2415 results about "Data classification" patented technology

The present invention relates to systems and methods that determine intent for received data (e.g., email, voice, graphics . . . ) and respond to the data based on the intent. The systems and methods employ various combinations of features based on shallow and deep linguistic analysis (e.g., semantic and syntactic) to yield very high accuracy. The systems and methods analyze and categorize received data to locate data that can include intent. This data can be further refined by extracting features related to the intent. The features can be utilized by a classifier to determine the intent. If the intent warrants a response, the data are further scrutinized and reformulated to generate a description that is indicative of the intent. The reformulation can include representing the features in a logical form, transforming the form and generating a description of the intent that can be presented to a user visually and / or audibly.

A system and method of monitoring and predicting tool overlay settings comprise generating current lot information, generating historical data, categorizing (binning) the historical data into discrete exposure field size ranges, and predicting current lot tool overlay settings based on the current lot information and historical data. The method monitors the overlay errors during each lot pass through each lithographic process operation. Moreover, the method uses a feedback sorting criteria to monitor the tool overlay settings. Furthermore, the current lot information comprises lithographic field dimensions, wherein the lithographic field optics distortion data is derived from the current lithographic process tool. Additionally, the historical data comprises same-bin lithographic field size dimensions of previous lots, which statistically means the data is derived from the same (or similar) bin of like lots, on the current lithographic process tool.

The invention discloses a desensitization system used for big data. The desensitization system comprises a database type management module, a system management module, a data source management module, a sensitive data discovery module, a desensitization task management module, a desensitization configuration management module, a multi-stage management module, a big data desensitization analysis module and a sensitive data classification module, wherein the database type management module manages the type of desensitization data; the system management module constructs a role for a desensitization system and carries out function authorization and management on the role; the data source management module provides data source management for the desensitization system; the sensitive data discovery module automatically discovers the sensitive data in the data source and submits the sensitive data to the user for regulation and confirmation; the desensitization task management module configures and manages the desensitization task of a big data desensitization system; the desensitization configuration management module manages and defines the sensitive data and carries out desensitization algorithm and desensitization strategy management; the multi-stage management module realizes the state monitoring, the strategy issuing and the data synchronization of multi-stage desensitization equipment; the big data desensitization analysis module carries out real-time analysis on collected desensitization log data to guarantee the security of the desensitization system; and the sensitive data classification module carries out characteristic analysis on the sensitive data to classify the sensitive data. The invention also discloses a desensitization method used for the big data.

A system and a method are provided for integrating a sensitive data discovery engine (SDDE), a data anonymization engine (DAE), a data monitoring module (DMM), and a data retirement module (DRM) and managing sensitive data security across its lifecycle. The SDDE determines sensitive data in similar and variant data sources and applications, identifies their operating application codes, and generates sensitive data discovery intelligence (SDDI). The system generates and distributes one or more templates including the SDDI with metadata, discovery results, and data security rules to the DAE, the DMM, and the DRM deployed on each data source. Based on the templates, the DAE flexibly and consistently masks, encrypts, or tokenizes the sensitive data for static, dynamic, blended, and hybrid anonymization based on different data classifications and application environments; the DMM continuously monitors sensitive data access by users and applications; and the DRM retires inactive sensitive data without removing transactional data.

Disclosed is a system and method for the analysis of event data that enables analysts to create user specified datasets in a dynamic fashion. Performance, equipment and system safety, reliability, and significant event analysis utilizes failure or performance data that are composed in part of time-based records. These data identify the temporal occurrence of performance changes that may necessitate scheduled or unscheduled intervention like maintenance events, trades, purchases, or other actions to take advantage of mitigate or compensate for the observed changes. The criteria used to prompt a failure or performance record can range from complete loss of function to subtle changes in performance parameters that are known to be precursors of more severe events. These specific criteria applied to any explicit specific application and this invention is relevant to this type of data taxonomy and can be applied across all areas in which event data may be collected.

