Abstract: Methods and systems for using images that represent time-series data to forecast corresponding images depicting future values of the time-series data are provided. The method includes: receiving a set of time-series data; converting the set of time-series data into a partial first image that includes a blank region to which future data to be included in the first set of time-series data corresponds; and performing an inpainting operation with respect to the partial first image by generating pixels for filling in the blank region in order to produce an augmented version of the first image. A machine learning algorithm that is trained by using historical time-series data may be used to perform the inpainting operation.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: Systems and methods for privacy-preserving summarization of digital activity are disclosed. According to one embodiment in an information processing apparatus comprising at least one computer processor and at least one display, a privacy-preserving digital activity computer program performing the following: (1) capturing a blurred or pixelated screenshot of the at least one display; (2) identifying a plurality of computer application visible in the blurred or pixelated screenshot; (3) identifying a foreground or actively-used application out of the plurality computer applications in the blurred or pixelated screenshot; and (4) logging the visible computer applications and the foreground or actively-used application.

Abstract: A method for providing a data-driven tool for individualized financial planning is provided. The method includes: receiving first information that relates to a user, the first information including at least one financial goal of the user; applying, to the first information, a machine learning algorithm that is uses historical data that relates to financial outcomes; calculating, based on an output of the machine learning algorithm, a probability that the at least one financial goal of the user is achievable; and determining, based on an output the machine learning algorithm, a proposed sequence of actions to be taken by the user with respect to achieving the at least one financial goal. The method may further include applying an artificial intelligence (AI) algorithm that uses a Monte Carlo tree search (MCTS) technique with respect to potential user actions.

Abstract: A method for using images that represent time-series data to forecast future images depicting future values as pixelated information is provided. The method includes: receiving a first set of time-series data; converting the received first set of time-series data into a first image; and using the first image to forecast a future image depicting future values as pixelated information that corresponds to a future time interval Training sets of time-series data are used to generate historical data that provides input to a machine learning algorithm, which provides, as an output, a composite image that depicts the future values as pixelated information that reflects associated uncertainties in the value predictions.

Abstract: A method and system for detecting, segmenting, and extracting salient regions in documents by using attention tracking sensors is provided. The method includes: receiving an image that corresponds to a document; receiving, from a sensor, a sequence of measurements that correspond to a human reading of the document; determining, based on the sequence of measurements, at least one region of the document as being a salient document region; demarcating the salient document region in an electronically displayable manner; and outputting a file that includes a displayable version of the document with the demarcated document region. The salient document region may include a title, a section header, and/or a table. The sensor may be an eye-tracking sensor that detects a sequence of eye-gaze positions on the document as a function of time.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: A method for analyzing time-series data is provided. The method includes: identifying, from within a first set of time-series data, data sequences that correspond to patterns, each data sequence being representative of a predetermined time interval; converting the data sequences into respective pattern images; receiving a second set of time-series data; converting the second set of time-series data into an additional image; comparing each of the pattern images with a portion of the additional image that corresponds to a most recent time interval; and determining whether the portion of the additional image that corresponds to the most recent time interval corresponds to any of the patterns based on a result of the comparison.

Abstract: A method for identifying, contracting, evaluating, bounding, and filtering out uncertainty in survey data is provided. The method includes: receiving survey responses with respect to a customer survey; constructing a simulated numerical model that replicates the structure of the original survey by using responses that are generated randomly from distribution of responses with constraint variability that specifically account for the uncertainty that arises from the subjective nature of sampling response from an ordinal range of possible options; matching between the original survey and the numerical model using a machine learning algorithm; and evaluating and filtering out the uncertainty of the original survey. In addition, a method is offered to constrain and contract the uncertainty by assigning survey responses to corresponding evenly distributed bins and by calibrating the survey responses by attaching a short textual description to each of the ordinal values in the original survey.

Abstract: Systems and methods for privacy-preserving summarization of digital activity are disclosed. According to one embodiment in an information processing apparatus comprising at least one computer processor and at least one display, a privacy-preserving digital activity computer program performing the following: (1) capturing a blurred or pixelated screenshot of the at least one display; (2) identifying a plurality of computer application visible in the blurred or pixelated screenshot; (3) identifying a foreground or actively-used application out of the plurality computer applications in the blurred or pixelated screenshot; and (4) logging the visible computer applications and the foreground or actively-used application.

Abstract: A method for using images that represent time-series data to forecast future images depicting future values as pixelated information is provided. The method includes: receiving a first set of time-series data; converting the received first set of time-series data into a first image; and using the first image to forecast a future image depicting future values as pixelated information that corresponds to a future time interval Training sets of time-series data are used to generate historical data that provides input to a machine learning algorithm, which provides, as an output, a composite image that depicts the future values as pixelated information that reflects associated uncertainties in the value predictions.