Abstract: Training and using a machine learning model for data extraction is provided. The method comprises receiving keys of interest received from a user through an interface and receiving a batch of documents containing unstructured text. Unstructured text of a first document is processed to extract structured text. The model predicts text classifications of the structured text according to the keys of interest. The predicted text classifications are output to the user through the interface. Annotations to correct any incorrect predictions are received from the user, and the model is retrained according to the annotations. The above steps are repeated for less than ten additional documents from the batch until the model has been trained to predict text classifications with a specified level of accuracy. The trained model then classifies extracted structured text in the remaining documents in the batch.

Abstract: A method of information retrieval is provided. The method comprises receiving a query from a user and parsing the query in real-time as the user enters the query. The parsed query is interpreted dynamically based on a defined schema of a knowledge base, and a number of query interpretations is displayed in real-time as the user enters the query. When a selection of one of the query interpretations is received from the user information is retrieved from the knowledge base according to the selected query interpretation.

Abstract: An embodiment of the present invention discloses an object tracking system for tracking objects across a first frame and a second frame of a video. The object tracking system comprises of a processor, a predictor, a non-transitory storage element coupled to the processor and encoded instructions stored in the non-transitory storage element. The encoded instructions when implemented by the processor, configure the object tracking system to detect a reference object in a received first video and/or image frame (v/i) based on a pre-defined feature-based characteristic via the object tracking unit.

Abstract: The primary purpose of the present invention is to enable devices/machines/systems to perform an optimized video analytics on images and videos. The present invention focuses on detecting, tracking, and classifying objects in a scene. Here, the tracking is performed by comparing objects across at least two frames and then associating the objects based on a cost matrix. Some examples of the objects include, but are not limited to, persons, animals, vehicles, or any other articles or items.

Abstract: A system and method for detecting human presence in or absence from a field-of-view of a camera by analyzing camera data using a processor inside of or adjacent to the camera itself. In an example, the camera can be integrated with or embedded in another edge-based sensor device. In an example, a video signal processing system receives image data from one or more image sensors and uses a local processing circuit to process the image data and determine if a human being is or is not present during a particular time, interval, or sequence of frames. In an example, the human being identification technique can be used in security or surveillance applications such as for home, business, or other monitoring cameras.

Abstract: An embodiment of the present invention discloses an object tracking system for tracking objects across a first frame and a second frame of a video. The object tracking system comprises of a processor, a non-transitory storage element coupled to the processor and encoded instructions stored in the non-transitory storage element. The encoded instructions when implemented by the processor, configure the object tracking system to detect one or more objects in the first frame of the video, and one or more candidate objects in the second frame of the video.

Abstract: A method of information retrieval is provided. The method comprises receiving a query from a user and parsing the query in real-time as the user enters the query. The parsed query is interpreted dynamically based on a defined schema of a knowledge base, and a number of query interpretations is displayed in real-time as the user enters the query. When a selection of one of the query interpretations is received from the user information is retrieved from the knowledge base according to the selected query interpretation.

Abstract: The primary purpose of the present invention is to enable devices/machines/systems to perform an optimized video analytics on images and videos. The present invention focuses on detecting, tracking, and classifying objects in a scene. Here, the tracking is performed by comparing objects across at least two frames and then associating the objects based on a cost matrix. Some examples of the objects include, but are not limited to, persons, animals, vehicles, or any other articles or items.

Abstract: The present invention discloses methods, systems, and computer programmable products for tracking objects across a first frame and a second frame of a video. An object tracking system computes a cost function between each object in the first frame and each object in the second frame. Further, a pair of objects is selected from the first frame and the second frame based on a pre-defined criteria which is based on the computed cost function. Thereafter, the object tracking system established a correspondence between the selected pair of objects.

Abstract: A system and method for detecting human presence in or absence from a field-of-view of a camera by analyzing camera data using a processor inside of or adjacent to the camera itself. In an example, the camera can be integrated with or embedded in another edge-based sensor device. In an example, a video signal processing system receives image data from one or more image sensors and uses a local processing circuit to process the image data and determine if a human being is or is not present during a particular time, interval, or sequence of frames. In an example, the human being identification technique can be used in security or surveillance applications such as for home, business, or other monitoring cameras.

Abstract: A system determines a set of users satisfying a given criteria based on attribute values of the users. The system associates each value of an attribute with a representation of users having that attribute value. The representation of users may be an enumeration of users or a bit index. The system receives a request for determining a set of users satisfying a given criterion. The system identifies a logical operation based on the criterion and performs the logical operation to determine the set of users. The representation of the set of users may be distributed across a plurality of processors, allowing the system to perform the logical operation in parallel. The system may further determine statistical information describing a characteristic of the set of users, for example, page likes by the set of users. The statistical information may be presented to a business for analyzing their target audience.

Abstract: A system determines a set of users satisfying a given criteria based on attribute values of the users. The system associates each value of an attribute with a representation of users having that attribute value. The representation of users may be an enumeration of users or a bit index. The system receives a request for determining a set of users satisfying a given criterion. The system identifies a logical operation based on the criterion and performs the logical operation to determine the set of users. The representation of the set of users may be distributed across a plurality of processors, allowing the system to perform the logical operation in parallel. The system may further determine statistical information describing a characteristic of the set of users, for example, page likes by the set of users. The statistical information may be presented to a business for analyzing their target audience.

Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.

Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.