Sprint Communications Comapny L.p.

Hrb Innovations, Inc.

Jason Norwood Ward

Tuan Quoc Tran

Raymond Emilio Reeves

Benjamin A Kite

Zhi Zheng

Deandra T Cassone

Kimberly Rae Raddatz

Paul R Sapenaro

George R Kather

Joseph E Dudley

Cesar Perez

Benjamin P Blinn

Louis Eric Wingo

Lauriann Lowe Hebb

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.

Automated calculation predictions with explanations

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.

Automated return evaluation with anomoly detection

Systems, methods, and computer-readable media for optimizing a user experience are provided. The method includes optimizing a user experience using clusters, user preferences, or a combination thereof. Clusters may be created based on, for example, user behaviors, or actions, exhibited by a user. User preferences may be established for each cluster in order to further customize the clusters. The clusters may be continuously monitored such that if changes are necessary they may be immediately applied such as a user exhibited different behavior and requiring association with a new cluster. This information, or clustering, may be utilized to predict user satisfaction such that more positive user experiences are encountered and negative user experiences are, to the extent possible, avoided, or at least lessened.

Optimizing a user experience

A wireless communication device receives a user request to download a software application and wirelessly receives the software application. The wireless communication device presents multiple application management tiers for the software application. The wireless communication device receives a user selection of a first application management tier for the software application. The wireless communication wirelessly transfers the user selection of the first application management tier for the software application and wirelessly receives a network selection of a second application management tier for the software application from the communication network. The wireless communication presents the second application management tier for the software application.

Application management for a wireless communication device

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. The application control system is configured to process the selected application management tier with a predetermined management tier for the application to identify a discrepancy between a setting of one of the management items in the selected application management tier and a predetermined setting for a corresponding item in the predetermined management tier, and transfer feedback for delivery to the wireless communication device that identifies the discrepancy and suggests the predetermined management tier for the application.

Methods, systems, and computer-readable media are provided for assessing call integrity of calls communicated via a mobile device. In particular, physical aspects of the mobile device that are related to call integrity are identified. The physical condition of the mobile device is then monitored based on the identified physical aspects. The data retrieved from monitoring the physical aspects of the mobile device is then provided to a physical condition assessment component that determines an assessed physical condition of the mobile device. Based on the assessed physical condition of the mobile device, call integrity of calls communicated via the mobile device is determined.

Assessing call integrity of calls communicated via a mobile device

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.

Providing notifications of wireless data consumption in a wireless communication device

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.

Performance scanning and correlation in wireless communication devices

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.

Enhanced project predictor

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.

Growing community of inventors

Lee's Summit, MO, United States of America

Jason Norwood Ward