Abstract: In one embodiment, a predictive contextual transaction scoring system receives transaction review data associated with person-to-person transactions from a marketplace, the transaction review data including feedback information relating to the person-to-person transaction. The system identifies contextual characteristics of the person-to-person transactions by analyzing the feedback information related to the person-to-person transactions. The system pairs a first party requesting a service to a second party providing the requested service by matching a contextual characteristic of the first party to a contextual characteristic of the second party. The system also predicts a transaction score for a new person-to-person transaction involving the first party and the second party, and provides the predicted transaction score to the marketplace to facilitate pairing of the first party requesting the service.

Abstract: In one embodiment, a predictive contextual transaction scoring system receives transaction review data associated with person-to-person transactions from a marketplace, the transaction review data including feedback information relating to the person-to-person transaction. The system identifies contextual characteristics of the person-to-person transactions by analyzing the feedback information related to the person-to-person transactions. The system pairs a first party requesting a service to a second party providing the requested service by matching a contextual characteristic of the first party to a contextual characteristic of the second party. The system also predicts a transaction score for a new person-to-person transaction involving the first party and the second party, and provides the predicted transaction score to the marketplace to facilitate pairing of the first party requesting the service.

Abstract: A trust platform receives transaction review values from a plurality of marketplace providers, each transaction review value associated with a person-to-person transaction. The platform weighs each transaction review value to generate a weighted transaction review value based on a characteristic of the transaction. A baseline individual trust score is generated based on an aggregation of the weighted transaction review values which reflects a “trust” attributable to a user. The trust platform is also configured to adjust new transaction review values based on the baseline individual trust score to render such review more accurate.

Abstract: In one embodiment, a predictive contextual transaction scoring system receives transaction review data associated with person-to-person transactions from a marketplace, the transaction review data including feedback information relating to the person-to-person transaction. The system identifies contextual characteristics of the person-to-person transactions by analyzing the feedback information related to the person-to-person transactions. The system pairs a first party requesting a service to a second party providing the requested service by matching a contextual characteristic of the first party to a contextual characteristic of the second party. The system also predicts a transaction score for a new person-to-person transaction involving the first party and the second party, and provides the predicted transaction score to the marketplace to facilitate pairing of the first party requesting the service.

Abstract: A trust platform receives transaction review values from a plurality of marketplace providers, each transaction review value associated with a person-to-person transaction. The platform weighs each transaction review value to generate a weighted transaction review value based on a characteristic of the transaction. A baseline individual trust score is generated based on an aggregation of the weighted transaction review values which reflects a “trust” attributable to a user. The trust platform is also configured to adjust new transaction review values based on the baseline individual trust score to render such review more accurate.

Abstract: In one embodiment, a smart home modeling system receives objective data from a public data source. The system modifies baseline age information from the objective data based on indications from service and upgrade records, as well as smart home sensor measurements, to generate a corrected age model for the home. The system determines the effective ages of systems in the home in the corrected age model, and then uses the corrected age model to generate a risk model for the home. Based on the generated risk model, maintenance needs and statuses are re-evaluated. The system provides a virtual dashboard to view results from the smart home model. The dashboard displays alerts about particular systems of the home with elevated risk levels. Additionally, the dashboard generates price information based on the corrected age model and the risk model. The dashboard also notifies a user about service interventions for home systems.

Abstract: A safe transit route generation system determines a travel route for a user traveling from an origin location to a destination location and generates a route safety value for the travel route based on one or more safety conditions associated with at least one geographic location of the travel route. The system also determines a risk aversion value for the user based on a plurality of safety preferences associated with a user risk profile and that the risk aversion value for the user exceeds the route safety value for the travel route. The system further modifies one or more portions of the travel route to increase the route safety value above the risk aversion value.

Abstract: A smart scheduling system determines a baseline set of user preferences based on at least one prior arrival time relative to a prior event time. The system also receives new event data including a new event time and a new event location, and further, determines a predicted travel time from an origin location to the new event location based on one or more historic environment conditions and the baseline set of user preferences. Based on the predicted travel time, the system generates a notification having an initial notification time. The system also monitors real-time environment conditions corresponding to at least one travel route from the origin location to the new event location, and determines a revised notification time based on the real-time environment conditions. The system provides the notification to one or more client devices associated with a user based on the revised notification time.

