Systems and methods for identifying entities directly from imagery
First Claim
1. A computer-implemented method comprising:
- identifying, by one or more computing devices and from a plurality of images, one or more images that depict an entity;
determining, by the one or more computing devices, location information associated with the one or more images that depict the entity;
identifying, by the one or more computing devices and based at least in part on the location information associated with the one or more images that depict the entity, one or more candidate entity profiles from an entity directory;
providing, by the one or more computing devices, the one or more images that depict the entity and the one or more candidate entity profiles as input to a machine learning model comprising a neural network and at least one recurrent neural network, the neural network comprising a deep convolutional neural network (CNN), the at least one recurrent neural network comprising a long short-term memory network (LSTM), the CNN being configured to receive data indicative of the one or more images that depict the entity, extract features from the one or more images that depict the entity, and provide data indicative of the extracted features to the LSTM, the LSTM being configured to receive data indicative of the one or more candidate entity profiles, obtain text-related information from the one or more candidate entity profiles, and model at least a portion of structured information from a candidate entity profile as a sequence of characters, such that a match score between the extracted features and data from the candidate entity profile can be determined;
generating, by the one or more computing devices, one or more outputs of the machine learning model, each output comprising a match score associated with at least one candidate entity profile and an image that depicts the entity; and
updating, by the one or more computing devices, the entity directory based at least in part on the one or more generated outputs of the machine learning model.
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Accused Products
Abstract
Systems and methods of identifying entities are disclosed. In particular, one or more images that depict an entity can be identified from a plurality of images. One or more candidate entity profiles can be determined from an entity directory based at least in part on the one or more images that depict the entity. The one or more images that depict the entity and the one or more candidate entity profiles can be provided as input to a machine learning model. One or more outputs of the machine learning model can be generated. Each output can include a match score associated with an image that depicts the entity and at least one candidate entity profile. The entity directory can be updated based at least in part on the one or more generated outputs of the machine learning model.
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Citations
19 Claims
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1. A computer-implemented method comprising:
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identifying, by one or more computing devices and from a plurality of images, one or more images that depict an entity; determining, by the one or more computing devices, location information associated with the one or more images that depict the entity; identifying, by the one or more computing devices and based at least in part on the location information associated with the one or more images that depict the entity, one or more candidate entity profiles from an entity directory; providing, by the one or more computing devices, the one or more images that depict the entity and the one or more candidate entity profiles as input to a machine learning model comprising a neural network and at least one recurrent neural network, the neural network comprising a deep convolutional neural network (CNN), the at least one recurrent neural network comprising a long short-term memory network (LSTM), the CNN being configured to receive data indicative of the one or more images that depict the entity, extract features from the one or more images that depict the entity, and provide data indicative of the extracted features to the LSTM, the LSTM being configured to receive data indicative of the one or more candidate entity profiles, obtain text-related information from the one or more candidate entity profiles, and model at least a portion of structured information from a candidate entity profile as a sequence of characters, such that a match score between the extracted features and data from the candidate entity profile can be determined; generating, by the one or more computing devices, one or more outputs of the machine learning model, each output comprising a match score associated with at least one candidate entity profile and an image that depicts the entity; and updating, by the one or more computing devices, the entity directory based at least in part on the one or more generated outputs of the machine learning model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A computing system, comprising:
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one or more processors; and one or more memory devices storing computer-readable instructions that when executed by the one or more processors cause the computing system to perform operations comprising; identifying, from a plurality of images, one or more images that depict an entity; determining location information associated with the one or more images that depict the entity; identifying, based at least in part on the location information associated with the one or more images that depict the entity, one or more candidate entity profiles from an entity directory; providing the one or more images that depict the entity and the one or more candidate entity profiles as input to a machine learning model comprising a neural network and at least one recurrent neural network, the neural network comprising a deep convolutional neural network (CNN), the at least one recurrent neural network comprising a long short-term memory network (LSTM), the CNN being configured to receive data indicative of the one or more images that depict the entity, extract features from the one or more images that depict the entity, and provide data indicative of the extracted features to the LSTM, the LSTM being configured to receive data indicative of the one or more candidate entity profiles, obtain text-related information from the one or more candidate entity profiles, and model at least a portion of structured information from a candidate entity profile as a sequence of characters, such that a match score between the extracted features and data from the candidate entity profile can be determined; generating one or more outputs of the machine learning model, each output comprising a match score associated with at least one candidate entity profile and an image that depicts the entity; and updating the entity directory based at least in part on the one or more generated outputs of the machine learning model. - View Dependent Claims (15, 17)
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16. One or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations comprising:
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identifying, from a plurality of images, one or more images that depict an entity; determining location information associated with the one or more images that depict the entity; identifying, based at least in part on the location information associated with the one or more images that depict the entity, one or more candidate entity profiles from an entity directory; providing the one or more images that depict the entity and the one or more candidate entity profiles as input to a machine learning model comprising a neural network and at least one recurrent neural network, the neural network comprising a deep convolutional neural network (CNN), the at least one recurrent neural network comprising a long short-term memory network (LSTM), the CNN being configured to receive data indicative of the one or more images that depict the entity, extract features from the one or more images that depict the entity, and provide data indicative of the extracted features to the LSTM, the LSTM being configured to receive data indicative of the one or more candidate entity profiles, obtain text-related information from the one or more candidate entity profiles, and model at least a portion of structured information from a candidate entity profile as a sequence of characters, such that a match score between the extracted features and data from the candidate entity profile can be determined; generating one or more outputs of the machine learning model, each output comprising a match score associated with at least one candidate entity profile and an image that depicts the entity; and updating the entity directory based at least in part on the one or more generated outputs of the machine learning model. - View Dependent Claims (18, 19)
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Specification