Abstract: Methods, systems, and techniques for providing visual expertise to objectively measure, evaluate, and visualize aesthetic change are provided. Example embodiments provide an Aesthetic Delta Measurement System (“ADMS”), which enables users to objectively measure and visualize aesthetic health and wellness and treatment outcomes and to continuously supplement a knowledge repository of objective aesthetic data based upon a combination of automated machine learning and surveyed human input data. The ADMS provides a labeling platform for labeling aesthetic health and wellness over large populations of individuals and a personal analysis application for viewing an individuals aesthetic changes over time. The ADMS provides labeling of images using guides with corresponding discrete scalar values or using pairwise comparison techniques.

Abstract: A system for modular image mining and search, comprising a plurality of image capture devices, a search engine, an indexing engine, a database, and user interface software. A plurality of image capture devices capture images and store them to a database. The indexing engine indexes images to create image data for each image with associated metadata and stores the information to the database. The interface software is used to request search queries via a search engine. The search engine accesses the image data to find images or parts of images that satisfy the search query.

Abstract: Methods and systems for entity recognition and disambiguation using natural language processing techniques are provided. Example embodiments provide an entity recognition and disambiguation system (ERDS) and process that, based upon input of a text segment, automatically determines which entities are being referred to by the text using both natural language processing techniques and analysis of information gleaned from contextual data in the surrounding text. In at least some embodiments, supplemental or related information that can be used to assist in the recognition and/or disambiguation process can be retrieved from knowledge repositories such as an ontology knowledge base. In one embodiment, the ERDS comprises a linguistic analysis engine, a knowledge analysis engine, and a disambiguation engine that cooperate to identify candidate entities from a knowledge repository and determine which of the candidates best matches the one or more detected entities in a text segment using context information.

Abstract: Methods, systems, and techniques for creating, managing, and using query templates to facilitate the execution of relationship queries are provided. Example embodiments provide a Query Template System “QTS”, which enables users, a system, program code, or other people or code to define search tips (i.e., predefined searches) through the generation of query templates that can be used by other users or code, to perform relationship searches using IQL. In one embodiment, the QTS includes a QT editor, a QT dispatcher, a QT creation and index management system, and one or more QT data repositories and indexes. These components cooperate to create and maintain query templates and to search for and retrieve matching query templates. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Methods and systems for entity recognition and disambiguation using natural language processing techniques are provided. Example embodiments provide an entity recognition and disambiguation system (ERDS) and process that, based upon input of a text segment, automatically determines which entities are being referred to by the text using both natural language processing techniques and analysis of information gleaned from contextual data in the surrounding text. In at least some embodiments, supplemental or related information that can be used to assist in the recognition and/or disambiguation process can be retrieved from knowledge repositories such as an ontology knowledge base. In one embodiment, the ERDS comprises a linguistic analysis engine, a knowledge analysis engine, and a disambiguation engine that cooperate to identify candidate entities from a knowledge repository and determine which of the candidates best matches the one or more detected entities in a text segment using context information.

Abstract: A system for modular image mining and search, comprising a plurality of image capture devices, a search engine, an indexing engine, a database, and user interface software. A plurality of image capture devices capture images and store them to a database. The indexing engine indexes images to create image data for each image with associated metadata and stores the information to the database. The interface software is used to request search queries via a search engine. The search engine accesses the image data to find images or parts of images that satisfy the search query.

Abstract: Land classification based on analysis of image data. Feature extraction techniques may be used to generate a feature stack corresponding to the image data to be classified. A user may identify training data from the image data from which a classification model may be generated using one or more machine learning techniques applied to one or more features of the image. In this regard, the classification module may in turn be used to classify pixels from the image data other than the training data. Additionally, quantifiable metrics regarding the accuracy and/or precision of the models may be provided for model evaluation and/or comparison. Additionally, the generation of models may be performed in a distributed system such that model creation and/or application may be distributed in a multi-user environment for collaborative and/or iterative approaches.

Abstract: Methods and systems for entity recognition and disambiguation using natural language processing techniques are provided. Example embodiments provide an entity recognition and disambiguation system (ERDS) and process that, based upon input of a text segment, automatically determines which entities are being referred to by the text using both natural language processing techniques and analysis of information gleaned from contextual data in the surrounding text. In at least some embodiments, supplemental or related information that can be used to assist in the recognition and/or disambiguation process can be retrieved from knowledge repositories such as an ontology knowledge base. In one embodiment, the ERDS comprises a linguistic analysis engine, a knowledge analysis engine, and a disambiguation engine that cooperate to identify candidate entities from a knowledge repository and determine which of the candidates best matches the one or more detected entities in a text segment using context information.

Abstract: Feature extraction of image data using feature extraction modules. The feature extraction modules may be provided in an architecture that allows for modular, decoupled generation and/or operation of the feature extraction modules to generate feature data corresponding to image data. In this regard, the feature extraction modules may communicate with a file system storing image data and feature data by way of a common interface format. Accordingly, regardless of the nature of the execution of the feature extraction module, each feature extraction module may be communicative by way of the common interface format, thereby providing a modular approach that is highly scalable, flexible, and adaptive.

Abstract: A system for modular image mining and search, comprising a plurality of image capture devices, a search engine, an indexing engine, a database, and user interface software. A plurality of image capture devices capture images and store them to a database. The indexing engine indexes images to create image data for each image with associated metadata and stores the information to the database. The interface software is used to request search queries via a search engine. The search engine accesses the image data to find images or parts of images that satisfy the search query.

