| Patent application number | Description | Published |
|---|
| 20080313211 | Data Relationship Visualizer - Data having express or implied relationships may be displayed by selecting a starting entity in a data structure, building a relationship tree, and building and optimizing a relationship matrix based on the relationship tree. The optimized relationship matrix may be used to layout and render a graphical image that positions various elements with respect to the starting entity based on the relationships. The distance matrix may be optimized by creating a first distance matrix based on the relationship tree, developing a dissimilarity matrix based on expressed or implied relationships, and multiplying the dissimilarity matrix by a weighting factor to determine a distance matrix that may be optimized by multi-dimensional scaling. An optimized weighting factor may be determined and used to select an optimized distance matrix. | 12-18-2008 |
| 20090002392 | Integrated platform for user input of digital ink - Described is a technology that provides an integrated platform for users to use different kinds of digital ink (e.g., handwritten characters, sketched shapes, handwritten formulas) when interacting with computer programs. The platform interprets the user's digital ink input and outputs one or more associated items into an application program. The output items can be customized for different application programs. In one aspect, the platform includes an ink panel having different operating modes for receiving digital ink, and a recognition service that recognizes different types of digital ink. The recognition service may include a unified recognizer that recognizes different types of digital ink, e.g., characters and shapes. Another recognizer may be included such as an equation recognizer. If the recognition result is text while in a non-text mode, the text may be used in a keyword search to locate items; otherwise, the recognition result may be used without keyword searching. | 01-01-2009 |
| 20090003658 | Digital ink-based search - Described is searching directly based on digital ink input to provide a result set of one or more items. Digital ink input (e.g., a handwritten character, sketched shape, gesture, drawing picture) is provided to a search engine and interpreted thereby, with a search result (or results) returned. Different kinds of digital ink can be used as search input without changing modes. The search engine includes a unified digital ink recognizer that recognizes digital ink as a character or another type of digital ink. When the recognition result is a character, the character may be used in a keyword search to find one or more corresponding non-character items, e.g., from a data store. When the recognition result is a non-character item, the non-character item is provided as the result, without keyword searching. The search result may appear as one or more item representations, such as in a user interface result panel. | 01-01-2009 |
| 20090003703 | Unifield digital ink recognition - Described is a unified digital ink recognizer that recognizes various different types of digital ink data, such as handwritten character data and custom data, e.g., sketched shapes, handwritten gestures, and/or drawn pictures, without further participation by a user such as recognition mode selection or parameter input. For a custom item, the output may be a Unicode value from a private use area of Unicode. Building the unified digital ink recognizer may include defining the data set to be recognized, extracting features of training samples corresponding to the dataset items to build a recognizer model, evaluating the recognizer model using testing data, and modifying the recognizer model using tuning data. The extracted features may be processed into feature data for a multi-dimensional nearest neighbor recognizer approach; the extracted features for the samples of each class is calculated and combined into the feature set for this class in the resulting recognizer model. | 01-01-2009 |
| 20090003706 | Combining online and offline recognizers in a handwriting recognition system - Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score. | 01-01-2009 |
| 20090006883 | Software error report analysis - Described herein is technology for, among other things, accessing error report information. It involves various techniques and tools for analyzing and interrelating failure data contained in error reports and thereby facilitating developers to more easily and quickly solve programming bugs. Numerous parameters may also be specified for selecting and searching error reports. Several reliability metrics are provided to better track software reliability situations. The reliability metrics facilitate the tracking of the overall situation of failures that happen in the real word by providing metrics based on error reports (e.g., failure occurrence trends, failure distributions across different languages). | 01-01-2009 |
| 20090007271 | Identifying attributes of aggregated data - A method for identifying a portion of aggregated software security data is described. The method includes accessing aggregated data associated with software vulnerabilities retrieved from a plurality of on-line sources. The method further includes searching a portion of the aggregated data for an exact match to a particular attribute of the data and searching the portion of the aggregated data for one or more partial matches associated with the particular attribute. The method also includes associating the portion of the data with the particular attribute based on the exact match of one or more of the partial matches. | 01-01-2009 |
