Abstract: Methods and apparatuses are described for automated prediction of computing resource performance scaling using reinforcement learning. A server executes performance tests against a production computing environment comprising a plurality of computing layers to capture performance data for computing resources in the production environment, where the performance tests are configured according to transactions-per-second (TPS) values. The server trains a classification model using the performance data, the trained model configured to predict computing power required by the plurality of computing layers. The server identifies a target TPS value and a target cost tolerance for the production environment and executes the trained classification model using the target TPS value and the target cost tolerance as input to generate a prediction of computing power required by the plurality of computing layers.

Abstract: Systems and methods for generating notifications based on extracted data from documents using a machine learning algorithm. The method includes receiving a document uploaded by a user including data corresponding to the user. The method further includes receiving an indication from the user granting permission to extract the data from the document. The method also includes extracting the data from the document using optical character recognition. The method further includes determining a document type based on the extracted data and document format data using a machine learning algorithm. The method also includes verifying the extracted data based on the determined document type and user data. The method also includes identifying a triggering event corresponding to the document based on the extracted data and the user data. The method further includes generating a notification corresponding to the triggering event.

Abstract: Systems and methods are described for automating retrieval of application data prior to wireless network outages and rendering mobile device UIs during the outages. The system includes a server and database of data object files corresponding to features of applications executable on the mobile device. The mobile device receives a notification from the server before a predicted network outage, and sends back a request including parameters indicating resource status. The mobile device receives a response including a data object file from the database and automatically invokes a network bot, UI bot, and navigation bot. The network bot retrieves application data from a web server based on an object from the data object file. The navigation bot generates a navigation handle based on an object from the data object file, and the UI bot renders a UI based upon a UI object from the data object file and the navigation handle.

Abstract: Systems and methods for generating notifications based on extracted data from documents using a machine learning algorithm. The method includes receiving a document uploaded by a user including data corresponding to the user. The method further includes receiving an indication from the user granting permission to extract the data from the document. The method also includes extracting the data from the document using optical character recognition. The method further includes determining a document type based on the extracted data and document format data using a machine learning algorithm. The method also includes verifying the extracted data based on the determined document type and user data. The method also includes identifying a triggering event corresponding to the document based on the extracted data and the user data. The method further includes generating a notification corresponding to the triggering event.

Abstract: Systems and methods are described for automating retrieval of application data prior to wireless network outages and rendering mobile device UIs during the outages. The system includes a server and database of data object files corresponding to features of applications executable on the mobile device. The mobile device receives a notification from the server before a predicted network outage, and sends back a request including parameters indicating resource status. The mobile device receives a response including a data object file from the database and automatically invokes a network bot, UI bot, and navigation bot. The network bot retrieves application data from a web server based on an object from the data object file. The navigation bot generates a navigation handle based on an object from the data object file, and the UI bot renders a UI based upon a UI object from the data object file and the navigation handle.

Abstract: Methods and apparatuses are described for generating and displaying custom-selected content for a mobile application. A mobile device captures a request to launch an application and determines content cards eligible to be displayed. A user-specific content classification model is generated for selecting one of the content cards to be displayed, where the mobile device identifies historical usage characteristics and transmits the historical usage characteristics to a server, which generates the user-specific content classification model based upon the historical usage characteristics and transmits the user-specific content classification model to the mobile device. The mobile device executes the user-specific content classification model to select one or more content cards to be displayed and launches the application to display the selected content cards.

Abstract: A computer-implemented method is provided for automatically masking data for display in a mobile computing device. The computer-implemented method includes receiving a request to display data on the mobile computing device and detecting a physical location of the mobile computing device, a time corresponding to the request, and at least one network characteristic of a wireless network on which the mobile computing device is making the request. The method also includes automatically determining whether to mask the data for display in the mobile computing device based on the physical location, the time, and the at least one network characteristic. The method further includes responsive to determining to mask the data, applying one or more masking rules to the data.

Abstract: Methods and apparatuses are described for automated execution of computer software based upon determined empathy of a communication participant. A server captures a digitized voice segment from a remote computing device. The server analyzes vocal cues of the digitized voice segment to generate a voice empathy score. The server converts speech in the digitized voice segment into text and determines empathy keywords in the text to generate a keyword empathy score. The server captures digitized images of the participant's face and analyzes physical expressions of the face to identify emotions and generate a facial empathy score. The server generates an overall empathy confidence score for the communication participant based upon the voice empathy score, the keyword empathy score, and the facial empathy score. The server generates recommended changes for the user based upon the overall empathy confidence score and executes a software application that displays the recommended changes.