Abstract: Methods and systems for extending keyword searching techniques to syntactically and semantically annotated data are provided. Example embodiments provide a Syntactic Query Engine (“SQE”) that parses, indexes, and stores a data set as an enhanced document index with document terms as well as information pertaining to the grammatical roles of the terms and ontological and other semantic information. In one embodiment, the enhanced document index is a form of term-clause index, that indexes terms and syntactic and semantic annotations at the clause level. The enhanced document index permits the use of a traditional keyword search engine to process relationship queries as well as to process standard document level keyword searches. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Land classification based on analysis of image data. Feature extraction techniques may be used to generate a feature stack corresponding to the image data to be classified. A user may identify training data from the image data from which a classification model may be generated using one or more machine learning techniques applied to one or more features of the image. In this regard, the classification module may in turn be used to classify pixels from the image data other than the training data. Additionally, quantifiable metrics regarding the accuracy and/or precision of the models may be provided for model evaluation and/or comparison. Additionally, the generation of models may be performed in a distributed system such that model creation and/or application may be distributed in a multi-user environment for collaborative and/or iterative approaches.

Abstract: Feature extraction of image data using feature extraction modules. The feature extraction modules may be provided in an architecture that allows for modular, decoupled generation and/or operation of the feature extraction modules to generate feature data corresponding to image data. In this regard, the feature extraction modules may communicate with a file system storing image data and feature data by way of a common interface format. Accordingly, regardless of the nature of the execution of the feature extraction module, each feature extraction module may be communicative by way of the common interface format, thereby providing a modular approach that is highly scalable, flexible, and adaptive.

Abstract: Methods and systems for extending keyword searching techniques to syntactically and semantically annotated data are provided. Example embodiments provide a Syntactic Query Engine (“SQE”) that parses, indexes, and stores a data set as an enhanced document index with document terms as well as information pertaining to the grammatical roles of the terms and ontological and other semantic information. In one embodiment, the enhanced document index is a form of term-clause index, that indexes terms and syntactic and semantic annotations at the clause level. The enhanced document index permits the use of a traditional keyword search engine to process relationship queries as well as to process standard document level keyword searches. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Land classification based on analysis of image data. Feature extraction techniques may be used to generate a feature stack corresponding to the image data to be classified. A user may identify training data from the image data from which a classification model may be generated using one or more machine learning techniques applied to one or more features of the image. In this regard, the classification module may in turn be used to classify pixels from the image data other than the training data. Additionally, quantifiable metrics regarding the accuracy and/or precision of the models may be provided for model evaluation and/or comparison. Additionally, the generation of models may be performed in a distributed system such that model creation and/or application may be distributed in a multi-user environment for collaborative and/or iterative approaches.

Abstract: Feature extraction of image data using feature extraction modules. The feature extraction modules may be provided in an architecture that allows for modular, decoupled generation and/or operation of the feature extraction modules to generate feature data corresponding to image data. In this regard, the feature extraction modules may communicate with a file system storing image data and feature data by way of a common interface format. Accordingly, regardless of the nature of the execution of the feature extraction module, each feature extraction module may be communicative by way of the common interface format, thereby providing a modular approach that is highly scalable, flexible, and adaptive.

Abstract: Methods, systems, and techniques for creating, managing, and using query templates to facilitate the execution of relationship queries are provided. Example embodiments provide a Query Template System “QTS”, which enables users, a system, program code, or other people or code to define search tips (i.e., predefined searches) through the generation of query templates that can be used by other users or code, to perform relationship searches using IQL. In one embodiment, the QTS includes a QT editor, a QT dispatcher, a QT creation and index management system, and one or more QT data repositories and indexes. These components cooperate to create and maintain query templates and to search for and retrieve matching query templates. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Feature extraction of image data using feature extraction modules. The feature extraction modules may be provided in an architecture that allows for modular, decoupled generation and/or operation of the feature extraction modules to generate feature data corresponding to image data. In this regard, the feature extraction modules may communicate with a file system storing image data and feature data by way of a common interface format. Accordingly, regardless of the nature of the execution of the feature extraction module, each feature extraction module may be communicative by way of the common interface format, thereby providing a modular approach that is highly scalable, flexible, and adaptive.

Abstract: Land classification based on analysis of image data. Feature extraction techniques may be used to generate a feature stack corresponding to the image data to be classified. A user may identify training data from the image data from which a classification model may be generated using one or more machine learning techniques applied to one or more features of the image. In this regard, the classification module may in turn be used to classify pixels from the image data other than the training data. Additionally, quantifiable metrics regarding the accuracy and/or precision of the models may be provided for model evaluation and/or comparison. Additionally, the generation of models may be performed in a distributed system such that model creation and/or application may be distributed in a multi-user environment for collaborative and/or iterative approaches.

Abstract: Methods, systems, and techniques for creating, managing, and using query templates to facilitate the execution of relationship queries are provided. Example embodiments provide a Query Template System “QTS”, which enables users, a system, program code, or other people or code to define search tips (i.e., predefined searches) through the generation of query templates that can be used by other users or code, to perform relationship searches using IQL. In one embodiment, the QTS includes a QT editor, a QT dispatcher, a QT creation and index management system, and one or more QT data repositories and indexes. These components cooperate to create and maintain query templates and to search for and retrieve matching query templates.