Abstract: The invention provides in some aspects a transport system comprising a guideway having a plurality of regions in which one or more vehicles are propelled, where each such vehicle includes a magnet. Disposed along each region are a plurality of propulsion coils, each comprising one or more turns that are disposed about a common axis, such that the respective common axes of the plurality of coils in that region are (i) substantially aligned with one another, and (ii) orthogonal to a direction in which the vehicles are to be propelled in that region. The plurality of coils of at least one such region are disposed on opposing sides of the magnets of vehicles being propelled along that region so as to exert a propulsive force of substance on those magnets.

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: 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: Provided herein are oral dosage forms comprising a) a core tablet comprising (i) a drug layer comprising apremilast and hypromellose acetate succinate (HPMCAS) in an amorphous solid dispersion; and (ii) a swellable layer comprising one or more swellable polymers; and b) a coating layer disposed on the core tablet, wherein the oral dosage form surface comprises at least one drug release orifice. The disclosed oral dosage forms provide once-a-day dosing of apremilast and are suitable for treating diseases or disorders ameliorated by inhibiting phosphodiesterase subtype IV (PDE4).

Abstract: Systems, devices, and methods for intradermal or subdermal fluid delivery via a microneedle are provided. A delivery system can include an injector, fluid-filled cartridge or syringe, and a needle. A cartridge or syringe can be compatibly coupled with an injector device such that the injector device is capable of ejecting fluid out of the cartridge and though the needle. Fluid delivery systems can be utilized in a number of applications, including medications and supplements for the skin.

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: 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: Methods for identifying a cell population secreting a biomolecule with desirable attributes are disclosed. The desirable attributes can include, for example, quantity and quality (e.g., minimal aggregation and/or desired configuration). Cell populations identified by the disclosed methods are more likely to successfully scale during production. The methods can include assessing multiple domains/binding sites of a complex biomolecule and/or the formation of aggregates by the biomolecules. Methods of assessing a secretion level for biomolecules having a wide range of molecular weights and method for enhancing loading of cells into chambers of a microfluidic device.

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: 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 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 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: Systems and methods include extracting item parameters from images of items positioned at a POS system. The item parameters associated with each item are indicative as to an identification of each item thereby enabling the identification of each item based on the item parameters. The item parameters are analyzed to determine whether the item parameters match item parameters stored in a database. The database stores different combinations of item parameters to thereby identify each item based on each different combination of item parameters associated with each item. Each item positioned at the POS system is identified when the item parameters for each item match item parameters as stored in the database and fail to identify each item when the item parameters fail to match item parameters. The item parameters associated with the items that fail to match are streamed to the database thereby enabling the identification of each failed item.

Abstract: Systems and methods include extracting item parameters from images of items positioned at a POS system. The item parameters associated with each item are indicative as to an identification of each item thereby enabling the identification of each item based on the item parameters. The item parameters are analyzed to determine whether the item parameters match item parameters stored in a database. The database stores different combinations of item parameters to thereby identify each item based on each different combination of item parameters associated with each item. Each item positioned at the POS system is identified when the item parameters for each item match item parameters as stored in the database and fail to identify each item when the item parameters fail to match item parameters. The item parameters associated with the items that fail to match are streamed to the database thereby enabling the identification of each failed item.

Abstract: Assisted checkout devices, including point-of-sale stations, can use computer vision and machine learning to speed the checkout process while maintaining human verification, assistance, and customer interaction provided by human clerks. A plurality of optical sensors, including cameras, can be arranged with different views of a checkout plane upon which items being purchased by a buyer are placed. Moreover, one or more support towers can be utilized to elevate the optical sensors to vertical heights at which the checkout plane, and items placed thereon, is within the field of view of the optical sensors. The information captured by the plurality of optical sensors can be analyzed using machine learning models to detect and identify the items placed on the checkout plane.

Abstract: A mixing attachment for use with a reciprocating mechanism of a reciprocating hand tool, to quickly and efficiently mix contents of paint cans or other containers, may include a support member and one or more retention ring assemblies. The support member may be disconnectably connected to the reciprocating mechanism. The one or more retention ring assemblies may be attached to the support member. The retention ring assemblies may be mounted in a longitudinal slot on the support member and include an adjustable band and a securing mechanism for tightening the adjustable band about an outer surface of a paint can.