Abstract: We provide an imaging system for use in a fluoroscopy procedure carried out on a subject, the imaging system being configured to receive images of a portion of the subject from an image acquisition device, the imaging system comprising an interface module for displaying images received from the image acquisition device to a user, and an optimisation module for determining, based on an image received from the image acquisition device, one or more properties of the image, wherein the optimisation module is configured to output a control routine.

Abstract: Apparatuses, systems and methods are provided for generating customized code. The apparatuses, systems and methods may cause a chatbot to generate customized code. The apparatuses, systems and methods may integrate the customized code in an insurance application. The customized code, when executed by one or more processors, may cause the one or more processors to collect user information from a user device, determine whether a change of insurance policy for the user is required, and responsive to determining that a change of insurance policy is required, automatically update the insurance policy in the insurance application based upon the required change.

Abstract: Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, whether there is a solution to fix the error. The apparatuses, systems and methods may, responsive to determining that there is a solution to fix the error, cause the ML chatbot to (i) fix the error, and/or (ii) present the error and/or the solution to a user.

Abstract: Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, a solution to fix the error. The apparatuses, systems and methods may analyze the solution to determine a number of at least one of (i) a set of steps or (ii) a set of interactions required by the solution. The apparatuses, systems and methods may, responsive to determining that the number exceeds a predetermined threshold, fix the error by implementing the solution with respect to the target code.

Abstract: A computer system may include one or more processors configured to: (1) obtain insurance claim information; (2) generate, via an ML chatbot (or voice bot), the customized presentation based upon the insurance claim information; and/or (3) provide, via the ML chatbot, the customized presentation to a user device, such as a visual depiction and/or a verbal description of the customized presentation.

Abstract: Techniques for intelligent coordination between parties for short-term rental or lending of items are disclosed herein. An exemplary computer-implemented method includes aggregating party data from the plurality of parties, and determining, by executing a machine learning (ML) chatbot, one or more items associated with each party of the plurality of parties. The ML chatbot is trained using a plurality of training party data from a plurality of training parties as input to output a plurality of training items associated with the plurality of training parties. The computer-implemented method may further include generating, by executing the ML chatbot, a set of associated parties that includes at least a pair of parties from the plurality of parties for each item of the one or more items; and for each set of parties, initiating contact between parties.

Abstract: Systems and methods for an artificial intelligence (AI)/machine learning (ML) chatbot for intelligent negotiation of terms is disclosed herein. An exemplary computing system includes one or more processors; and a memory storing executable instructions thereon. When executed by the one or more processors, these instructions may cause the one or more processors to: receive, from a first contracting party during a negotiating period, a plurality of prospective terms for a contractual agreement; and determine, via an AI chatbot, whether the plurality of prospective terms are acceptable terms based on a plurality of parameters indicated by a second contracting party. Responsive to determining that the plurality of prospective terms are unacceptable terms, the instructions may further cause the processors to generate, via the AI chatbot, a plurality of counter terms based on the plurality of parameters; and transmit the plurality of counter terms to the first contracting party.

Abstract: An apparatus and method for transferring a fluid sample from a fluid sample collection apparatus to a liquid sample analyzer. The apparatus includes a barrel having a first end, a second end, a sidewall extending between the first end and the second end, an inner surface defining an internal chamber, and an external surface defining at least a portion of a chromatographic assay chamber in fluid communication with the internal chamber. The first end has an inlet opening with a clot catcher extending across the inlet opening upstream of the passage and the second end has an outlet opening. A chromatographic assay assembly is housed in the chromatographic assay chamber for detecting presence of free hemoglobin in the fluid sample.

Abstract: The following relates generally to using artificial intelligence (AI) and/or machine learning (ML) to identify patentable ideas from company emails. In some embodiments, one or more processors: access one or more email messages; identify, via a machine learning (ML) chatbot trained to analyze text and generate text, one or more patentable concepts in the one or more email messages; compare the one or more patentable concepts to a patent database including published patent applications with respective publication dates on or before the date of database access, wherein comparing includes generating a comparison document indicating at least one difference and/or at least one similarity between the one or more patentable concepts and the patent applications of the patent database, wherein the comparing includes using the ML chatbot; and generate, based upon the comparison, one or more novelty scores for each of the one or more patentable concepts.

Abstract: Receiving a first notice of loss (FNOL) using a machine learning (ML) chatbot (or voice bot) when initiating an FNOL session with a user device to obtain FNOL information. Generating one or more requests for claim information by the ML chatbot based upon the FNOL information to provide to the user device during the FNOL session. Receiving the claim information by the ML chatbot from the user device based upon the one or more requests for the claim information. Analyzing information from the FNOL session to determine one or more session actions and implementing the one or more session actions.

