Abstract: The following relates generally to AI-based review of insurance claims complaints. In some embodiments, one or more processors: (1) receive, via a chatbot, an insurance claim complaint; (2) categorize, via the chatbot, the insurance claim complaint by determining a category of the insurance claim complaint, the category comprising a tone category or a policy category; (3) build, via the chatbot, a complaint report including information of the insurance claim complaint and an indication of the category; and/or (5) send, via the chatbot, the complaint report to an insurance complaint administrator computing device.

Abstract: The following relates generally to artificial intelligence (AI)-based responses to social media posts. In some embodiments, one or more processors may be configured to: (1) receive, via a chatbot, a social media post; (2) categorize, via the chatbot, the social media post; (3) determine, via the chatbot, based upon the categorization, an entity to contact; (4) build, via the chatbot, based upon the determined entity to contact, a response to the social media post; and/or (5) send, via the chatbot, the response to the entity.

Abstract: The following relates generally to artificial intelligence (AI)-based communication with an insurance customer. In some embodiments, one or more processors: receive, via a chatbot (or voicebot or other bot), a question in a first language; translate, via the chatbot, the question from the first language to a second language; determine, via the chatbot, an answer to the translated question, the answer being determined in the second language; translate, via the chatbot, the answer from the second language to the first language; and/or present, via the chatbot, the answer in the first language to an insurance customer.

Abstract: Methods, systems, and devices for interface techniques for stacked memory architectures are described. A semiconductor system, such as a memory system, may distribute memory access circuitry among multiple semiconductor dies of a stack. A first die of the system may include logic circuitry operable to configure a set of multiple first interface blocks of the first die. Each first interface block may include circuitry operable to communicate with one or more second interface blocks of one or more second dies of the system to access a respective set of one or more memory arrays of the one or more second dies. In some examples, the system may include a respective controller for each first interface block to support access operations via the first interface block. The system may also include non-volatile storage, one or more sensors, or a combination thereof to support various operations of the system.

Abstract: Methods and systems for alerting a user of crime-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors from a user device, location data of the user device and a destination input by the user; (2) determining, by the one or more processors, based upon the location data, that the user device is approaching the destination; (3) loading, by the one or more processors, a crime density map surrounding the destination; (4) determining, by the one or more processors, based upon the crime density map, at least one non-crime-prone parking spot, wherein the at least one non-crime-prone parking spot meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one non-crime-prone parking spot.

Abstract: Methods and systems for alerting a user of accident-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors and from a user device associated with the user, location data and motion data of the user device; (2) determining, by the one or more processors, based upon the motion data, that the user has started a trip; (3) loading, by the one or more processors, an accident density map surrounding a location of the user device based upon the location data; (4) determining, by the one or more processors, based upon the accident density map, at least one accident-prone location within a scope of the accident density map, wherein the at least one accident-prone location meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one accident-prone location.

Abstract: The following generally relates to using Augmented Reality (AR) to enhance pedestrian navigation. In some examples, AR techniques are applied to provide AR indications of a location of a group member that includes two or more AR devices. In these examples, AR techniques may be applied to determine a relative position of the group member and/or provide navigational guidance to the group member. In other examples, AR techniques are applied to provide AR-assisted pedestrian guidance. In these examples, AR techniques may be applied to present an AR display that includes information for a point of interest along a route.

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: 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: 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: 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.