Abstract: In one embodiment, a stress monitoring and intervention system receives interaction data from a client device, where the interaction data corresponds to an interaction between a user and one or more client devices. The system also determines the interaction data corresponds to an interaction metric associated with a health profile for the user, and generates a stress value for the interaction metric based on the interaction data. The system further determines the stress value exceeds a stress threshold associated with the interaction metric, selects at least one health intervention, and executes the at least one health intervention to change a user behavior.

Abstract: A network of sensors is utilized to capture and relay data that is relevant to specific types of insurance coverage. Sensors included in the network can be deployed as specialized devices, or can be found in existing consumer products. Behavioral and environmental data collected from the sensor network is used to establish a feedback relationship between an insurance policy holder's behavior and insurance policy pricing. Various types of insurance products can benefit from behavioral and environmental data, including health insurance, dental insurance, disability insurance and automobile insurance. Collected data can be used for other purposes, such as initial risk assessment, risk monitoring, and prevention services.

Abstract: A trust platform receives transaction review values from a plurality of marketplace providers, each transaction review value associated with a person-to-person transaction. The platform weighs each transaction review value to generate a weighted transaction review value based on a characteristic of the transaction. A baseline individual trust score is generated based on an aggregation of the weighted transaction review values which reflects a “trust” attributable to a user. The trust platform is also configured to adjust new transaction review values based on the baseline individual trust score to render such review more accurate.

Abstract: An insurance server accesses communications from at least one social media platform. The accessed communications include at least one of text data or image data. The insurance server determines a portion of the communications corresponds to a category of activity. For this category of activity, the insurance server determines a level of risk based on a potential for injury. Based on the level of risk for the category of activity, the insurance server identifies at least one insurance product. The insurance server further identifies a set of users associated with the portion of the communications and insures this set of users for the category of activity based on the at least one insurance product.

Abstract: A network of sensors is utilized to capture and relay data that is relevant to specific types of insurance coverage. Sensors included in the network can be deployed as specialized devices, or can be found in existing consumer products. Behavioral and environmental data collected from the sensor network is used to establish a feedback relationship between an insurance policy holder's behavior and insurance policy pricing. Various types of insurance products can benefit from behavioral and environmental data, including health insurance, dental insurance, disability insurance and automobile insurance. Collected data can be used for other purposes, such as initial risk assessment, risk monitoring, and prevention services.

Abstract: A network of sensors is utilized to capture and relay data that is relevant to specific types of insurance coverage. Sensors included in the network can be deployed as specialized devices, or can be found in existing consumer products. Behavioral and environmental data collected from the sensor network is used to establish a feedback relationship between an insurance policy holder's behavior and insurance policy pricing. Various types of insurance products can benefit from behavioral and environmental data, including health insurance, dental insurance, disability insurance and automobile insurance. Collected data can be used for other purposes, such as initial risk assessment, risk monitoring, and prevention services.

Abstract: A network of sensors is utilized to capture and relay data that is relevant to specific types of insurance coverage. Sensors included in the network can be deployed as specialized devices, or can be found in existing consumer products. Behavioral and environmental data collected from the sensor network is used to establish a feedback relationship between an insurance policy holder's behavior and insurance policy pricing. Various types of insurance products can benefit from behavioral and environmental data, including health insurance, dental insurance, disability insurance and automobile insurance. Collected data can be used for other purposes, such as initial risk assessment, risk monitoring, and prevention services.

Abstract: Disclosed herein is a system and a computer implemented method for automatically adjusting a customer's insurance product or insurance portfolio upon a triggering event according to predefined guidelines. For example, a customer's vehicle purchase would cause the system to adjust the customer's auto insurance policy based upon predefined guidelines. Another aspect of the disclosure relates to applying a heuristic approach to create and implement the predefined guidelines.

Abstract: The present application discloses systems and methods for providing group insurance. An exemplary insurance system identifies or manages data relating to group membership based on a web portal associated with the membership. The insurance system may develop one or more insurance products based on risks that are shared amongst members.

Abstract: Disclosed is a system and method for protecting a user's virtual assets from various risks by providing risk-management products for virtual losses. Risks include situations resulting in a loss of virtual assets, for example but not limited to: account theft or hacking, fraud, breach of virtual contracts, and various in game events (such as a virtual hurricane destroying a virtual home).