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40694 results about "Machine learning" patented technology

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Method, transaction card or identification system for transaction network comprising proprietary card network, eft, ach, or atm, and global account for end user automatic or manual presetting or adjustment of multiple account balance payoff, billing cycles, budget control and overdraft or fraud protection for at least one transaction debit using at least two related financial accounts to maximize both end user control and global account issuer fees from end users and merchants, including account, transaction and interchange fees

The present invention provides methods, systems and transaction cards or identification systems, using transaction network comprising proprietary card network, EFT, ACH, or ATM, for end user management of a global financial account by manual or automatic prepaying, prepaying, paying or unpaying, debiting or crediting, or readjustment or presetting, using parameters relating to portions of paid or unpaid financial transactions or account balance amounts in multiple credit, cash or other existing, or end user created, financial accounts or sub-accounts in said global financial account that is optionally subject to financial account issuer transaction or readjustment fees from end users and merchants, including optional use for financial transactions as a credit transaction card requiring merchant credit card interchange or other fees, and optional end user fees, as additional revenue to the global account issuer.
Owner:ROSENBERGER RONALD JOHN

Systems and methods for investigation of financial reporting information

Financial data including general ledger activity and underlying journal entries are examined to determine whether risks of material misstatement due to fraudulent financial reporting can be identified. The financial data is analyzed statistically and modeled over time, comparing actual data values with predicted data values to identify anomalies in the financial data. The anomalous financial data is then analyzed using clustering algorithms to identify common characteristics of the various transactions underlying the anomalies. The common characteristics are then compared with characteristics derived from data known to derive from fraudulent activity, and the common characteristics are reported, along with a weight or probability that the anomaly associated with the common characteristic is an identification of risks of material misstatement due to fraud. Large volumes of financial data are therefore efficiently processed to accurately identify risks of material misstatement due to fraud in connection with financial audits, or for actual detection of fraud in connection with forensic and investigative accounting activities. The analysis is enhanced by using flow analysis methods to select subsets of financial data to examine for anomalies. Flow analysis methods are also used to reveal useful business information found in money flow graphs of financial data.
Owner:PRICEWATERHOUSECOOPERS LLP

Human face super-resolution reconstruction method based on generative adversarial network and sub-pixel convolution

The invention discloses a human face super-resolution reconstruction method based on a generative adversarial network and sub-pixel convolution, and the method comprises the steps: A, carrying out the preprocessing through a normally used public human face data set, and making a low-resolution human face image and a corresponding high-resolution human face image training set; B, constructing the generative adversarial network for training, adding a sub-pixel convolution to the generative adversarial network to achieve the generation of a super-resolution image and introduce a weighted type loss function comprising feature loss; C, sequentially inputting a training set obtained at step A into a generative adversarial network model for modeling training, adjusting the parameters, and achieving the convergence; D, carrying out the preprocessing of a to-be-processed low-resolution human face image, inputting the image into the generative adversarial network model, and obtaining a high-resolution image after super-resolution reconstruction. The method can achieve the generation of a corresponding high-resolution image which is clearer in human face contour, is more specific in detail and is invariable in features. The method improves the human face recognition accuracy, and is better in human face super-resolution reconstruction effect.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
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