstract: Techniques are described herein for marking virtual objects. An exemplary system includes one or more processors; and a non-transitory computer-readable medium coupled to the one or more processors and storing instructions thereon. The instructions, when executed by the one or more processors, cause the one or more processors to: (1) receive a transaction listing from a first entity, the transaction listing including (i) a digital asset that is associated with a virtual object in a virtual environment and (ii) a status of the digital asset indicating an active association with the first entity, (2) generate a transaction based upon the transaction listing, the transaction including an indication of the virtual object, (3) record the transaction in a distributed ledger, and (4) responsive to recording the transaction in the distributed ledger, cause the status of the digital asset to be displayed when the virtual object is viewed in the virtual environment.

Abstract: Techniques are described herein for managing transactions associated with vehicles using a distributed ledger maintained by a plurality of participants. An exemplary system includes one or more processors; and a non-transitory computer-readable medium coupled to the one or more processors and storing instructions thereon. The instructions, when executed by the one or more processors, cause the one or more processors to: (1) receive a transaction listing from a first entity, the transaction listing including (i) entity data corresponding to the first entity, (ii) a non-fungible token (NFT) corresponding to a vehicle, and (iii) an updated state of the vehicle; (2) generate a transaction including a description of the transaction listing; and (3) record the transaction in a distributed ledger. A vehicle identification number (VIN) of the vehicle may be linked to the NFT in the distributed ledger.