Abstract: A computerized method of generating a financial wellness score for retirement planning includes steps performed by a computing device including: receiving user information relating to demographic profile, financial health, physical health, psychosocial health, financial planning maturity and financial planning readiness of a user; categorizing the user into a career level classification based on demographic profile; identifying, based on the career level classification, one or more financial impact factors for the user; generating, based on the financial impact factors for the user and the user information relating to financial health, a future projected financial state; generating, based on the information relating to physical health and psychosocial health, a future projected health cost of the user; and calculating, based on the future projected financial state and the future projected health cost, a score indicating likelihood of achieving financial wellness in retirement.

Abstract: Methods and apparatuses are described for of recognizing handwritten characters in digital images using context-based machine learning. A server captures an image of a document that comprises one or more handwritten data fields, the document associated with a user identifier. The server identifies a field type for each handwritten data field in the image. The server creates a pixel intensity array for each character in each handwritten data field and determines whether a user-specific character map exists for the user identifier. If a map exists, the server retrieves the map and generates digital form data by executing a user-specific handwriting classifier using the map, the pixel intensity arrays, and the field types. If a map does not exist, the server builds a map based upon the pixel intensity arrays and generates digital form data by executing a baseline handwriting classifier using the map, the pixel intensity arrays, and the field types.

Abstract: Methods and apparatuses are described for monitoring usage and adjusting operational characteristics of physical assets. Asset sensors capture temperature data and x-y-z coordinate displacement data associated with a physical asset. A reference sensor captures temperature data of a defined physical area. For each asset sensor, a server computing device monitors the temperature data and the x-y-z coordinate displacement data. The server applies triggers to the temperature data and the displacement data to determine whether a change event occurred. The server records the temperature data and the displacement data when a change event occurred for the physical asset. The server generates instructions comprising a change to: an operational state or an availability of the physical asset. The server transmits the instructions to a remote device associated with the physical asset. The remote device executes the instructions to change: the operational state or the availability of the physical asset.

Abstract: Methods, systems and apparatuses, including computer program products, are described for detecting a proximity of a mobile computing device during a user session. The mobile computing device enters a first operational mode during the user session and obtains sensor data from at least one sensor responsive to a property of the location. The mobile computing device forwards to a server computing device, a baseline profile including the sensor data and a time stamp indicating a time the sensor data was obtained. The mobile computing device receives from the server computing device, a differential trigger threshold including a range of permissible values for sensor data obtained from the at least one sensor. The mobile computing device obtains updated sensor data from the at least one sensor. The mobile computing device enters a second operational mode when the updated sensor data is outside of the differential trigger threshold.

Abstract: Methods and apparatuses are described for of recognizing handwritten characters in digital images using context-based machine learning. A server captures an image of a document that comprises one or more handwritten data fields, the document associated with a user identifier. The server identifies a field type for each handwritten data field in the image. The server creates a pixel intensity array for each character in each handwritten data field and determines whether a user-specific character map exists for the user identifier. If a map exists, the server retrieves the map and generates digital form data by executing a user-specific handwriting classifier using the map, the pixel intensity arrays, and the field types. If a map does not exist, the server builds a map based upon the pixel intensity arrays and generates digital form data by executing a baseline handwriting classifier using the map, the pixel intensity arrays, and the field types.

Abstract: Methods and apparatuses are described for monitoring usage and adjusting operational characteristics of physical assets. Asset sensors capture temperature data and x-y-z coordinate displacement data associated with a physical asset. A reference sensor captures temperature data of a defined physical area. For each asset sensor, a server computing device monitors the temperature data and the x-y-z coordinate displacement data. The server applies triggers to the temperature data and the displacement data to determine whether a change event occurred. The server records the temperature data and the displacement data when a change event occurred for the physical asset. The server generates instructions comprising a change to: an operational state or an availability of the physical asset. The server transmits the instructions to a remote device associated with the physical asset. The remote device executes the instructions to change: the operational state or the availability of the physical asset.

Abstract: Methods, systems and apparatuses, including computer program products, are described for detecting movement of a mobile computing device based upon geospatial data about a property location of the mobile computing device. The mobile computing device generates a baseline geospatial profile including baseline altitude data, baseline pressure data, and a predetermined pressure range. The mobile computing device enters a first operational mode permitting a user to establish a user session on the mobile computing device. The mobile computing device obtains altitude data from a first sensor and pressure data from a second sensor, and generates a geospatial signature based upon the altitude data and the pressure data. The mobile computing device compares the geospatial signature and the baseline geospatial profile, and enters a second operational mode if a difference between one or more corresponding values of the baseline geospatial profile and the geospatial signature exceeds a predetermined threshold.

Abstract: Methods, systems and apparatuses, including computer program products, are described for detecting a proximity of a mobile computing device during a user session. The mobile computing device enters a first operational mode during the user session and obtains sensor data from at least one sensor responsive to a property of the location. The mobile computing device forwards to a server computing device, a baseline profile including the sensor data and a time stamp indicating a time the sensor data was obtained. The mobile computing device receives from the server computing device, a differential trigger threshold including a range of permissible values for sensor data obtained from the at least one sensor. The mobile computing device obtains updated sensor data from the at least one sensor. The mobile computing device enters a second operational mode when the updated sensor data is outside of the differential trigger threshold.

Abstract: Methods, systems and apparatuses, including computer program products, are described for determining a performance metric of a mobile computing device application. A test computing device captures a plurality of images displayed on a mobile computing device based on execution of a mobile computing device application. The test computing device determines a first property for first and second images of the plurality of images. A first performance parameter is set based on a difference between the first properties of the first and second images. The test computing device determines a first property for third and fourth images of the plurality of images. A second performance parameter is set based on a difference between the first properties of the third and fourth image. A performance metric is determined based on a difference between the first performance parameter and the second performance parameter.

