Abstract: Systems and methods for assisting a user during a tax interview are disclosed. In some embodiments, the system may utilize historical data and machine learning algorithms to determine an optimal set of questions to the user. The questions may be order based on a desired result of the optimization algorithm. The user may respond to the questions and at each response, a new set of questions may be generated based on a real-time simulation of the potential questions. As the user continues to provide input, an error based on unknown information may reduce to a minimal error below a threshold. At this point, the interview may be complete, and the desired result may be maximized.

Abstract: Media, methods, and systems are disclosed for automatically calculating predicted values using deep learning models. One or more input forms having a plurality of input form field values are received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a data frame having a set of normalized field values. In response to determining which portions of the data frame to apply to a line calculation neural network, a corresponding line calculation neural network is applied. At least one output form line calculation is performed. A natural language explanation regarding the at least one output form line calculation is generated. Additional user provided inputs are received in response to the natural language explanation, and at least one remaining calculation is carried out based on the one or more additional user provided inputs.

Abstract: Media, methods, and systems are disclosed for automatically calculating predicted values using deep learning models. One or more input forms having a plurality of input form field values are received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a data frame having a set of normalized field values. In response to determining which portions of the data frame to apply to a line calculation neural network, a corresponding line calculation neural network is applied. At least one output form line calculation is performed. A natural language explanation regarding the at least one output form line calculation is generated. Additional user provided inputs are received in response to the natural language explanation, and at least one remaining calculation is carried out based on the one or more additional user provided inputs.

Abstract: Media, methods, and systems are disclosed for applying a computer-implemented model to a table of computed values to identify one or more anomalies. One or more input forms having a plurality of input form field values is received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a corresponding set of normalized field values, based on a computer-automated input normalization model. An automated review model controller is applied to automatically identify a review model to apply to the set of normalized field values, based on certain predetermined target field values. The automatically identified review model is then applied to the set of normalized inputs, and in response to detecting an anomaly, a field value is flagged accordingly.

Abstract: Media, methods, and systems are disclosed for automatically calculating predicted values using deep learning models. One or more input forms having a plurality of input form field values are received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a data frame having a set of normalized field values. In response to determining which portions of the data frame to apply to a line calculation neural network, a corresponding line calculation neural network is applied. At least one output form line calculation is performed. A natural language explanation regarding the at least one output form line calculation is generated. Additional user provided inputs are received in response to the natural language explanation, and at least one remaining calculation is carried out based on the one or more additional user provided inputs.

Abstract: Media, methods, and systems are disclosed for automatically calculating predicted values using deep learning models. One or more input forms having a plurality of input form field values are received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a data frame having a set of normalized field values. In response to determining which portions of the data frame to apply to a line calculation neural network, a corresponding line calculation neural network is applied. At least one output form line calculation is performed. A natural language explanation regarding the at least one output form line calculation is generated. Additional user provided inputs are received in response to the natural language explanation, and at least one remaining calculation is carried out based on the one or more additional user provided inputs.

Abstract: Media, methods, and systems are disclosed for applying a computer-implemented model to a table of computed values to identify one or more anomalies. One or more input forms having a plurality of input form field values is received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a corresponding set of normalized field values, based on a computer-automated input normalization model. An automated review model controller is applied to automatically identify a review model to apply to the set of normalized field values, based on certain predetermined target field values. The automatically identified review model is then applied to the set of normalized inputs, and in response to detecting an anomaly, a field value is flagged accordingly.

Abstract: Media, methods, and systems are disclosed for automatically calculating predicted values using deep learning models. One or more input forms having a plurality of input form field values are received. The input form field values are automatically parsed into a set of computer-generated candidate standard field values. The set of candidate standard field values are automatically normalized into a data frame having a set of normalized field values. In response to determining which portions of the data frame to apply to a line calculation neural network, a corresponding line calculation neural network is applied. At least one output form line calculation is performed. A natural language explanation regarding the at least one output form line calculation is generated. Additional user provided inputs are received in response to the natural language explanation, and at least one remaining calculation is carried out based on the one or more additional user provided inputs.

Abstract: A wireless communication system comprises a wireless communication device and an application control system. The wireless communication device is configured to receive an application transmitted from an application server over a communication network, present application management tiers comprising individual settings for management items of access, bandwidth, and security for the application, receive a selection of one of the application management tiers for the application from the user, and transfer the selected application management tier for delivery to the application control system.

Abstract: A wireless communication device communicates with a data network in response to data access operations generated by a plurality of applications operating within the wireless communication device. The wireless communication device displays a plurality of framed icons based on icons individually associated with each of the applications and monitors the applications to determine a data throughput for the data access operations on a per-application basis. The wireless communication device processes the data throughput to select frame colors on a per-application basis, and directs the wireless communication device to display the framed icons with the selected frame colors.

Abstract: A wireless communication device communicates with a data network in response to data access operations generated by a plurality of applications operating within the wireless communication device. The wireless communication device displays a plurality of framed icons based on icons individually associated with each of the applications and monitors the applications to determine a frequency and data throughput for the data access operations on a per-application basis. The wireless communication device processes the frequency to select numeric indicators on a per-application basis, processes the data throughput to select frame colors on a per-application basis, and directs the wireless communication device to display the framed icons with the selected frame colors and with bubbles having the numeric indicators.

Abstract: A wireless communication device is disclosed for scanning a preferred roaming list (PRL) in a non-roaming mode. The wireless communication device exchanges wireless communications with a first wireless communication network and monitors communication performance of the first wireless communication network while operating in a non-roaming mode. The wireless communication device processes the PRL to perform scans for a pilot signal from a second wireless communication network while operating in the non-roaming mode. The wireless communication device subsequently performs a time correlation of the communication performance of the first wireless communication network and the scans for the pilot signal of the second wireless communication network to determine that a roaming mode should be entered. The roaming mode is then entered and the wireless communication device exchanges wireless communications with the second wireless communication network while operating in the roaming mode.

Abstract: A computer implemented system for project prediction is provided. The system includes a data manager to obtain historical project data. The system also includes an analyzer to analyze the historical project data and an analysis cycle time to generate models for a proposed project cycle time. Additionally, the system includes a user interface to select one model for the proposed project cycle time, wherein the selected model includes linear sub-models corresponding to a historical data range, and apply proposed project data and analysis cycle time to one linear sub-model corresponding to a proposed data range to predict the proposed project cycle time. Furthermore, the system captures proposed project data and obtains additional project data to update the selected model. The models provide for the accurate prediction of cycle times, or project costs, in an enterprise development environment.

Abstract: A computer implemented system for project prediction is provided. The system includes a data manager to obtain historical project data. The system also includes an analyzer to analyze the historical project data to generate models for a proposed project cycle time. Additionally, the system includes a user interface to select one model for the proposed project cycle time, wherein the selected model includes linear sub-models corresponding to a historical data range, and apply proposed project data to one linear sub-model corresponding to a proposed data range to predict the proposed project cycle time. Furthermore, the system captures proposed project data and obtains additional project data to update the selected model. The models provide for the accurate prediction of cycle times, or project costs, in an enterprise development environment.