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: 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: Disclosed systems and methods incorporate machine learning to assess damage to vehicles. An example method includes: accessing image data representing one or more digital images of damage to a vehicle; selecting, using one or more processors, an applicable machine learning algorithm from a plurality of different trained machine learning algorithms, wherein the plurality of different machine learning algorithms are trained for respective different combinations of one or more of vehicle make, vehicle model, vehicle year, or area of damage; processing the image data, with the applicable machine learning algorithm using one or more processors, to determine assessed damage for the vehicle; accessing actual damage information for the vehicle; determining, using one or more processors, differences between the actual damage and the assessed damage; and iterating, using one or more processors, the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy.

Abstract: Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having autonomous or semi-autonomous operation features are provided. According to certain aspects, a computer-implemented method for generating or updating usage-based insurance policies for autonomous or semi-autonomous vehicles may be provided. A request to generate an insurance quote may be received via wireless communication, and with the customer's permission, risk levels associated with intended usage by the customer of an autonomous or semi-autonomous vehicle may be determined. An insurance policy may be adjusted based upon the risk levels and the intended vehicle usage. The insurance policy may then be presented on the customer's mobile device for review and approval. In some aspects, the vehicle may be rented, and the intended vehicle usage is measured in distance or duration of vehicle operation. Insurance discounts may be provided to risk averse vehicle owners based upon low risk levels.

Abstract: In a general aspect, a modular quantum processing unit (QPU) includes quantum processor chips inter-connected by a cap structure. In some cases, a modular quantum processing unit includes a tunable-frequency coupler device, a first quantum processor chip, a second quantum processor chip, and a cap structure. The tunable-frequency coupler device includes a superconducting quantum interference device (SQUID) loop, a shunt capacitor, and a flux bias control line that controls a magnetic flux through the SQUID loop. The first quantum processor chip includes a first qubit device, the SQUID loop, the flux bias control line, and a first capacitive coupler device galvanically connected between the first qubit device and the tunable-frequency coupler device. The second quantum processor chip includes a second qubit device.

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: 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: 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 providing real-time feedback to a driver of a vehicle based on real-time datasets is disclosed. The systems and methods include obtaining one or more sets of real-time data associated with a vehicle. Performing one or more operations on the set of real-time data, wherein the one or more operations are based on the set of real-time data. Generating, a feedback alert based on results of the one or more operations and one or more detected real-time behaviors of the vehicle. Providing the feedback alert for presentation at a user interface on-board the vehicle. The real-time data may include contextual data, environmental data, operating data, data for a plurality of drivers, or combinations thereof.

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: An oscillatory respiratory therapy device (100) has a rocker aim (105) that opens and closes a valve seat (106) by breathing through the device. The device (100) also includes a gas treatment arrangement including an HME (122) and filter (121) located such that both inhaled and exhaled gas passes through the HME and filter.

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: 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 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: Methods and systems for validating proper fastening of vehicle seats via an application executing on a mobile computing device. The method and systems may include: obtain input data via an input interface of the mobile computing device, the input data including one or more of vehicle data, vehicle seat data, or child data; obtain underlay layer data generated by an image sensor of the mobile computing device; generate overlay layer data based upon the input data and/or the underlay layer data, the overlay layer data including an indication associated with proper fastening of the vehicle seat; corelate the overlay layer data with the underlay layer data; the one or more processors to create an AR display based upon the correlation; and/or present the AR display via an AR interface of the mobile computing device.

Abstract: Methods and systems for maintaining and classifying data related to vehicle seats stored on a blockchain. The systems and methods may include (1) detecting one or more sets of data relating to vehicle seats; (2) executing a data maintenance smart contact to classify the sets of data as being publicly accessible or proprietary; (3) based upon the classification assigned by the data maintenance smart contract, adding the one or more sets of data to a data repository accessible via a blockchain; (4) detecting a request to access a requested set of proprietary data from a requestor; (5) executing a security smart contract to determine whether the requestor is authorized to access the requested set of proprietary data; and/or (6) enforcing the determination of the security smart contract with respect to the request.

Abstract: A system and method are provided for controlling an interior configuration of a vehicle following a collision. Sensor data that includes, or is derived from data that includes, data collected by one or more sensors is received, and a vehicle accident condition indicative of an accident having occurred is detected by processing the sensor data. After detecting the vehicle accident condition, an actuator component is caused to prevent a passenger from adjusting an interior vehicle component outside a predetermined range of physical configurations, while allowing the passenger to adjust the interior vehicle component within the predetermined range of physical configurations.

Abstract: Systems and methods for automatically evaluating a vehicle seat replacement after a vehicle collision. Upon detecting a vehicle collision is detected, obtaining vehicle seat condition data from vehicle seat sensors and vehicle condition data from vehicle sensors. Analyzing the vehicle seat condition data and/or the vehicle condition data to detect one or more collision conditions indicating (i) the vehicle is not drivable, (ii) a vehicle door proximate the vehicle seat is damaged, (iii) an airbag of the vehicle is deployed, and/or (iv) the vehicle seat is visibly damaged. In response to detecting the one or more collision conditions, generating a recommendation to replace the vehicle seat and providing the recommendation to a user device.

Abstract: Systems and methods for simulating a vehicle seat in a vehicle using augmented reality. The systems and methods overlay a virtual vehicle seat model associated with a physical vehicle seat onto a physical vehicle via a display of the viewer device to generate a virtual configuration of the virtual vehicle seat model in the physical vehicle.