The invention discloses a method for determining the abnormal transaction process of an electronic commodity. The method comprises the steps of: for each electronic commodity, extracting the historical transaction data of the commodity within specified time duration in the past from the historical transaction data recorded in a commodity transaction database; according to each transaction feature which is predetermined to be extracted, extracting a corresponding transaction feature value from the extracted historical transaction data of the commodity within specified time duration in the past; according to a data classification principle corresponding to the corresponding conditions which are satisfied by the extracted transaction feature values simultaneously, taking the volume of transactions of the commodity predicted by one type of historical transaction data corresponding to the data classification principle as the predicted volume of transactions of the commodity within the specified time duration in the past; extracting the actual volume of transactions of the commodity within the specified time duration in the past from the historical transaction data recorded in the commodity transaction database; and determining whether the transactions of the commodity within the specified time duration in the past are abnormal according to the predicted volume of transactions and the actual volume of transactions of the commodity within the specified time duration in the past.

A computer system and method for capture, managing and presenting data obtained from various often unrelated postings via the Internet for examination by a user. This system includes a scraping module having one or more scraping engines operable to scrape information data sets from listings on the corporate sites and web sites, direct feeds, and other sources, wherein the scraping module receives and stores the scraped listing information data sets in a database. The system also has a management platform coordinating all operation of and communication between the sources, system administrators and processing modules. The processing modules in the platform include scraping management module analyzing selected scraped data stored in the database, and a categorization module that examines and categorizes each data set stored in the database into one or more of a predetermined set of categories and returns categorized data sets to the database.

The invention discloses a public bicycle internet of things system, comprising a perception layer, a centralized control layer, a data processing layer, a system service layer and a system management layer. The perception layer is used for acquiring public bicycle equipment and citizen bicycle lease IC card information; the centralized control layer finishes the network building, the information receiving and transmitting of the perception layer, and the data conversion processing of the information; the data processing layer is used for carrying out creating, management, data classification and processing on a system database; the system service layer is used for processing all application operations related to users of a public bicycle system; and the system management layer is used for processing all operations related to management personnel and system maintenance personnel of the public bicycle system. According to the public bicycle internet of things system, the system scale can be configured dynamically, a distributed processing strategy is adopted for system data processing, a bicycle parking locking column and a bicycle branch management box have independent offline processing capability, so that the communication pressure on a system background is greatly reduced, and the system reliability, extensibility, applicability and fault-tolerant ability are greatly improved.

The present invention discloses a multiple-UAV operation information display system which comprises a data receiving unit for receiving flying state data and image data from airborne sensors and classifies and stores into a storage unit corresponding to each UAV, then a processing unit calculates and generates an integrated information display set according to the flying state information, terrain database information of each UAV in the storage unit and the control options of a control unit, and a display unit combines a display parameter and displays the integrated information display set in the whole. According to the system, the relative positions of multiple UAV can be displayed in an integrated way, the relative relationship of UAVs flying at a low altitude and terrain and obstacles on the ground is displayed in a highlighted way, the state sensing ability of control personnel is effectively enhanced, and the system is especially for a multiple-unmanned-helicopter cluster application.

The invention relates to a learning process generating neural networks designed to sort data into two classes separated by a non-linear surface and built up as the needs of the task to be carried out are defined. This surface is either quadratic, or partly linear and partly quadratic. Applications include shape recognition and sorting of objects or data.

The invention relates to a course field multi-modal document classification method based on a cross-modal attention convolutional neural network. Multi-modal document data in a course field is preprocessed; an attention mechanism and a dense convolutional network are combined, a convolutional neural network based on cross-modal attention is provided, and image features with sparsity can be constructed more effectively; a text feature-oriented attention mechanism-based bidirectional long-short-term memory network is provided, so that text features locally associated with image semantics can beefficiently constructed; and cross-modal grouping fusion based on an attention mechanism is designed, so that the local association relationship between the images and the texts in the document can belearned more accurately, and the accuracy of cross-modal feature fusion is improved. Under the data set of the same course field, compared with an existing multi-modal document classification model,the method has better performance, and the accuracy of multi-modal document data classification is improved.

Systems and methods for personal data classification, linkage and purpose of processing prediction are provided. The system for personal data classification includes an entity extraction module for extracting personal data from one or more data repositories in a computer network or cloud infrastructure, a linkage module coupled to the entity extraction module, a linkage module coupled to the entity extraction module and a processing prediction module. The entity extraction module performs entity recognition from the structured, semi-structured and unstructured records in the one or more data repositories. The linkage module uses graph-based methodology to link the personal data to one or more individuals. And the purpose prediction module includes a feature extraction module a purpose of processing prediction module, wherein the feature extraction module extracts both context features and record's features from records in the one or more data repositories, and the purpose of processing prediction module predicts a unique or multiple purpose of processing of the personal data.