Abstract: Apparatus and methods are provided for enabling a plurality of applications running on a user device or in communication therewith to share data. In one exemplary embodiment, a single user device is configured to run a plurality of heath-monitoring applications which collect data from a respective plurality of health-monitoring devices and/or via user entry. According to the present disclosure, once the applications are linked, the user accesses, views, and analyzes the plurality of health-related data from the plurality of applications at a single application. Moreover, once the applications are linked, the user may sign-in to one application and be automatically signed into the other applications. In this manner, the user's activity and updated information entered, sensed, or otherwise collected into or by one application may be accessible at the other applications for analysis and display therein as well.

Abstract: A semantic search engine is enhanced to employ user preferences to customize answer output by, for a first user, extracting user preferences and sentiment levels associated with a first question; receiving candidate answer results of a semantic search of the first question; weighting the candidate answer results according to the sentiment levels for each of the user preferences; and producing the selected candidate answers to the first user. Optionally, user preferences and sentiment levels may be accumulated over different questions for the same user, or over different users for similar questions. And, supplemental information may be retrieved relative to a user preference in order to further tune the weighting per the preferences and sentiment levels.

Abstract: There are provided a system, a method and a computer program product for suggesting content to a group. The system identifies a group of users in a social network. The system receives inputs associated with the social network group. The system aggregates the received inputs. The system analyzes the aggregated inputs. The system searches, based on the aggregation and the analysis, one or more tailored content to be delivered to the group in Internet, one or more database and one or more data warehouse. The system delivers the tailored content to the group in the social network.

Abstract: There are provided a system, a method and a computer program product for creating the social group whose participants are involved in an online conversation. The system aggregates data associated with the online conversation and a group profile, of the social group. The system determines, based on the online conversation data and the group profile, a context of the online conversation and a goal of the social group. The system receives, based on the determined context and the goal, a first bidding from each provider, the first bidding associated with the determined context and the goal, the each provider providing one or more of: goods, services or discounts associated with the context and the goal.

Abstract: Software that preserves information provided by a user in a first application utilizing a first interaction mode for use by a second application utilizing a second interaction mode, by performing the following steps: (i) generating a natural language log describing an interaction between a user and a first application, where the user interacts with the first application utilizing a first interaction mode; (ii) analyzing the natural language log using natural language processing to determine first user data; and (iii) utilizing the first user data by a second application, where the user interacts with the second application utilizing a second interaction mode different from the first interaction mode, and where the second application does not receive the first user data from the user via the second interaction mode.

Abstract: Software that preserves information provided by a user in a first application utilizing a first interaction mode for use by a second application utilizing a second interaction mode, by performing the following steps: (i) generating a natural language log describing an interaction between a user and a first application, where the user interacts with the first application utilizing a first interaction mode; (ii) analyzing the natural language log using natural language processing to determine first user data; and (iii) utilizing the first user data by a second application, where the user interacts with the second application utilizing a second interaction mode different from the first interaction mode, and where the second application does not receive the first user data from the user via the second interaction mode.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. Natural language content is received, upon which a reasoning operation is to be performed. A first parse representation of the natural language content is generated, by a parser, by performing natural language processing on the natural language content. A logical parse of the first parse is generated by identifying latent logical operators within the first parse indicative of logical relationships between elements of the natural language content. A reasoning operation on the logical parse is executed to generate a knowledge output indicative of knowledge associated with one or more of the logical relationships between elements of the natural language content.

Abstract: An approach is provided for evaluating a potential post based on historical data. In the approach, historically highly rated attributes that to previously received highly rated posts are identified. The process receives a potential post from an online Subject Matter Expert (SME). The process analyzes the potential post, using a Natural Language Processing (NLP) routine performed by computer processors. The analysis identifies a lack of one or more of the historically highly rated attributes in the potential post. The process then notifies the SME, based on the analysis, regarding the lack of one or more of the historically highly rated attributes in the potential post.

Abstract: Dynamically recommending changes to an association between an operating system image and an update group includes monitoring a configuration of a deployed copy of a first master operating system (OS) image; detecting a modification in the configuration of the deployed copy; determining that the configuration of the deployed copy with the modification more closely matches a configuration of a second master OS image than a configuration of the first master OS image; in response to determining that the configuration of the deployed copy with the modification more closely matches the configuration of the second master OS image, generating an association recommendation that recommends associating the deployed copy with a second update group of the second master OS image; and associating the deployed copy with the second update group of the second master OS image instead of the first update group of the first master OS image.

