Abstract: Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving telematics data related to an insured asset; (2) analyzing the telematics data to identify a characteristic associated with the insured asset; (3) identifying a record on the blockchain related to the asset; (4) recording the indication of the detected characteristic associated with the asset; and/or (5) updating one or more entries of the asset record associate with the asset.

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: Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle information related to a vehicle; (2) analyzing the vehicle information to identify one or more safety features installed in the vehicle; (3) generating, based upon the one or more safety features identified, a smart contract that may be configured to (i) maintain a set of indications related to whether safety features in the one or more safety features installed in the vehicle have been activated, and (ii) automatically execute on the blockchain based upon the maintained set of indications; and/or (4) deploying the smart contract at a particular address on the blockchain.

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: A roof assessment (RA) computing device may be programmed to store a roof assessment model within memory wherein the roof assessment model may be configured to determine a roof status of a selected roof after experiencing a weather event, output from the roof assessment model target weather event parameters for the selected roof wherein the target weather event parameters are weather event parameters that result in at least repairable damage to the selected roof, store the target weather event parameters in memory for the selected roof, determine that the selected roof has experienced a first weather event having weather event parameters that meet the target weather event parameters, and/or transmit a message to a user computing device associated with the selected roof advising that a claim associated with an insurance policy has been triggered as a result of the selected roof experiencing the first weather event.

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: Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle information related to a vehicle; (2) analyzing the vehicle information to identify one or more safety features installed in the vehicle; (3) generating, based upon the one or more safety features identified, a smart contract that may be configured to (i) maintain a set of indications related to whether safety features in the one or more safety features installed in the vehicle have been activated, and (ii) automatically execute on the blockchain based upon the maintained set of indications; and/or (4) deploying the smart contract at a particular address on the blockchain.

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: 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: Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle information related to a vehicle; (2) analyzing the vehicle information to identify one or more safety features installed in the vehicle; (3) generating, based upon the one or more safety features identified, a smart contract that may be configured to (i) maintain a set of indications related to whether safety features in the one or more safety features installed in the vehicle have been activated, and (ii) automatically execute on the blockchain based upon the maintained set of indications; and/or (4) deploying the smart contract at a particular address on the blockchain.

Abstract: Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle information related to a vehicle; (2) analyzing the vehicle information to identify one or more safety features installed in the vehicle; (3) generating, based upon the one or more safety features identified, a smart contract that may be configured to (i) maintain a set of indications related to whether safety features in the one or more safety features installed in the vehicle have been activated, and (ii) automatically execute on the blockchain based upon the maintained set of indications; and/or (4) deploying the smart contract at a particular address on the blockchain.

Abstract: Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle information related to a vehicle; (2) analyzing the vehicle information to identify one or more safety features installed in the vehicle; (3) generating, based upon the one or more safety features identified, a smart contract that may be configured to (i) maintain a set of indications related to whether safety features in the one or more safety features installed in the vehicle have been activated, and (ii) automatically execute on the blockchain based upon the maintained set of indications; and/or (4) deploying the smart contract at a particular address on the blockchain.

Abstract: Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle information related to a vehicle; (2) analyzing the vehicle information to identify one or more safety features installed in the vehicle; (3) generating, based upon the one or more safety features identified, a smart contract that may be configured to (i) maintain a set of indications related to whether safety features in the one or more safety features installed in the vehicle have been activated, and (ii) automatically execute on the blockchain based upon the maintained set of indications; and/or (4) deploying the smart contract at a particular address on the blockchain.

Abstract: An external optical feedback element (108) for tuning an output beam of a gas laser (102) having multiple wavelengths includes a partially reflective optical element (108) positioned on a beam path of the output beam (106) outside of an internal optical cavity of the gas laser (102), and a stage (114) to support the optical element and adjust rotation, horizontal tilt angle, and vertical tilt angle of the optical element with respect to the beam path. The output beam (106) is partially reflected at the optical element (108) and fed back into the internal optical cavity of the gas laser (102), with the intensity varying for multiple wavelengths and adjusted by changing rotation, horizontal tilt angle and vertical tilt angle of the optical element. Thereby, a variable feedback of the output beam into the internal optical cavity of the gas laser is provided, which leads to a selective output wavelength of the gas laser, either at a single line or at multiple lines simultaneously.