Abstract: According to one embodiment, a document processing apparatus includes an acquisition unit, an extractor, and a detector. The acquisition unit acquires a first document and a second document different from the first document. The extractor extracts a first structure representing a word dependence of structural elements in the first document and extracts a second structure representing a word dependence of structural elements in the second document. The detector detects a difference for each structural element from the first structure and the second structure.

Abstract: According to an embodiment, a material recommendation apparatus includes an extractor, a creator, a matcher and a recommender. The extractor extracts information about a substance as a candidate for a material for an industrial product, a property of the substance, and a report time of the property from an electronic document. The creator creates, for each substance, time series data in which the property is associated with the report time. The matcher matches the time series data with a pattern. The recommender recommends, as the material, a substance corresponding to time series data that matches the pattern.

Abstract: According to one embodiment, a data connection device includes a first log storage, a second log storage, and a hardware processor. The first log storage is configured to store a plurality of first logs indicating a first behavior of a first user. The second log storage is configured to store a plurality of second logs which are independent of the plurality of first logs and indicate a second behavior of a second user. The hardware processor is configured to acquire a set of first logs indicating a specific first behavior, from the plurality of first logs, acquire a set of second logs having a feature in common with the set of first logs, and connect the set of first logs with the set of second logs.

Abstract: According to one embodiment, a content evaluating device includes a first storage, a second storage and a processor. The first storage is configured to store a first viewing log indicating a device with which the first content has been viewed in the first region and a second viewing log indicating a device with which second content has been viewed in the first region. The second storage is configured to store evaluation information including an evaluation value that represents an evaluation of the second content in the second region. The processor is configured to calculate a predicted evaluation value of the first content in the second region in accordance with the first and second viewing logs and the evaluation value included in the evaluation information.

Abstract: According to one embodiment, a data connection device includes a first log storage, a second log storage, and a hardware processor. The first log storage is configured to store a plurality of first logs indicating a first behavior of a first user. The second log storage is configured to store a plurality of second logs which are independent of the plurality of first logs and indicate a second behavior of a second user. The hardware processor is configured to acquire a set of first logs indicating a specific first behavior, from the plurality of first logs, acquire a set of second logs having a feature in common with the set of first logs, and connect the set of first logs with the set of second logs.

Abstract: According to an embodiment, a material recommendation apparatus includes an extractor, a creator, a matcher and a recommender. The extractor extracts information about a substance as a candidate for a material for an industrial product, a property of the substance, and a report time of the property from an electronic document. The creator creates, for each substance, time series data in which the property is associated with the report time. The matcher matches the time series data with a pattern. The recommender recommends, as the material, a substance corresponding to time series data that matches the pattern.

Abstract: According to one embodiment, a document processing apparatus includes an acquisition unit, an extractor, and a detector. The acquisition unit acquires a first document and a second document different from the first document. The extractor extracts a first structure representing a word dependence of structural elements in the first document and extracts a second structure representing a word dependence of structural elements in the second document. The detector detects a difference for each structural element from the first structure and the second structure.

Abstract: According to one embodiment, a program extraction apparatus includes an acquirer, a setting unit, a first calculator and an extractor. The acquirer acquires a viewing history including information on at least one program viewed by each of users, and an area code indicating a location of the each of users. The setting unit sets a range of a target area by using the area code. The first calculator calculates, for each program included in the viewing history of at least one user belonging to the target area, a viewing amount indicating how much the program was viewed to obtain a local area viewing amount. The extractor extracts at least one program by referring to the local area viewing amount.

Abstract: A device for speech input includes a speech input unit configured to convert a speech of a user to a speech signal; an angle detection unit configured to detect an angle of the speech input unit; a distance detection unit configured to detect a distance between the speech input unit and the user; and an input switch unit configured to control on and off of the speech input unit based on the angle and the distance.

Abstract: A viewing behavior learning apparatus includes: a viewing history acquiring unit that acquires a viewing record that indicates an attribute of a program and start and end times during which a viewer viewed the program; a viewing history dividing unit that divides the viewing record every discretized time points that is discretized by a unit time; a viewing history replicating unit that discretizes a viewing time period denoted by the divided viewing records by interval shorter than the unit time to obtain discretized viewing time periods; a viewing behavior storage unit that builds a model of the viewing behavior by use of a Bayesian network having the viewing time period and the attribute as random variables and stores a conditional probability table of the Bayesian network; and a viewing behavior updating unit that updates the conditional probability table using the discretized viewing time periods.

Abstract: A viewing recommendation apparatus includes an acquisition unit configured to acquire first broadcast program information for each of first broadcast programs to be broadcasted, a storage unit configured to store a plurality of previously broadcasted second broadcast programs in correspondence with second broadcast program information, a calculation unit configured to calculate an urgency in accordance with the first broadcast program information and the second broadcast program information to obtain a plurality of urgencies, the urgency indicating a degree to view a broadcast program earlier, and a generation unit configured to generate a recommendation list of programs to be viewed based on levels of the urgencies.

