73 results about "Co occurring" patented technology

Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Consumer vectors are developed within the vector space, to represent interests of particular consumers by virtue of relative vector positions of consumer and merchant vectors. Various techniques, including clustering, supervised segmentation, and nearest-neighbor analysis, are applied separately or in combination to generate improved predictions of consumer behavior.

Transaction-based behavioral profiling, whereby the entity to be profiled is represented by a stream of transactions, is required in a variety of data mining and predictive modeling applications. An approach is described for assessing inconsistency in the activity of an entity, as a way of detecting fraud and abuse, using service-code information available on each transaction. Inconsistency is based on the concept that certain service-codes naturally co-occur more than do others. An assessment is made of activity consistency looking at the overall activity of an individual entity, as well as looking at the interaction of entities. Several approaches for measuring consistency are provided, including one inspired by latent semantic analysis as used in text analysis. While the description is in the context of fraud detection in healthcare, the techniques are relevant to application in other industries and for purposes other than fraud detection.

An apparatus generates a statistical class sequence model called A class bi-multigram model from input training strings of discrete-valued units, where bigram dependencies are assumed between adjacent variable length sequences of maximum length N units, and where class labels are assigned to the sequences. The number of times all sequences of units occur are counted, as well as the number of times all pairs of sequences of units co-occur in the input training strings. An initial bigram probability distribution of all the pairs of sequences is computed as the number of times the two sequences co-occur, divided by the number of times the first sequence occurs in the input training string. Then, the input sequences are classified into a pre-specified desired number of classes. Further, an estimate of the bigram probability distribution of the sequences is calculated by using an EM algorithm to maximize the likelihood of the input training string computed with the input probability distributions. The above processes are then iteratively performed to generate statistical class sequence model.

A computer-implemented service recommends works to a user based on works currently or previously played or downloaded by the user on a player device or based on playlists stored on the player device. The works may be, for example, music files, video files, electronic books, or other digital content for playing by users. A user may thus obtain personalized recommendations that are based on works obtained from sources (web sites, physical CDs, etc.) that are independent of the recommendations system. In one embodiment, the service identifies pairs of works that are related to each other by virtue of the relatively high frequency with which they co-occur on playlists or within play histories of users. The resulting mappings of works to related works are in-turn used to select works to recommend to users.

Diagnosing and detecting causes of an incident may comprise classifying the incident by keywords, searching for co-occurring and reoccurring group of incidents, summarizing commonalities in the group of incidents, correlating the group of incidents with causes, defining association rules between the commonalities, and predicting potential problems based on the correlated group of incidents with causes.

A system for and method of remote monitoring, screening, assessment and treatment of patients having a mental health illness such as post-traumatic stress disorders or other traumatic stress injury and co-occurring symptomatology. Patients in constant communication with one or more healthcare professionals through a wireless network, complete executable programs on their patient handheld electronic devices and transmit the results of the executable programs to their supervising mental health professional. The mental health professionals review and analyze the collected patient data to make clinical assessments of the patients' mental health status.

Meaningful phrases are distinguished from chance word sequences statistically, by analyzing a large number of documents and using a statistical metric such as a mutual information metric to distinguish meaningful phrases from groups of words that co-occur by chance. In some embodiments, multiple lists of candidate phrases are maintained to optimize the storage requirement of the phrase-identification algorithm. After phrase identification, a combination of words and meaningful phrases can be used to construct clusters of documents.

The application borrows terminology from data mining, association rule learning and topology. A geometric structure represents a collection of concepts in a document set. The geometric structure has a high-frequency keyword set that co-occurs closely which represents a concept in a document set. Document analysis seeks to automate the understanding of knowledge representing the author's idea. Granular computing theory deals with rough sets and fuzzy sets. One of the key insights of rough set research is that selection of different sets of features or variables will yield different concept granulations. Here, as in elementary rough set theory, by “concept” we mean a set of entities that are indistinguishable or indiscernible to the observer (i.e., a simple concept), or a set of entities that is composed from such simple concepts (i.e., a complex concept).

Provided is a process of enhancing or suppressing measures of relationships between documents based on the relationships arising from text pertaining to selected topics, the process including: obtaining a corpus of documents; obtaining a set of topics by self-extracting topics according to the tokens present in text of the documents or manual provision; receiving a selected topic among the set of topics, the selection indicating that relationships between the documents are to be enhanced or suppressed in virtue of the relationships arising from text relating to the selected topic; forming a relationship graph of the documents, wherein: the relationships between pairs of the documents are determined based on co-occurrence of n-grams in the pairs of the documents, and wherein the relationships are enhanced or suppressed in response to co-occurring n-grams being in the respective set of n-grams of the selected topic.

Provided is a process of modifying semantic similarity graphs representative of pair-wise similarity between documents in a corpus, the method comprising obtaining a semantic similarity graph that comprises more than 500 nodes and more than 1000 weighted edges, each node representing a document of a corpus, and each edge weight indicating an amount of similarity between a pair of documents corresponding to the respective nodes connected by the respective edge; obtaining an n-gram indicating that edge weights affected by the n-gram are to be increased or decreased; expanding the n-gram to produce a set of expansion n-grams; adjusting edge weights of edges between pairs of documents in which members of the expanded n-gram set co-occur.

