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: 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: 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: 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: 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 unmanned insurance drone can include a drone body and a sensor unit disposed on the drone body to collect sensor data. An on-board data processor converts the sensor data into insurance related information, and a wireless communication unit in communication with the data processor to transmit the insurance related information. In another example, the data processor may not be on the drone but remotely located. The location can be with the pilot or a control collection location. If the insurance related information is separate from the drone, than the wireless communication unit can transmit the raw sensor data to the processor.

Abstract: An unmanned insurance drone can include a drone body and a sensor unit disposed on the drone body to collect sensor data. An on-board data processor converts the sensor data into insurance related information, and a wireless communication unit in communication with the data processor to transmit the insurance related information. In another example, the data processor may not be on the drone but remotely located. The location can be with the pilot or a control collection location. If the insurance related information is separate from the drone, than the wireless communication unit can transmit the raw sensor data to the processor.