Patent application number | Description | Published |
20110054853 | RECOVERING THE STRUCTURE OF SPARSE MARKOV NETWORKS FROM HIGH-DIMENSIONAL DATA - A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface. | 03-03-2011 |
20130013538 | RECOVERING THE STRUCTURE OF SPARSE MARKOV NETWORKS FROM HIGH-DIMENSIONAL DATA - A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface. | 01-10-2013 |
20130034277 | SYSTEMS AND METHODS FOR MODELING AND PROCESSING FUNCTIONAL MAGNETIC RESONANCE IMAGE DATA USING FULL-BRAIN VECTOR AUTO-REGRESSIVE MODEL - Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification. | 02-07-2013 |
20140336998 | SYSTEMS AND METHODS FOR MODELING AND PROCESSING FUNCTIONAL MAGNETIC RESONANCE IMAGE DATA USING FULL-BRAIN VECTOR AUTO-REGRESSIVE MODEL - Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification. | 11-13-2014 |
20150348569 | SEMANTIC-FREE TEXT ANALYSIS FOR IDENTIFYING TRAITS - A method, system, and/or computer program product uses speech traits of an entity to predict a future state of the entity. Units of speech are collected from a stream of speech that is generated by a first entity. Tokens from the stream of speech are identified, where each token identifies a particular unit of speech from the stream of speech, and where identification of the tokens is semantic-free. Nodes in a first speech graph are populated with the tokens, and a first shape of the first speech graph is identified. The first shape is matched to a second shape, where the second shape is of a second speech graph from a second entity in a known category. The first entity is assigned to the known category, and a future state of the first entity is predicted based on the first entity being assigned to the known category. | 12-03-2015 |
20160124908 | FACILITATING A MEETING USING GRAPHICAL TEXT ANALYSIS - Embodiments relate to facilitating a meeting. A method for facilitating a meeting of a group of participants is provided. The method generates a graph of words from speeches of the participants as the words are received from the participants. The method partitions the group of participants into a plurality of subgroups of participants. The method performs a graphical text analysis on the graph to identify a cognitive state for each participant and a cognitive state for each subgroup of participants. The method informs at least one of the participants about the identified cognitive state of a participant or a subgroup of participants. | 05-05-2016 |
20160124940 | FACILITATING A MEETING USING GRAPHICAL TEXT ANALYSIS - Embodiments relate to facilitating a meeting. A method for facilitating a meeting of a group of participants is provided. The method generates a graph of words from speeches of the participants as the words are received from the participants. The method partitions the group of participants into a plurality of subgroups of participants. The method performs a graphical text analysis on the graph to identify a cognitive state for each participant and a cognitive state for each subgroup of participants. The method informs at least one of the participants about the identified cognitive state of a participant or a subgroup of participants. | 05-05-2016 |
20160140107 | PREDICTING INDIVIDUAL OR CROWD BEHAVIOR BASED ON GRAPHICAL TEXT ANALYSIS OF POINT RECORDINGS OF AUDIBLE EXPRESSIONS - Embodiments relate to determining a crowd behavior. A method of determining a crowd behavior is provided. The method collects, at one or more recording points in a crowd of individuals, audible expressions that the individuals of the crowd make. The method generates a graph of the audible expressions as the audible expressions are collected from the individuals. The method determines a crowd behavior by performing a graphical text analysis on the graph. The method outputs an indication of the crowd behavior to trigger a crowd control measure. | 05-19-2016 |
20160140984 | PREDICTING INDIVIDUAL OR CROWD BEHAVIOR BASED ON GRAPHICAL TEXT ANALYSIS OF POINT RECORDINGS OF AUDIBLE EXPRESSIONS - Embodiments relate to determining a crowd behavior. A method of determining a crowd behavior is provided. The method collects, at one or more recording points in a crowd of individuals, audible expressions that the individuals of the crowd make. The method generates a graph of the audible expressions as the audible expressions are collected from the individuals. The method determines a crowd behavior by performing a graphical text analysis on the graph. The method outputs an indication of the crowd behavior to trigger a crowd control measure. | 05-19-2016 |