Abstract: A digital ink annotation process and system for processing digital documents and digital ink annotations therein. An annotation's position is maintained within a document such that the original intent and meaning of the annotation is preserved. This is true even if the document is edited, resized, displayed on a different device or otherwise modified. The process includes automatic and manual grouping of digital ink strokes within a document to define digital ink annotations, classifying the annotations according to annotation type, and anchoring the annotations to appropriate regions or positions in a document. The process further includes reflowing the annotations in a new document layout such that the annotations conform and adapt to the new layout while preserving the original intents and meanings of the annotations. The system includes a classification module, an anchoring module, a reflow module and a clean-up module to implement the digital ink annotation process.

Abstract: A two-dimensional representation of a document is leveraged to extract a hierarchical structure that facilitates recognition of the document. The visual structure is grammatically parsed utilizing two-dimensional adaptations of statistical parsing algorithms. This allows recognition of layout structures (e.g., columns, authors, titles, footnotes, etc.) and the like such that structural components of the document can be accurately interpreted. Additional techniques can also be employed to facilitate document layout recognition. For example, grammatical parsing techniques that utilize machine learning, parse scoring based on image representations, boosting techniques, and/or “fast features” and the like can be employed to facilitate in document recognition.

Abstract: A malicious behavior detection/prevention system, such as an intrusion detection system, is provided that uses active learning to classify entries into multiple classes. A single entry can correspond to either the occurrence of one or more events or the non-occurrence of one or more events. During a training phase, entries are automatically classified into one of multiple classes. After classifying the entry, a generated model for the determined class is utilized to determine how well an entry corresponds to the model. Ambiguous classifications along with entries that do not fit the model well for the determined class are selected for labeling by a human analyst. The selected entries are presented to a human analyst for labeling. These labels are used to further train the classifier and the models. During an evaluation phase, entries are automatically classified using the trained classifier and a policy associated with determined class is applied.

Abstract: The present invention leverages spatial relationships to provide a systematic means to recognize text and/or graphics. This allows augmentation of a sketched shape with its symbolic meaning, enabling numerous features including smart editing, beautification, and interactive simulation of visual languages. The spatial recognition method obtains a search-based optimization over a large space of possible groupings from simultaneously grouped and recognized sketched shapes. The optimization utilizes a classifier that assigns a class label to a collection of strokes. The overall grouping optimization assumes the properties of the classifier so that if the classifier is scale and rotation invariant the optimization will be as well. Instances of the present invention employ a variant of AdaBoost to facilitate in recognizing/classifying symbols. Instances of the present invention employ dynamic programming and/or A-star search to perform optimization.

Abstract: A handwriting recognition system interprets handwritten text and produces a typed interpretation of that text. When the initial interpretation of the handwritten text is inaccurate, the handwriting recognition system alters the initial recognition by reinterpreting the handwritten text in view of a correction made by a user and constraints (e.g., derived by assumptions in user behavior). The handwriting recognition system intelligently reinterprets and renews its text recognition each time the user implements a correction. In effect, a single correction can trigger multiple adjustments to the text recognition. Therefore, with the use of a reinterpretation algorithm, the handwriting recognition system helps the user obtain the desired result in fewer correction steps.

Abstract: A system and method that facilitates and effectuates in situ search for active note taking. The system and method includes receiving gestures from a stylus and a tablet associated with the system. Upon recognizing the gesture as belonging to a set of known and recognized gestures, the system creates an embeddable object, initiates a search with terms indicated by the gesture, associates the search results with the created object and inserts the object in close proximity with the terms that instigated the search.

Abstract: Techniques for efficiently and accurately organizing freeform handwriting into lines. A global cost function is employed to find the simplest partitioning of electronic ink strokes into line groups that also maximize the “goodness” of the resulting lines and the consistency of their configuration. The “goodness” of a line may be based upon its linear regression error and the horizontal and vertical compactness of the strokes making up the line. The line consistency configuration for a grouping of strokes is measured by the angle difference between neighboring groups. The global cost function also takes into account the complexity of the stroke partitioning, measured by the number of lines into which the strokes are grouped. An initial grouping of strokes is made, and the cost for this initial grouping is determined. Alternate groupings of the initial stroke grouping are then generated.

Abstract: Image recognition is utilized to facilitate in scoring parse trees for two-dimensional recognition tasks. Trees and subtrees are rendered as images and then utilized to determine parsing scores. Other instances of the subject invention can incorporate additional features such as stroke curvature and/or nearby white space as rendered images as well. Geometric constraints can also be employed to increase performance of a parsing process, substantially improving parsing speed, some even resolvable in polynomial time. Additional performance enhancements can be achieved in yet other instances of the subject invention by employing constellations of integral images and/or integral images of document features.

Abstract: Documents are collected from a variety of publicly available sources that contain product data including product reviews, product specifications, price information and the like. Snippets of text obtained from the documents are analyzed for relevance, sentiment, credibility and other aspects that help evaluate the quality of a product. Feature vectors are computed for snippets to analyze relevance, sentiment, or credibility. Statistical analysis is performed on the feature vectors to estimate a measure of the relevance, sentiment, or credibility. Factors associated with various snippets are aggregated to compute a quality score for a product or a particular aspect of product including product features, attributes, usages, or user personas. Information is displayed on a user interface that allows the user to examine the details relevant to computation of the quality score.

