Abstract: Certain aspects of the present disclosure provide techniques for providing sentiment analysis of content. In order to determine the overall sentiment of content, a request is received by a sentiment analyzer, which then identifies a content identification number and retrieves comments associated with the content identification number. The sentiment analyzer pre-processes the comments, which includes removing all personal identifying information from the comments. The sentiment analyzer sends the pre-processed comments to a natural language processing service, and in turn, receives sentiment indications corresponding to the comments provided. Based on the sentiment scores, the sentiment analyzer generates a sentiment analysis and displays the sentiment analysis in the graphical user interface generated by the sentiment analyzer.

Abstract: Certain aspects of the present disclosure provide techniques for training and using machine learning models to extract key-value sets from a document. An example method generally includes identifying regions of a document including key-value sets corresponding to inputs to a data processing application based on a first machine learning model and an electronic version of the document. One or more keys and one or more values are identified in the document based on a second machine learning model. One or more key-value sets are generated based on matching keys of the one or more keys and values of the one or more values in the region of the document. The one or more key-value sets are provided to a data processing application for processing.

Abstract: A server for recommending tasks for completion includes a memory including a database configured to store information associated with a plurality of users, and an application coupled to a processor. The server stores a set of chores associated with a plurality of existing users. Each chore is matched with an existing user characteristic. The server receives a first request by a first user for registering with the application. The first request includes a first user characteristic. The server recommends a first chore assignment for the first user based on the first user characteristic. The first chore assignment is based on a comparison of the first user characteristic against a plurality of existing user characteristics. The server further sends a signal to an electronic device for displaying the first chore assignment.

Abstract: Systems and methods for providing contextual information for computerized document understanding. The systems and methods can be used to assist users in filling out documents by providing contextual information based on anomalies identified in a provided document. The methods and systems may identify the deficiency in the document and automatically generate a query related to the anomaly. The query can be fed as an input to a question-answering (QA) model that can provide an answer as the contextual information.

Abstract: Certain aspects of the present disclosure provide techniques for providing sentiment analysis of content. In order to determine the overall sentiment of content, a request is received by a sentiment analyzer, which then identifies a content identification number and retrieves comments associated with the content identification number. The sentiment analyzer pre-processes the comments, which includes removing all personal identifying information from the comments. The sentiment analyzer sends the pre-processed comments to a natural language processing service, and in turn, receives sentiment indications corresponding to the comments provided. Based on the sentiment scores, the sentiment analyzer generates a sentiment analysis and displays the sentiment analysis in the graphical user interface generated by the sentiment analyzer.

Abstract: Certain aspects of the present disclosure provide techniques for providing sentiment analysis of content. In order to determine the overall sentiment of content, a request is received by a sentiment analyzer, which then identifies a content identification number and retrieves comments associated with the content identification number. The sentiment analyzer pre-processes the comments, which includes removing all personal identifying information from the comments. The sentiment analyzer sends the pre-processed comments to a natural language processing service, and in turn, receives sentiment indications corresponding to the comments provided. Based on the sentiment scores, the sentiment analyzer generates a sentiment analysis and displays the sentiment analysis in the graphical user interface generated by the sentiment analyzer.

Abstract: A wearable device, e.g., a smart watch, may be connected to a communication device, such as a mobile smart phone, via Bluetooth or other Near Field Communication (NFC). In particular, the wearable device may have a touch screen that may serve as an extended display and/or user input device of the communication device. For example, a user may receive payment related notifications on the wearable device. The user also may operate the wearable device to make and/or receive payments, view transaction history, view and/or accept offers from nearby merchants, view and/or check in to nearby stores and etc. Further, the wearable device also may allow the user to make payment transactions in extreme environments not suitable for the communication device, such as under water.

Abstract: A wearable device, e.g., a smart watch, may be connected to a communication device, such as a mobile smart phone, via Bluetooth or other Near Field Communication (NFC). In particular, the wearable device may have a touch screen that may serve as an extended display and/or user input device of the communication device. For example, a user may receive payment related notifications on the wearable device. The user also may operate the wearable device to make and/or receive payments, view transaction history, view and/or accept offers from nearby merchants, view and/or check in to nearby stores and etc. Further, the wearable device also may allow the user to make payment transactions in extreme environments not suitable for the communication device, such as under water.

Abstract: A wearable device, e.g., a smart watch, may be connected to a communication device, such as a mobile smart phone, via Bluetooth or other Near Field Communication (NFC). In particular, the wearable device may have a touch screen that may serve as an extended display and/or user input device of the communication device. For example, a user may receive payment related notifications on the wearable device. The user also may operate the wearable device to make and/or receive payments, view transaction history, view and/or accept offers from nearby merchants, view and/or check in to nearby stores and etc. Further, the wearable device also may allow the user to make payment transactions in extreme environments not suitable for the communication device, such as under water.