A portable device configured to provide an augmented reality experience is provided. The portable device has a display screen configured to display a real world scene. The device includes an image capture device associated with the display screen. The image capture device is configured to capture image data representing the real world scene. The device includes image recognition logic configured to analyze the image data representing the real world scene. Image generation logic responsive to the image recognition logic is included. The image generation logic is configured to incorporate an additional image into the real world scene. A computer readable medium and a system providing an augmented reality environment are also provided.

Portable augmented reality device and method

A system and method for capturing objects and balancing systems resources in a capture system are described. An object is captured, metadata associated with the objected generated, and the object and metadata stored.

Attributes of captured objects in a capture system

A Natural Language Understanding system is provided for indexing of free text documents. The system according to the invention utilizes typographical and functional segmentation of text to identify those portions of free text that carry meaning. The system then uses words and multi-word terms and phrases identified in the free to text to identify concepts in the free text. The system uses a lexicon of terms linked to a formal ontology that is independent of a specific language to extract concepts from the free text based on the words and multi-word terms in the free text. The formal ontology contains both language independent domain knowledge concepts and language dependent linguistic concepts that govern the relationships between concepts and contain the rules about how language works. The system according to the current invention may preferably be used to index medical documents and assign codes from independent coding systems, such as, SNOMED, ICD-9 and ICD-10. The system according to the current invention may also preferably make use of syntactic parsing to improve the efficiency of the method.

Conceptual world representation natural language understanding system and method

Computer method and apparatus for extracting information from a Web page is disclosed. The invention apparatus is formed of an extractor coupled to receive Web pages from a source. The extractor uses natural language processing to extract desired information from the Web page. A storage subsystem receives from the extractor the extracted desired information and stores the extracted desired information in a database. The invention method for extracting data from a Web page includes the computer implemented steps of (i) using natural language processing, finding possible formal names on a given Web page, (ii) using pattern matching, searching the given Web page for formal names not found by the natural language processing, and (iii) refining a combined set of the found formal names to produce a working set of people and organization names extracted from the given Web page. The refining includes determining aliases of respective people and organization names, so as to effectively reduce duplicate names.

Computer method and apparatus for extracting data from web pages

A system enables a transcriptionist to replace a first written form (such as an abbreviation) of a concept with a second written form (such as an expanded form) of the same concept. For example, the system may display to the transcriptionist a draft document produced from speech by an automatic speech recognizer. If the transcriptionist recognizes a first written form of a concept that should be replaced with a second written form of the same concept, the transcriptionist may provide the system with a replacement command. In response, the system may identify the second written' form of the concept and replace the first written form with the second written form in the draft document.

Replacing text representing a concept with an alternate written form of the concept

A natural language understanding system is described to provide generation of concept codes from free-text medical data. A probabilistic model of lexical semantics, is implemented by means of a Bayesian network, and is used to determine the most probable concept or meaning associated with a sentence or phrase. The inventive method and system includes the steps of checking for synonyms, checking spelling, performing syntactic parsing, transforming text to its “deep” or semantic form, and performing a semantic analysis based on a probabilistic model of lexical semantics.

Probabilistic system for natural language processing

A natural language understanding system is described which provides for the generation of concept codes from free-text medical data. A probabilistic model of lexical semantics, in the preferred embodiment of the invention implemented by means of a Bayesian network, is used to determine the most probable concept or meaning associated with a sentence or phrase. The inventive method and system includes the steps of checking for synonyms, checking spelling, performing syntactic parsing, transforming text to its “deep” or semantic form, and performing a semantic analysis based on a probabilistic model of lexical semantics. In the preferred embodiment of the invention, spell checking and transformational processing as well as semantic analysis make use of semantic probabilistic determinations.

Probabilistic method for natural language processing and for encoding free-text data into a medical database by utilizing a Bayesian network to perform spell checking of words

Objectives: This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems Effectiveness is evaluated on how well NLP supports downstream influenza case-detection for disease surveillance Methods: We independently developed two NLP parsers, one at Intermountain Healthcare (IH) in Utah and the other at University of Pittsburgh Medical Center (UPMC) using local clinical notes from emergency department (ED) encounters of influenza We measured NLP parser performance for the presence and absence of 70 clinical findings indicative of influenza We then developed Bayesian network models from NLP processed reports and tested their ability to discriminate among cases of influenza, non-influenza influenza-like illness (NI-ILI), and ‘other’ diagnosis Results: On Intermountain Healthcare reports, recall and precision of the IH NLP parser were 071 and 075, respectively, and UPMC NLP parser, 067 and 079 On University of Pittsburgh Medical Center reports, recall and precision of the UPMC NLP parser were 073 and 080, respectively, and IH NLP parser, 053 and 080 Bayesian case-detection performance measured by AUROC for influenza versus non-influenza on Intermountain Healthcare cases was 093 (using IH NLP parser) and 093 (using UPMC NLP parser) Case-detection on University of Pittsburgh Medical Center cases was 095 (using UPMC NLP parser) and 083 (using IH NLP parser) For influenza versus NI-ILI on Intermountain Healthcare cases performance was 070 (using IH NLP parser) and 076 (using UPMC NLP parser) On University of Pisstburgh Medical Center cases, 076 (using UPMC NLP parser) and 065 (using IH NLP parser) Conclusion: In all but one instance (influenza versus NI-ILI using IH cases), local parsers were more effective at supporting case-detection although performances of non-local parsers were reasonable

/pdf/the-effects-of-natural-language-processing-on-cross-1trtwvfkaa.pdf

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance

A natural language understanding is described which provides for the generation of concept codes from free-text medical data. A probabilistic model of lexical semantics, in the preferred embodiment of the invention implemented by means of a Bayesian network, is used to determine the most probable concept or meaning associated with a sentence or phrase. The inventive method and system include the steps of checking for synonyms (301), checking spelling (302), performing syntactic parsing (303), transforming text into its 'deep' or semantic form (304), and performing a semantic analysis based on a probabilistic model of lexical semantics (305). In the preferred embodiment of the invention, spell checking and transformational processing as well as semantic analysis make use of semantic probabilistic determinations.

Aprobabilistic system for natural language processing

La presente invention concerne un systeme comprenant un langage naturel qui assure la generation de codes de concept a partir de donnees sous forme de texte libre. Selon le mode de realisation prefere de l'invention mis en oeuvre a l'aide d'un reseau de Bayes, on utilise un modele de probabilites de semantique lexicale de maniere a determiner la signification ou le concept les plus probables associes a une phrase ou a une locution. Le procede et le systeme de l'invention consistent a verifier les synonymes (301), a verifier l'orthographe (302), a executer une analyse syntaxique (303), a transformer un texte en sa forme 'profonde' ou semantique (304) et a executer une analyse semantique basee sur un modele de probabilites de semantique lexicale (305). Selon le mode de realisation prefere de l'invention, la verification d'orthographe et le procede de transformation ainsi que l'analyse semantique utilisent des determinations de probabilitee semantiques.

Systeme de probabilites pour traitement du langage naturel

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