Abstract: Methods, systems and apparatuses, including computer program products, are described for monitoring a presence of an authorized user of a mobile computing device during a user session based upon motion of the mobile computing device. The mobile computing device enters a first operational mode based upon a user session established by a user. The mobile computing device generates a first motion signature based upon first motion data obtained from a plurality of sensors during the user session, the first motion signature corresponding to the user. The mobile computing device generates a second motion signature based upon second motion data obtained from the plurality of sensors during the user session. The mobile computing device enters a second operational mode if a difference between the first motion signature and the updated motion signature exceeds a predetermined threshold.

Abstract: Methods, systems and apparatuses, including computer program products, are described for determining a performance metric of a mobile computing device application. A test computing device captures a plurality of images displayed on a mobile computing device based on execution of a mobile computing device application. The test computing device determines a first property for first and second images of the plurality of images. A first performance parameter is set based on a difference between the first properties of the first and second images. The test computing device determines a first property for third and fourth images of the plurality of images. A second performance parameter is set based on a difference between the first properties of the third and fourth image. A performance metric is determined based on a difference between the first performance parameter and the second performance parameter.

Abstract: Methods, systems and apparatuses, including computer program products, are described for monitoring a presence of an authorized user of a mobile computing device during a user session based upon motion of the mobile computing device. The mobile computing device enters a first operational mode based upon a user session established by a user. The mobile computing device generates a first motion signature based upon first motion data obtained from a plurality of sensors during the user session, the first motion signature corresponding to the user. The mobile computing device generates a second motion signature based upon second motion data obtained from the plurality of sensors during the user session. The mobile computing device enters a second operational mode if a difference between the first motion signature and the updated motion signature exceeds a predetermined threshold.

Abstract: Methods, systems and apparatuses, including computer program products, are described for detecting movement of a mobile computing device based upon geospatial data about a property location of the mobile computing device. The mobile computing device generates a baseline geospatial profile including baseline altitude data, baseline pressure data, and a predetermined pressure range. The mobile computing device enters a first operational mode permitting a user to establish a user session on the mobile computing device. The mobile computing device obtains altitude data from a first sensor and pressure data from a second sensor, and generates a geospatial signature based upon the altitude data and the pressure data. The mobile computing device compares the geospatial signature and the baseline geospatial profile, and enters a second operational mode if a difference between one or more corresponding values of the baseline geospatial profile and the geospatial signature exceeds a predetermined threshold.

Abstract: A computer-implemented method for securely receiving user data includes a computer associating a marker with a sensitive data field in a form. The method also includes the computer using the marker to facilitate secure entry of user data corresponding to the sensitive data field. The method further includes the computer entering the user data in the sensitive data field in the form. The marker is configured to refer to the sensitive data field without revealing additional information regarding the sensitive data field.

Abstract: Methods, systems and apparatuses, including computer program products, are described for determining a performance metric of a mobile computing device application. A test computing device captures a plurality of images displayed on a mobile computing device based on execution of a mobile computing device application. The test computing device determines a first property for first and second images of the plurality of images. A first performance parameter is set based on a difference between the first properties of the first and second images. The test computing device determines a first property for third and fourth images of the plurality of images. A second performance parameter is set based on a difference between the first properties of the third and fourth image. A performance metric is determined based on a difference between the first performance parameter and the second performance parameter.

Abstract: Methods, systems and apparatuses, including computer program products, are described for detecting movement of a mobile computing device based upon geospatial data about a property location of the mobile computing device. The mobile computing device generates a baseline geospatial profile including baseline altitude data, baseline pressure data, and a predetermined pressure range. The mobile computing device enters a first operational mode permitting a user to establish a user session on the mobile computing device. The mobile computing device obtains altitude data from a first sensor and pressure data from a second sensor, and generates a geospatial signature based upon the altitude data and the pressure data. The mobile computing device compares the geospatial signature and the baseline geospatial profile, and enters a second operational mode if a difference between one or more corresponding values of the baseline geospatial profile and the geospatial signature exceeds a predetermined threshold.

Abstract: User weighted data indicating acceptance of a given data extraction template and weighted data indicating the number of data fields that the data extraction template can extract accurately is used to calculate data extraction template ranking, or a weighted ranking score, to be associated with the data extraction template. Then the data extraction template having the highest data extraction template ranking score is used in a first attempt to extract data from a source documents of the source document type associated with the data extraction templates. As more data extraction templates associated with a given source document type are received, the data extraction templates having the lowest data extraction template ranking scores are detected/eliminated.

Abstract: User acceptance of a given data extraction template and the number of data fields that the data extraction template can extract accurately is used to calculate data extraction template ranking, or a ranking score, to be associated with the data extraction template. Then the data extraction template having the highest data extraction template ranking score is used in a first attempt to extract data from a source documents of the source document type associated with the data extraction templates. As more data extraction templates associated with a given source document type are received, data extraction template ranking scores are updated/modified, and, in one example, the data extraction templates having the lowest data extraction template ranking scores are detected/eliminated.

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: User acceptance of a given data extraction template and the number of data fields that the data extraction template can extract accurately is used to calculate data extraction template ranking, or a ranking score, to be associated with the data extraction template. Then the data extraction template having the highest data extraction template ranking score is used in a first attempt to extract data from a source documents of the source document type associated with the data extraction templates. As more data extraction templates associated with a given source document type are received, data extraction template ranking scores are updated/modified, and, in one example, the data extraction templates having the lowest data extraction template ranking scores are detected/eliminated.