Abstract: Methods and apparatuses are described for automated execution of computer software based upon determined empathy of a communication participant. A server captures a digitized voice segment from a remote computing device. The server analyzes vocal cues of the digitized voice segment to generate a voice empathy score. The server converts speech in the digitized voice segment into text and determines empathy keywords in the text to generate a keyword empathy score. The server captures digitized images of the participant's face and analyzes physical expressions of the face to identify emotions and generate a facial empathy score. The server generates an overall empathy confidence score for the communication participant based upon the voice empathy score, the keyword empathy score, and the facial empathy score. The server generates recommended changes for the user based upon the overall empathy confidence score and executes a software application that displays the recommended changes.

Abstract: A method of testing source code for a computer application includes: monitoring a source code repository to detect when a changed version of one or more source code files is saved to the repository; identifying, for a source code file that has changed, one or more changed features of the file, by comparing a changed version of the file with a previous version of the file; generating, based on the one or more changed features, a source code artifact for use in testing the file; generating, based on the source code artifact, a test case framework artifact for use in testing the file; executing one or more test cases based on the one or more changed features, the source code artifact, and the test case framework artifact; and compiling a report including details of the executed test cases.

Abstract: A computer-implemented method is provided for automatically masking data for display in a mobile computing device. The computer-implemented method includes receiving a request to display data on the mobile computing device and detecting a physical location of the mobile computing device, a time corresponding to the request, and at least one network characteristic of a wireless network on which the mobile computing device is making the request. The method also includes automatically determining whether to mask the data for display in the mobile computing device based on the physical location, the time, and the at least one network characteristic. The method further includes responsive to determining to mask the data, applying one or more masking rules to the data.

Abstract: An optical image of a source document is captured. Two or more source document image test regions are then defined/determined. An optical image scan is performed on each source document image test region to determine if there are identifiable alpha-numeric characters or symbols present. If one or more of the source document image test regions are determined not to contain identifiable alpha-numeric characters, the captured optical image of the source document is determined to be of insufficient quality to identify and extract individual characters and symbols and it is recommended that optical images of source documents determined to be of insufficient quality to identify and extract individual characters and symbols be re-captured using an image capture device.

Abstract: An optical image of a source document is captured. Two or more source document image test regions are then defined/determined. An optical image scan is performed on each source document image test region to determine if there are identifiable alpha-numeric characters or symbols present. If one or more of the source document image test regions are determined not to contain identifiable alpha-numeric characters, the captured optical image of the source document is determined to be of insufficient quality to identify and extract individual characters and symbols and it is recommended that optical images of source documents determined to be of insufficient quality to identify and extract individual characters and symbols be re-captured using an image capture device.

Abstract: Data extraction templates are created and associated with source documents from a specific source document source. One or more known pictorial representations associated with one or more source document sources are then identified and key data is generated for the known pictorial representations. Source document data is then obtained and analyzed to identify potential pictorial representation data. Key data associated with the potential pictorial representation data is then generated and compared with the key data associated with one or more known pictorial representations and if the key data matches, the data extraction template associated with the matched known pictorial representations is obtained and used for identifying and extracting data from the source document data.

Abstract: A viewfinder screen display is generated and positioned such that a source document is displayed in the viewfinder screen display. Source document image blocks corresponding to different portions of the source document are then defined. For each source document image block, the image capture parameter of an image capture device is set to an optimized image capture parameter setting for the source document image block. The image capture device then captures an image block optimized image of the source document optimized for the source document image block. The optimized source document image blocks are then extracted from each image block optimized image of the source document. The extracted optimized source document image blocks are then aggregated and used to construct an image capture parameter optimized image of the source document.

Abstract: A viewfinder screen display is generated and positioned such that a source document is displayed in the viewfinder screen display. Source document image blocks corresponding to different portions of the source document are then defined. For each source document image block, the image capture parameter of an image capture device is set to an optimized image capture parameter setting for the source document image block. The image capture device then captures an image block optimized image of the source document optimized for the source document image block. The optimized source document image blocks are then extracted from each image block optimized image of the source document. The extracted optimized source document image blocks are then aggregated and used to construct an image capture parameter optimized image of the source document.

Abstract: A method to use a single touch-based device for a set of users involves analyzing a biometric signal of a user, obtained using a biometric sensor of the single touch-based device, to generate a biometric data item; determining an identity of the user by comparing the biometric data item to a set of biometric data items stored in the single touch-based device; activating, in response solely to the biometric signal and based on the identity of the user, a user data set residing on the single touch-based device, where the user data set belongs to the user; and performing, in response to a touch input from the user and activation of the user data set, a task on the single touch-based device using the user data set.