A method for indexing, extracting, analyzing, and utilizing co-occurrences of logical concepts in text documents is disclosed. References to logical concepts are detected by a text processing procedure by detection of descriptors, e.g., names, also including abbreviations, from a hierarchical dictionary with names and synonyms for said logical concepts, or database identifiers. Co-occurring concepts are indexed and stored in a database as a list or a table. Analysis of co-occurrences detects expressed and implied relationships between co-occurring concepts based on statistical and lexical text analysis. The method includes a procedure to create domain-specific hierarchical dictionaries for logical concepts in a given domain.

In various embodiments, the present invention relates disparate objects based on user behavior, thus enabling search engines to provide more comprehensive and accurate results. According to various embodiments of the present invention, multiple kinds of interactions by users with multiple classes of objects can be analyzed. The result is that disparate classes of objects can be related. Derived relations between text and objects can be used to implement search-like functionality or to extend a conventional text retrieval system.

In one embodiment, the present invention is used to improve search results and/or recommendations by employing a filtered co-occurrence matrix that provides a representation as to which queries tend to co-occur with the originally submitted query. By supplementing or replacing the original query with co-occurring queries, the system of the present invention is able to generate results that are more likely to be of interest.

A method for determining a semantic concept associated with an audio signal captured using an audio sensor. A data processor is used to automatically analyze the audio signal using a plurality of semantic concept detectors to determine corresponding preliminary semantic concept detection values, each semantic concept detector being adapted to detect a particular semantic concept. The preliminary semantic concept detection values are analyzed using a joint likelihood model based on predetermined pair-wise likelihoods that particular pairs of semantic concepts co-occur to determine updated semantic concept detection values. One or more semantic concepts are determined based on the updated semantic concept detection values. The semantic concept detectors and the joint likelihood model are trained together with a joint training process using training audio signals, at least some of which are known to be associated with a plurality of semantic concepts.

Provided are: a data related information generating unit for generating data related information DL including detection data and air conditioner information of air conditioners; a class classifying unit for creating a plurality of classes on the basis of the air conditioner information related to alarm data extracted by an alarm data extracting unit among the air conditioner information and for classifying the data related information DL into the plurality of classes; a priority setting unit for setting priority to each of a plurality of types of alarm data and the plurality of classes; and a priority calculating unit for assessing co-occurrence of anomaly data extracted by an anomaly data extracting unit and the alarm data, assessing co-occurrence of the alarm data and the plurality of classes, assigning priority about the alarm data and the plurality of classes to the co-occurred anomaly data, and calculating priority of anomaly data.

A method for clustering multi-dimensional data streams includes: when data elements are input, determining 1-D subclusters and assigning identifiers to the determined 1-D subclusters; (b) generating a matching set that is a set of identifiers of the 1-D subclusters where each dimensional value of the data elements belongs to the range of the 1-D subclusters of the corresponding dimensions; and (c) determining subclusters by finding a set of frequently co-occurring 1-D subclusters among a set of 1-D subclusters that belong to the generated matching set. With the present invention, the processing time required to find the subclusters can be improved and the performance of the memory is further improved.

The invention discloses a method utilizing Chinese online resources for supervising extraction of character relations remotely. According to the method, at first, an online encyclopedia website, formed through a semi-manual mode, on a web is utilized for automatically constructing a knowledge base so as to obtain accurate relation types comprehensive as much as possible, and examples of the character relations; then co-occurring names and context features are extracted from a text corpus, and the names and the relation examples in the knowledge base are matched to obtain name pair sets of the marked relations and name pair sets of unmarked relations; finally, a label propagation algorithm is introduced to achieve relation match of unmarked name pairs, so that extraction of the character relations is achieved. According to the method, the knowledge base of the character relations can be automatically constructed, and the richer and more accurate relation types are included; based on the knowledge base, the label propagation algorithm is introduced to supervise extraction of the character relations remotely, and therefore accuracy of results of the extracted relations can be ensured.

Methods, systems, and articles of manufacture consistent with certain principles related to the present invention enable a computing system to perform hierarchical topical clustering of text data based on statistical modeling of co-occurrences of (document, word) pairs. The computing system may be configured to receive a collection of documents, each document including a plurality of words, and perform a modified deterministic annealing Expectation-Maximization (EM) process on the collection to produce a softly assigned hierarchy of nodes. The process may involve assigning documents and document fragments to multiple nodes in the hierarchy based on words included in the documents, such that a document may be assigned to any ancestor node included in the hierarchy, thus eliminating the hard assignment of documents in the hierarchy.

The embodiment of the invention provides a comment information marking method and a comment information marking device. The method comprises the following steps of: obtaining data of a plurality of event comments; performing statement division on each event comment, and using each divided statement as a comment viewpoint; extracting sentiment words from each comment viewpoint; associating the sentiment words co-occurring in any one comment viewpoint and building a sentiment word community network; and marking data with the sentiment property for the sentiment word affiliated comment viewpoint in any one sentiment word community in the sentiment word community network, wherein the sentiment property is positive, negative or neutral, and the sentiment word community comprises a group of directly or indirectly associated sentiment words. The comment information marking method and the comment information marking device provided by the embodiment of the invention have the advantages that the event comments can be automatically subjected to sentiment clustering; and the sentiment properties can be marked for the event comments.