| 20090007272 | Identifying data associated with security issue attributes - A method for identifying data related to a software security issue is provided. The method includes accessing a software security issue and determining one or more attributes associated with the software security issue. The method also includes accessing aggregated software security data retrieved from a plurality of on-line sources and searching the aggregated software security data for the attributes associated with the security issue. The method further includes associating a portion of the aggregated data with the security issue based on matching the attributes associated with the security issue with contents of the portion of the aggregated data. | 01-01-2009 |
| 20100161701 | POLYNOMIAL REPRESENTATION FOR SYMBOLIC COMPUTATION - A method for converting a polynomial expression to a data structure for symbolic computation. One or more variables in the polynomial expression may be determined. The variables may be stored in a first array. One or more terms in the polynomial expression may be determined. One or more exponents of the variables in each term may be determined. The exponents may be stored in a second array. One or more coefficients of the terms may be determined. The coefficients may be stored in a third array. | 06-24-2010 |
| 20100163316 | Handwriting Recognition System Using Multiple Path Recognition Framework - Described is a multi-path handwriting recognition framework based upon stroke segmentation, symbol recognition, two-dimensional structure analysis and semantic structure analysis. Electronic pen input corresponding to handwritten input (e.g., a chemical expression) is recognized and output via a data structure, which may include multiple recognition candidates. A recognition framework performs stroke segmentation and symbol recognition on the input, and analyzes the structure of the input to output the data structure corresponding to recognition results. For chemical expressions, the structural analysis may perform a conditional sub-expression analysis for inorganic expressions, or organic bond detection, connection relationship analysis, organic atom determination and/or conditional sub-expression analysis for organic expressions. The structural analysis also performs subscript, superscript analysis and character determination. Further analysis may be performed, e.g., chemical valence analysis and/or semantic structure analysis. | 07-01-2010 |
| 20100169605 | ARBITRARY PRECISION FLOATING NUMBER PROCESSING - Techniques for providing arbitrary precision floating number (APFN) processing are disclosed. In some aspects, an APFN store may be used to store a large number (i.e., an APFN) having many significant digits, which in turn may enable a high degree of precision in mathematical operations. An APFN module may be used to create and define the APFN store. The APFN module may enable a user to define a precision (significant digits) for the large number that corresponds to the size of an array of bytes in the APFN store that are allocated for storing the large number. In further aspects, the APFN store may be used to store additional intermediary data and a resultant. | 07-01-2010 |
| 20100191793 | Symbolic Computation Using Tree-Structured Mathematical Expressions - A method for performing symbolic computations on a mathematical expression. The mathematical expression may be converted to a tree structure having one or more parent nodes and one or more child nodes. Each parent node may be a mathematical operation. Each child node may be a mathematical expression on which the mathematical operation is performed in a specified order. Each child node may be in a hierarchical relationship to one of the parent nodes. The parent nodes, the child nodes or both may be manipulated to perform a first symbolic computation on the mathematical expression. | 07-29-2010 |
| 20100205120 | PLATFORM FOR LEARNING BASED RECOGNITION RESEARCH - A method for researching and developing a recognition model in a computing environment, including gathering one or more data samples from one or more users in the computing environment into a training data set used for creating the recognition model, receiving one or more training parameters defining a feature extraction algorithm configured to analyze one or more features of the training data set, a classifier algorithm configured to associate the features to a template set, a selection of a subset of the training data set, a type of the data samples, or combinations thereof, creating the recognition model based on the training parameters, and evaluating the recognition model. | 08-12-2010 |
| 20100231595 | LARGE SCALE DATA VISUALIZATION WITH INTERACTIVE CHART - This disclosure describes a user interface and techniques for an interactive graphical representation of large scale data on a display. The disclosure describes how large scale data may be viewed using multiple linked charts. In one implementation, a user interface comprises an overview chart. The user may use chart controller(s) to designate one or more portions of the overview chart viewable in subsequent charts. The user may navigate between the overview chart and the subsequent charts using the chart controller(s). | 09-16-2010 |
| 20100262643 | COMPUTING MINIMAL POLYNOMIALS - Described is a technology, such as implemented in a computational software program, by which a minimal polynomial is efficiently determined for a radical expression over the ring Z of integer numbers or the ring Q of rational numbers. The levels of the radical are grouped into a level permutation group that is used to find a level permutation set. An annihilation polynomial is found based upon the level permutation set. The annihilation polynomial is factored, and a selection mechanism selects the minimal polynomial based upon the annihilation polynomial's factors. | 10-14-2010 |