Abstract: A recommendation engine for works of literature uses patterns of flow and element similarities for scoring a first user-rated work of literature against one or more recommendation candidate works of literature. Cluster models are created using meta-data modeling the works of literature, the meta-data having literary element categories and instances within each category. Each instance is described by an index value (position in the literature) and significance value (e.g. weight or significance). Cluster finding process(es) invoked for each instance in each category find Similarity Concept clusters and Consistency Trend clusters, which are recorded into the cluster models representing each work of literature. The cluster model can be printed or displayed so that a user can visually understand the ebb and flow of each literary element in the literature, and may be digitally compared to other cluster models of other works of literature for potential recommendation to a user.

Abstract: Systems and methods are disclosed for automatically classifying images of pages of a source, such as a book, into classifications such as front cover, copyright page, table of contents, text, index, etc. In one embodiment, three phases are provided in the classification process. During a first phase of the classification process, a first classifier may be used to determine a preliminary classification of a page image based on single-page criteria. During a second phase of the classification process, a second classifier may be used to determine a final classification for the page image based on multiple-page and/or global criteria. During an optional third phase of classification, a verifier may be used to verify the final classification of the page image based on verification criteria. If automatic classification fails, the page image may be passed on to a human operator for manual classification.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: Visualizable subject matter contained within data is detected. A context in which the visualizable subject matter appears is determined. A visualization engine of a plurality of visualization engines is selected based, at least in part, on the context in which the visualizable subject matter appears. The visualization engine is configured to generate a visualization of the visualizable subject matter.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of a natural language content is generated by identifying latent logical operators within the first parse indicative of logical relationships between elements of the natural language content. The logical parse comprises nodes and edges linking nodes. At least one knowledge value is associated with each node in the logical parse. The at least one knowledge value of at least a subset of the nodes in the logical parse is propagated to one or more other nodes in the logical parse based on propagation rules. A reasoning operation is performed on the logical parse to generate a knowledge output indicative of knowledge associated with one or more of the logical relationships between elements of the natural language content.

Abstract: Mechanisms are provided for implementing a logical reasoning and justification engine that operates to receive a logical parse data structure of natural language content. The logical parse data structure comprises nodes and edges linking nodes and identifies latent logical terms within the natural language content indicative of logical relationships between elements of the natural language content. The engine further operates to receive a selection of a node in the logical parse data structure to thereby form a selected node, and execute at least one logical justification module on the selected node to identify zero or more justifying nodes that provide a contribution to a knowledge state of the selected node. The engine further operates to generate a logical justification output based on the identified zero or more justifying nodes, and output the logical justification output.

Abstract: A visualization of literary elements of a work of literature, such as a novel or short story, is generated from meta-data records representing a digital work of literature including literary elements (humor, drama, adventure, etc.), characters, and plot devices related to a position within the work of literature where each appears. A significance level is determined for each of the elements, characters and plot devices at each position within the work of literature, and these are plotted into a sequential graph having position (e.g. timeline) axis and a significance level axis. The sequential graph is then output for printing or display. Human-generated and machine-generated meta-data may be ingested equally well by the method. Colors, line thickness, and a broken line patterns may be employed for greater visual meaning. And, the sequential graph may be annotated according to segments (e.g. chapters, sections) and dominant genre within each segment.

Abstract: A visualization of literary elements of a work of literature, such as a novel or short story, is generated from meta-data records representing a digital work of literature including literary elements (humor, drama, adventure, etc.), characters, and plot devices related to a position within the work of literature where each appears. A significance level is determined for each of the elements, characters and plot devices at each position within the work of literature, and these are plotted into a sequential graph having position (e.g. timeline) axis and a significance level axis. The sequential graph is then output for printing or display. Human-generated and machine-generated meta-data may be ingested equally well by the method. Colors, line thickness, and a broken line patterns may be employed for greater visual meaning. And, the sequential graph may be annotated according to segments (e.g. chapters, sections) and dominant genre within each segment.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: A visualization of literary elements of a work of literature, such as a novel or short story, is generated from meta-data records representing a digital work of literature including literary elements (humor, drama, adventure, etc.), characters, and plot devices related to a position within the work of literature where each appears. A significance level is determined for each of the elements, characters and plot devices at each position within the work of literature, and these are plotted into a sequential graph having position (e.g. timeline) axis and a significance level axis. The sequential graph is then output for printing or display. Human-generated and machine-generated meta-data may be ingested equally well by the method. Colors, line thickness, and a broken line patterns may be employed for greater visual meaning. And, the sequential graph may be annotated according to segments (e.g. chapters, sections) and dominant genre within each segment.