Abstract: Methods and systems are disclosed herein for secure communication between computing devices. A mobile device may communicate with an untrusted device to cause the untrusted device to send information (e.g., encrypted information that the untrusted device is unable to decrypt) to a server using an Internet connection of the untrusted device. The mobile device may have limited or no access to the Internet. To prevent potential security risks associated with using a public or untrusted device, the mobile device may encrypt information stored on the mobile device (e.g., stored in a mobile application associated with the server), send it to the untrusted device (e.g., by displaying a QR code to a camera of the untrusted device), and the untrusted device may send the information to the server via a network connection of the untrusted device.

Abstract: In some implementations, a system may identify, using a machine learning model, a pattern of events based on account information associated with a user. The system may generate a prediction of a time period during which a predicted event, associated with the pattern of events, is predicted to occur. The system may receive an indication that a vehicle, associated with the user, is to travel a route from a starting location to an ending location during the time period. The system may identify a physical location associated with the predicted event based on a location associated with the vehicle or based on the route. The system may generate a new route that includes the starting location, the physical location as a waypoint along the new route, and the ending location. The system may transmit information that identifies the new route to a device associated with the vehicle.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: A connection system for a linear lighting system has channels of adjacent luminaires coupled together by a coupling arrangement having a first plate, a second plate and fixings for clamping the first and second plates around wall portions of the two channels. The fixings are slidable along slots so that the coupling arrangement can be slid into an operable position or to an out-of-the way position for disassembly. One of the first and second plates comprises a spring member having sprung projections for projecting into the apertures, to provide an automatic clamping function when the coupling arrangement is tightened. For example, there may be an outer coupling plate and an inner spring plate which forms the spring member.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: A system for dynamically routing customer calls. For example, the system may receive user interaction data associated with a first user using a first user device. The system may also receive a phone call from a user using a first phone number. The system may also identify the user via the first phone number. The system may determine, using a first machine learning model, whether the first user has a first emotion type based on the user interaction data. When the first user does not have the first emotion type, the system may route the first user to any call center representative. When the first user has the first emotion type, the system may route the first user to a first call center representative among one or more first call center representatives.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: An automated method is provided for activating a NFC application in a mobile interface device. In this method, the device receives from a beacon a wireless beacon signal comprising at least one beacon identifier. The wireless beacon signal is processed by the mobile interface device to determine the at least one beacon identifier. The mobile interface device determines based on the at least one beacon identifier, whether to activate the NFC application. Responsive to a determination that the NFC application should be activated, the mobile interface device transitions the NFC application from a passive state to an active state. NFC communication is then established between the mobile interface device and at least one NFC transmitting device disposed within a beacon range volume defined by the beacon location and the beacon range.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: A connection system for a linear lighting system has channels of adjacent luminaires coupled together by a coupling arrangement having a first plate, a second plate and fixings for clamping the first and second plates around wall portions of the two channels. The fixings are slidable along slots so that the coupling arrangement can be slid into an operable position or to an out-of-the way position for disassembly. One of the first and second plates comprises a spring member having sprung projections for projecting into the apertures, to provide an automatic clamping function when the coupling arrangement is tightened. For example, there may be an outer coupling plate and an inner spring plate which forms the spring member.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing recommendations for splitting bills. The approaches disclosed include the ability to obtain information about a bill to be split (such as a photo of the bill), and then use several machine learning models to determine the ‘who,’ ‘what,’ and ‘where’ of the underlying transaction. In particular, machine learning models described herein are used to perform facial recognition of a ‘selfie’ taken when a transaction was made against social media accounts to determine participants of the transaction. The machine learning models may also identify expected pricing from data about a merchant associated with the transaction, and expected amounts for each participant based on the expected pricing.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing recommendations for splitting bills. The approaches disclosed include the ability to obtain information about a bill to be split (such as a photo of the bill), and then use several machine learning models to determine the ‘who,’ ‘what,’ and ‘where’ of the underlying transaction. In particular, machine learning models described herein are used to perform facial recognition of a ‘selfie’ taken when a transaction was made against social media accounts to determine participants of the transaction. The machine learning models may also identify expected pricing from data about a merchant associated with the transaction, and expected amounts for each participant based on the expected pricing.

Abstract: System and devices for preempting customer acceptance of predatory loan offers is disclosed. The system may store customer account data and customer contact information. The system may determine an applicable first loan tier, each loan tier associated with a predetermined loan offer and indicative of a financial health of the customer. The first loan tier may be determined based on the customer account data. The system may receive a GPS signal indicating that the customer has entered and remained in a geofenced area associated with a predatory loan provider. In response to determining that the loan merchant is a predatory loan provider, the system may update customer account data and determine a second loan tier based on the updated customer account data, wherein the second loan tier is indicative of a higher risk loan than the first loan tier. Before offering the loan, the customer may be authenticated.

Abstract: Systems and methods for decision-making with multi-channel data inputs are provided. The system includes a plurality of devices, and a server in data communication with the plurality of devices. The server includes a decision engine, a sentiment analysis machine learning model, and a behavior analysis machine learning model. The server is configured to: receive the at least one data input from each of the plurality of device; perform, using the sentiment analysis machine learning model, a sentiment analysis on the at least one data input to generate sentiment information indicative of an emotional state of a user; perform, using the behavior analysis machine learning model, a behavior analysis on the at least one data input to generate behavior information indicative of a behavioral state of the user; determine, using the decision engine, a responsive action based on the sentiment information and the behavior information; and perform the responsive action.

Abstract: Example embodiments of systems and methods for card production are provided. A card may include processing circuitry including a chip and memory. The card may include one or more antennas in communication with the chip. The card may include a first layer of material aligned within a perimeter of the card via laminate encapsulation. The first layer of material may comprise a non-rectangular shape. The first layer of material may be offset with a shape of the card. The first layer of material may be in communication with the chip. The first layer of material may comprise at least one selected from the group of steel, tungsten, titanium, or any combination thereof. The card may be compliant with one or more form factors.

Abstract: Disclosed herein are system, method, and computer program product embodiments for recommending a service provider. An embodiment operates by receiving a search request specifying a service request by a user and location for the requested service, retrieving prior financial transactions of the user to purchase the service from a database, and determining a preference of the user based on the retrieved prior financial transactions. The embodiment further operates by applying the preference to a model trained to output a recommended service provider and selecting the recommended service provider for display on a user's device. The model more likely recommends service providers in the location having a higher ratio of tip-to-price relative to other providers for the service in the location based on a history of prior financial transactions of a plurality of users.