Abstract: Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Abstract: Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Abstract: Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group/demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group/demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group/demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Abstract: Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group/demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.

Abstract: Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Abstract: Systems and methods are described herein for calculating trust score based on crowdsourced information. The trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of an entity in a particular community or in relation to another entity. The trust score may be calculated based on data received from a variety of public and private data sources, including “crowdsourced” information. For example, users may provide and/or comment on attributes, characteristics, features, or any other information about another user. These inputs may serve to both validate the available data as well as provide additional information about the user that may not be otherwise available. The participation of the “crowd” may form a type of validation in itself and give comfort to second-order users, who know that the crowd can spectate and make contributions to the attributes, characteristics, features, and other information.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.