Abstract: A device for speech input includes a speech input unit configured to convert a speech of a user to a speech signal; an angle detection unit configured to detect an angle of the speech input unit; a distance detection unit configured to detect a distance between the speech input unit and the user; and an input switch unit configured to control on and off of the speech input unit based on the angle and the distance.

Abstract: A behavior prediction apparatus includes an input unit inputting sole behaviors of a human and simultaneous occurrence probability of behaviors, a behavior discrimination unit specifying a behavior pattern based on correspondence between behaviors corresponding to simultaneous occurrence probability and actual behaviors, an information generation/recording unit generating behavior history information of the specified behavior pattern, and to generate and record information of each of a behavior transition probability, a behavior time and a behavior occurrence probability based on the behavior history information, a behavior prediction unit obtaining a starting behavior from the behavior occurrence probability, select another behavior pattern in order of behavior occurrence probability, and add the behavior time to the selected behavior pattern to output behavior prediction information, and a social value calculation unit obtaining a social value per unit time using the behavior prediction information, social

Abstract: According to an aspect of the present invention, there is provided with a behavior determining apparatus including: an elemental-behavior extracting unit configured to extract, from trajectory data in which positions of a traveling body in an observation area are held at predetermined time intervals, L (L is an integer equal to or larger than 1) elemental behavior quantities among a stopping time, a stop frequency, a walking time, a running time, a direction change frequency, a sum of angle change quantities at the times when the traveling body changes a traveling direction, a sum of absolute values of angle change quantities at the times when the traveling body changes a traveling direction, a gravitational center set vector, and a pre-stop average speed; and a behavior determining unit configured to determine a behavior of the traveling body from the L elemental behavior quantities.

Abstract: Sets of integrated data including history data and time-invariant data grouped for each analysis target are classified based on an inclusion between an amount of change of a time-varying item included in sets of integrated data and a numerical range expressed by an event sequence and also based on a common time-invariant item, to generate a prediction model in which a prediction-target event sequence expressing an amount of change of the event item included in each set of integrated data after being classified and an amount of time required for reaching the amount of change is associated with the event sequence together with a classification condition related to the classification.

Abstract: An apparatus includes: a module configured to extract category information and program abstracts of programs contained in an electronic program guide, extract program-specific terms from the program abstracts by morphological analysis and combine the category information and the program-specific terms to generate category-added terms; a module configured to analyze a history of programs viewed by a user based on the generated category-added terms to generate a preference vector indicating user's preferences for programs; a module analyzing the program abstracts based on the category-added terms to generate broadcast program vectors; a module generating a relevant term model for the category-added terms; a module calculating similarities between the preference vector and each of the broadcast program vectors based on the generated relevant term model; and a module outputting programs having the calculated similarities satisfying a predetermined condition as recommended programs matching with the user's prefere

Abstract: A device for prompting behavioral changes generates a probability classification model in consideration of a probability dependence relation among nodes equivalent to each item (attribute) of health examination and doctor's question result data. The device tentatively changes an attribute value of each node of the classification model in an improvement direction, checks changes in classification class node upon tentatively changing, and generates higher-ranking N-pieces of improved plans as plan candidates in descending order of improvements of probabilities of the classification class nodes from the least improvement. The device generates an improved plan portfolio by listing the plan candidates in descending order of easiness of improvements. In response to changes in personal preference and restriction data, the device executes again the generation of the improved plan portfolio, and generates to present an improved plan portfolio after changing.

Abstract: A viewing behavior learning apparatus includes: a viewing history acquiring unit that acquires a viewing record that indicates an attribute of a program and start and end times during which a viewer viewed the program; a viewing history dividing unit that divides the viewing record every discretized time points that is discretized by a unit time; a viewing history replicating unit that discretizes a viewing time period denoted by the divided viewing records by interval shorter than the unit time to obtain discretized viewing time periods; a viewing behavior storage unit that builds a model of the viewing behavior by use of a Bayesian network having the viewing time period and the attribute as random variables and stores a conditional probability table of the Bayesian network; and a viewing behavior updating unit that updates the conditional probability table using the discretized viewing time periods.

Abstract: There is provided with a program searching apparatus, including: an extracting unit extracting words or phrases described in plural program information as keywords; an identifying unit identifying categories to which the keywords belongs; a first calculating unit calculating a number of program information containing the keywords as first information; a second calculating unit calculating a number of keywords that belong to the categories as second information; a specifying unit specifying one program as a search query; a weight calculating unit calculating, for each of query keywords extracted from program information of the search query, a weight based on the first and second information; a similarity calculating unit calculating a similarity level to the search query with respect to a search target program according to the weight corresponding to a query keyword included in the program information of the search target program.

Abstract: A time-series data classifying apparatus may include a first database, a peak feature extracting unit, a second database, a data input unit, and a predicting unit. The first database stores a plurality of cases each including time-series data a classification label. The peak feature extracting unit may, for each of the cases, calculate intersection points of time-series data expanded in a coordinate system and each reference line, detect a peak point in each of sections formed between two intersection points being adjacent to generate a peak feature sequence that contains a sequence of detected peak points. The second database may store each peak feature sequence in association with a classification label of each of the cases. The data input unit may input target time-series data. The predicting unit may predict a classification label to be assigned to the target time-series data based on the second database.