Abstract: A system for providing dynamic roadside assistance coordination may include a customer mobile device or vehicle in direct, or indirect, wireless communication with an insurance provider and/or service provider remote server. The customer's device may transmit a request for roadside assistance (e.g., towing services), and a current GPS location. In response, a closest, trusted service provider vehicle may be determined, and then directed to the customer's location. For instance, a software application may receive the customer location and compare it with availability information to match the customer with a close and trusted service provider. The customer's device may receive an acknowledgement that help is on the way, and be able to track the current location of the service provider vehicle while en route. Payment for the roadside assistance may be automatically and electronically paid by the insurance provider. As a result, prompt and safe roadside assistance may be provided.

Abstract: The following relates generally to using artificial intelligence (AI) and/or machine learning (ML) to recommend a change in insurance coverage. In some embodiments, one or more processors: receive customer information of an insurance customer; determine that an insurance change recommendation trigger has occurred by routing the customer information into an insurance recommendation machine learning and/or artificial intelligence algorithm; and in response to determining the insurance change recommendation trigger, send a recommendation for an insurance policy change for the insurance customer.

Abstract: The following relates generally to aiding underwriting and insurance agents. In some embodiments, one or more processors: (1) receive, with an AI chatbot (or voice bot) of the one or more processors, an input statement; (2) determine, with the AI chatbot, from the received input statement, a type of insurance policy; (3) determine, with the AI chatbot, from the input statement, a question corresponding to the type of insurance policy; (4) determine, with the AI chatbot, an answer to the question by retrieving insurance information based upon (i) the type of insurance policy, and/or (ii) the question; and/or (5) present, via the AI chatbot, the answer to the question.

Abstract: The following relates generally to using AI and/or ML to build and/or present an insurance contract. In some embodiments, one or more processors: (1) receive, with an AI chatbot of the one or more processors, an input statement; (2) determine, with the AI chatbot, from the received input statement, a type of insurance policy, wherein the type of insurance policy comprises a homeowners insurance policy, a renters insurance policy, an auto insurance policy, a life insurance policy, a disability insurance policy, and/or an umbrella insurance policy; (3) build, with the AI chatbot, an insurance contract based upon the type of insurance policy; and/or (4) present, via the AI chatbot, the insurance contract to the potential insurance customer.

Abstract: The following relates generally to building a ground truth insurance database. In some embodiments, one or more processors: (1) receive potential insurance database information comprising (i) insurance company application (app) information, (ii) anonymized insurance claim information, (iii) police report information, and/or (iv) auxiliary information; (2) retrieve, from an insurance ground truth insurance database, existing ground truth insurance information by querying the ground truth database based upon the potential insurance database information; (3) determine if the potential insurance database information should be added to the insurance ground truth database by comparing the potential insurance database information to the existing ground truth insurance information; and/or (4) if the potential insurance database information should be added to the insurance ground truth database, add the potential insurance database information to the insurance ground truth database.

Abstract: A casket includes a casket body, at least a first lid, and a backer board. The casket body is configured to receive an adult human body in the supine position. The lid has a first bottom interior ledge defining a first nook in an underside of the lid, and a second bottom interior ledge defining a second nook in the underside of the lid. The backer board is formed of a pliable material, and includes a center wall having a first edge and an opposite second edge. The backer board also includes a first side wall extending away from the first edge, and a second side wall extending away from the second edge. The first side wall and the second side wall are configured to engage, respectively, the first and second nooks, under a tension configured to hold the center wall in a predetermined position.

Abstract: Systems and methods are described for generating transactions based upon rideshare data and updating a distributed ledger. The method may include (1) monitoring a distributed ledger for an indication of an accident, the indication of the accident identifying at least a user and a time period; (2) obtaining rideshare data from the distributed ledger, wherein the rideshare data: identifies one or more rideshare companies for which the user offered driving services during the time period; and (3) verifying, based upon the rideshare data, a controlling party, wherein the controlling party is one of the user or one of the one or more rideshare companies.

Abstract: Autonomous vehicle methods and systems are described herein for communicating with an autonomous or semi-autonomous vehicle to remotely control operation of the vehicle, to detect and remove unauthorized passengers, to deliver loads, to receive registration information for the vehicle, to provide accessibility information to the vehicle, and/or to receive sensor or other environmental data to integrate with an electronic game or other extended reality experience.

Abstract: Methods, systems, apparatus, and non-transitory computer readable media are described for using a vehicle as a payment device. Various aspects may include receiving a selection of a stored financial card or financial account at a vehicle head unit. The selected financial card or financial account may be transmitted to a point-of-sale (POS) terminal for making a payment by transmitting a tokenized card number to the POS terminal. The tokenized card number may be transmitted over a very short-range communication link to ensure that the transmission is secure. For example, electronic circuitry may be attached to the exterior of the vehicle, where the electronic circuitry may be within a threshold distance (e.g. one inch, three inches, six inches, one foot, three feet, etc.) of the POS terminal. The tokenized card number may be transmitted from the vehicle head unit to the electronic circuitry and then to the POS terminal.