75 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. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors, thus facilitating the targeting of promotional offers to consumers most likely to respond positively.

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.

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.

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 search engine process predicts the correct spellings of search terms within multiple-term search queries. In one embodiment, when a user submits a multiple-term search query that includes a non-matching term and at least one matching term, a table is accessed to look up a set of terms that are “related” to the matching term or terms. A spelling comparison function is then used to determine whether any of these related terms is sufficiently similar in spelling to the non-matching term to be deemed a candidate correctly-spelled replacement. A candidate replacement term may automatically be substituted for the non-matching term, or may be suggested to the user as a replacement. The invention also includes a process for identifying terms that are related to each other based on the relatively high frequencies with which they co-occur within search queries of users, database records, and / or specific database fields.

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.

A computer-implemented service recommends digital works (and / or creators of 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 (and / or work creators) that are similar 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 are used to provide recommendations to users.

A method of identifying groups of related digital images in a digital image collection, comprising: analyzing each of the digital images to generate associated feature descriptors related to image content or image capture conditions; storing the feature descriptors associated with the digital images in a metadata database; automatically analyzing the metadata database to identify a plurality of frequent itemsets, wherein each of the frequent itemsets is a co-occurring feature descriptor group that occurs in at least a predefined fraction of the digital images; determining a probability of occurrence for each the identified frequent itemsets; determining a quality score for each of the identified frequent itemsets responsive to the determined probability of occurrence; ranking the frequent itemsets based at least on the determined quality scores; and identifying one or more groups of related digital images corresponding to one or more of the top ranked frequent itemsets.

A related word presentation device (100) for performing a proper search to prevent an omission comprises: a broadcast program information storage section (101) for storing broadcast program information (101a) for each broadcast program; an information dividing section (103a) for generating at least one or more groups with a reference word which is a word belonging to the attributes of the words included in the broadcast program information (101a) and a set of words co-occurring within the broadcast programs as a unit for each of the attributes of the words included therein; a relation degree calculating section (103b) for storing attribute-specific association dictionaries (102a, 102b, 102c) of each of the attributes which have the relation degree between the two words calculated depending on the frequency at which the two words in each group of each of the attributes of the words co-occur and the two words in an association dictionary storage section (102); a search condition acquiring section (104) for acquiring a search word and its attribute, an alternate word acquiring section (105) for selecting an alternate word from an attribute-specific association dictionary corresponding to the acquired attribute; and an output section (106) for presenting the selected alternate word.