Abstract: The present invention leverages classification type detectors and/or context information to provide a systematic means to recognize and anchor annotation strokes, providing reflowable digital annotations. This allows annotations in digital documents to be archived, shared, searched, and easily manipulated. In one instance of the present invention, an annotation recognition method obtains an input of strokes that are grouped, classified, and anchored to underlying text and/or points in a document. Additional instances of the present invention utilize linguistic content, domain specific information, anchor context, and document context to facilitate in correctly recognizing an annotation.

Abstract: The present invention relates to systems and methods that facilitate annotating digital documents (e.g., digital inking) with devices such as Tablet PCs, PDAs, cell phones, and the like. The systems and methods provide for multi-scale navigation during document annotating via a space-scale framework that fluidly generates and moves a zoom region relative to a document and writing utensil. A user can employ this zoom region to annotate various portions of the document at a size comfortable to the user and suitably scaled to the device display. The space-scale framework enables dynamic navigation, wherein the zoom region location, size, and shape, for example, can automatically adjust as the user annotates. When the user finishes annotating the document, the annotations scale back with the zoom region to original page size. These novel features provide advantages over conventional techniques that do not contemplate multi-scale navigation during document annotating.

Abstract: Dynamic inference is leveraged to provide online sequence data labeling. This provides real-time alternatives to current methods of inference for sequence data. Instances estimate an amount of uncertainty in a prediction of labels of sequence data and then dynamically predict a label when an uncertainty in the prediction is deemed acceptable. The techniques utilized to determine when the label can be generated are tunable and can be personalized for a given user and/or a system. Employed decoding techniques can be dynamically adjusted to tradeoff system resources for accuracy. This allows for fine tuning of a system based on available system resources. Instances also allow for online inference because the inference does not require knowledge of a complete set of sequence data.

Abstract: Systems, methods, and computer-readable media for making rich, flexible, and more natural electronic ink annotations in an electronic document include creating a first context node associated with a first portion of a base portion of an electronic document; creating a second context node associated with an annotation to the base portion; and linking the second context node with the first context node.

Abstract: A malicious behavior detection/prevention system, such as an intrusion detection system, is provided that uses active learning to classify entries into multiple classes. A single entry can correspond to either the occurrence of one or more events or the non-occurrence of one or more events. During a training phase, entries are automatically classified into one of multiple classes. After classifying the entry, a generated model for the determined class is utilized to determine how well an entry corresponds to the model. Ambiguous classifications along with entries that do not fit the model well for the determined class are selected for labeling by a human analyst The selected entries are presented to a human analyst for labeling. These labels are used to further train the classifier and the models. During an evaluation phase, entries are automatically classified using the trained classifier and a policy associated with determined class is applied.

Abstract: A system and method that facilitates and effectuates in situ search for active note taking. The system and method includes receiving gestures from a stylus and a tablet associated with the system. Upon recognizing the gesture as belonging to a set of known and recognized gestures, the system creates an embeddable object, initiates a search with terms indicated by the gesture, associates the search results with the created object and inserts the object in close proximity with the terms that instigated the search.

Abstract: Annotation recognition and parsing is accomplished by first recognizing and grouping shapes such that relationships between the annotations and the underlying text and/or images can be determined. The recognition and grouping is followed by categorization of recognized annotations according to predefined types. The classification may be according to functionality, relation to content, and the like. In a third phase, the annotations are anchored to the underlying text or images they are found to be related to.

Abstract: A handwriting recognition system interprets handwritten text and produces a typed interpretation of that text. When the initial interpretation of the handwritten text is inaccurate, the handwriting recognition system alters the initial recognition by reinterpreting the handwritten text in view of a correction made by a user and constraints (e.g., derived by assumptions in user behavior). The handwriting recognition system intelligently reinterprets and renews its text recognition each time the user implements a correction. In effect, a single correction can trigger multiple adjustments to the text recognition. Therefore, with the use of a reinterpretation algorithm, the handwriting recognition system helps the user obtain the desired result in fewer correction steps.

Abstract: Dynamic inference is leveraged to provide online sequence data labeling. This provides real-time alternatives to current methods of inference for sequence data. Instances estimate an amount of uncertainty in a prediction of labels of sequence data and then dynamically predict a label when an uncertainty in the prediction is deemed acceptable. The techniques utilized to determine when the label can be generated are tunable and can be personalized for a given user and/or a system. Employed decoding techniques can be dynamically adjusted to tradeoff system resources for accuracy. This allows for fine tuning of a system based on available system resources. Instances also allow for online inference because the inference does not require knowledge of a complete set of sequence data.

Abstract: A method of analyzing electronic ink, in which document data for a document containing electronic ink content is received from a software application running on a first processing thread. The first processing thread is employed to provide the document data to an electronic ink analysis process for analyzing on a second processing thread. Control of the first processing thread is then returned to the software application. After the results of the analysis are received, the results are reconciled with the current document data for the document.

Abstract: A digital ink annotation process and system for processing digital documents and digital ink annotations therein. An annotation's position is maintained within a document such that the original intent and meaning of the annotation is preserved. This is true even if the document is edited, resized, displayed on a different device or otherwise modified. The process includes automatic and manual grouping of digital ink strokes within a document to define digital ink annotations, classifying the annotations according to annotation type, and anchoring the annotations to appropriate regions or positions in a document. The process further includes reflowing the annotations in a new document layout such that the annotations conform and adapt to the new layout while preserving the original intents and meanings of the annotations. The system includes a classification module, an anchoring module, a reflow module and a clean-up module to implement the digital ink annotation process.