Abstract: An image data processing method and device are disclosed. The method comprising: receiving a first display data combination, the first display data combination comprises red, green, and blue sub-pixel display data; transforming the first display data combination into a second display data combination, the second display data combination comprises red, green, blue, and white sub-pixel display data; transforming the second display data combination into a third display data combination, the third display data combination comprises any three of the red, green, blue, and white sub-pixel display data. The disclosed method can raise image contrast.

Abstract: A method for driving a liquid crystal display (LCD) device includes: A1: one image frame is extracted from a video; A2: the one image frame is divided into one left-eye image and one right-eye image according to different image arrangements, comparing a color data of the left-eye image with a color data of the right-eye image, and calculating a similarity degree between the left-eye image and the right-eye image of the different image arrangements according to the comparison; and A3: the image arrangement having a maximum similarity degree between the left-eye image and the right-eye image is chosen as a film resource format of a three-dimensional (3D) image of the video to drive the LCD device, and the image arrangement having the maximum similarity degree between the left-eye image and the right-eye image is the image arrangement of the one image frame.

Abstract: Disclosed systems and methods can determine predicted rental income, estimated error of the prediction, and a set of comparable rental real estate properties for use in the valuation of a subject real estate property rental value. In one embodiment, the rent prediction system receives rental information about real-estate properties, determines feature characteristics, trains a rent amount prediction model using the feature characteristics, determines a second set of feature characteristics based on the output of the rent amount prediction model, and trains an error prediction model using the determined second set of feature characteristics. Using the trained models, the systems and method may predict a rental value and prediction error for one or more subject properties.

Abstract: Disclosed systems and methods can determine predicted rental income, estimated error of the prediction, and a set of comparable rental real estate properties for use in the valuation of a subject real estate property rental value. In one embodiment, the rent prediction system receives rental information about real-estate properties, determines feature characteristics, trains a rent amount prediction model using the feature characteristics, determines a second set of feature characteristics based on the output of the rent amount prediction model, and trains an error prediction model using the determined second set of feature characteristics. Using the trained models, the systems and method may predict a rental value and prediction error for one or more subject properties.

Abstract: A technique is provided for determining credit ratings without the need for traditional credit bureau data. In at least one embodiment of the invention, a method comprises the steps of providing a network of linked identity records, each identity record comprising identity related information; receiving an identity record comprising identity related information of the entity; linking the received identity record with at least one of the identity records in the network of linked identity records, quantifying a plurality of linked identity records related to the entity; and generating a credit rating for the entity based on the quantified identity records. Application of the invention is particularly useful for individuals with less than a few years credit behavior recorded on a credit bureau, such as immigrants or young people with limited traditional historical information (“thin file”) and individuals with no traditional historical information (“no hit”).

Abstract: A method of income validation and evaluation for income risk assessment is disclosed in one embodiment. The method includes receiving a borrower loan request including a plurality of borrower data selected from the group consisting of: an employment type code, a borrower date-of-birth, a borrower zip code and a stated monthly income.

Abstract: A method for identifying a fraudulent account application includes receiving a new account application comprising a plurality of identity-related fields and linking the identity-related fields associated with the new account application with identity-related fields associated with a plurality of historical account applications. The links form a graphical pattern on which statistical analysis can be performed to determine the likelihood that the new account application is fraudulent. The statistical analysis can comprise comparing the graphical pattern to a known, or normal graphical pattern in order to detect differences, or anomalies occurring in the graphical pattern associated with the new account application.

Abstract: A method for identifying a fraudulent account application includes receiving a new account application comprising a plurality of identity-related fields and linking the identity-related fields associated with the new account application with identity-related fields associated with a plurality of historical account applications. The links form a graphical pattern on which statistical analysis can performed to determine the likelihood that the new account application is fraudulent. The statistical analysis can comprise comparing the graphical pattern to a known, or normal graphical pattern in order to detect differences, or anomalies occurring in the graphical pattern associated with the new account application.