| Patent application number | Description | Published |
|---|
| 20080281922 | AUTOMATIC GENERATION OF EMAIL PREVIEWS AND SUMMARIES - An incoming electronic communication is broken down into message portions. Features of the message portions are extracted and the message portions are converted into sparse feature vectors. The probabilities of the message portions being of interest of the user are calculated and the message portions are converted back into text. Message portions with a relatively high probability of being of interest to a user are presented to the user as a summary. | 11-13-2008 |
| 20080300872 | SCALABLE SUMMARIES OF AUDIO OR VISUAL CONTENT - Providing for browsing a summary of content formed of keywords that can scale to a user-defined level of detail is disclosed herein. Components of a system can include a summarization component that extracts keywords related to the content and associates the keywords with portions thereof, and a zooming component that displays a number of keywords based on a keyword/keyphrase relevance rank and a zoom factor. Additionally, a speech to text component can translate speech associated with the content into text, wherein the keywords are extracted from the translated text. Consequently, the claimed subject matter can present a variable hierarchy of keywords to form a scalable summary of such recorded content. | 12-04-2008 |
| 20090006343 | MACHINE ASSISTED QUERY FORMULATION - Architecture for completing search queries by using artificial intelligence based schemes to infer search intentions of users. Partial queries are completed dynamically in real time. Additionally, search aliasing can also be employed. Custom tuning can be performed based on at least query inputs in the form of text, graffiti, images, handwriting, voice, audio, and video signals. Natural language processing occurs, along with handwriting recognition and slang recognition. The system includes a classifier that receives a partial query as input, accesses a query database based on contents of the query input, and infers an intended search goal from query information stored on the query database. A query formulation engine receives search information associated with the intended search goal and generates a completed formal query for execution. | 01-01-2009 |
| 20090006344 | MARK-UP ECOSYSTEM FOR SEARCHING - Architecture for completing search queries by using artificial intelligence based schemes to infer search intentions of users. Partial queries are completed dynamically in real time. Additionally, search aliasing can also be employed. Custom tuning can be performed based on at least query inputs in the form of text, graffiti, images, handwriting, voice, audio, and video signals. Natural language processing occurs, along with handwriting recognition and slang recognition. The system includes a classifier that receives a partial query as input, accesses a query database based on contents of the query input, and infers an intended search goal from query information stored on the query database. A query formulation engine receives search information associated with the intended search goal and generates a completed formal query for execution. | 01-01-2009 |
| 20090006345 | VOICE-BASED SEARCH PROCESSING - Architecture for completing search queries by using artificial intelligence based schemes to infer search intentions of users. Partial queries are completed dynamically in real time. Additionally, search aliasing can also be employed. Custom tuning can be performed based on at least query inputs in the form of text, graffiti, images, handwriting, voice, audio, and video signals. Natural language processing occurs, along with handwriting recognition and slang recognition. The system includes a classifier that receives a partial query as input, accesses a query database based on contents of the query input, and infers an intended search goal from query information stored on the query database. A query formulation engine receives search information associated with the intended search goal and generates a completed formal query for execution. | 01-01-2009 |
| 20090024356 | DETERMINATION OF ROOT CAUSE(S) OF SYMPTOMS USING STOCHASTIC GRADIENT DESCENT - Diagnosis of one or more root causes of symptoms is performed by using stochastic gradient descent to find the optimal parameters of a variational distribution. This methodology, called variational gradient descent, permits fast diagnosis for a large number (greater than 1,000) or very large number (greater than 1,000,000) of symptom observations. A real-time application of the root cause diagnosis can determine currently occurring intermittent root causes. Diagnosis can be performed in a number of scenarios, such as medical disease detection or computer/network failure. | 01-22-2009 |
| 20090099988 | ACTIVE LEARNING USING A DISCRIMINATIVE CLASSIFIER AND A GENERATIVE MODEL TO DETECT AND/OR PREVENT MALICIOUS BEHAVIOR - 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. | 04-16-2009 |
| 20090198654 | DETECTING RELEVANT CONTENT BLOCKS IN TEXT - A system that facilitates detecting a targeted topic in a document is described herein. The system includes a receiver component that receives a document. The system additionally includes a topic model component trained using a plurality of training documents including the topic and a plurality of training documents that do not include the topic. The topic model component analyzes the document and automatically determines which portions of the document include the topic and which portions of the document do not include the topic. | 08-06-2009 |
