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: 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: 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: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

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 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: 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: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: Embodiments of the invention provide for the correlation of violating change sets during regression testing of a computer program. A method of the invention includes annotating a test case with a reference to logical operations of different programmatic objects of a computer program. Thereafter, change sets are applied to the program and the test case is executed by a development environment such as a debugger to a point of failure. It is then determined from the annotations change sets related to the logical operations and different ones of the determined change sets are sequentially replaced and the test case repeatedly re-executed. As such, the ones of the replaced change sets resulting in failure from re-execution of the test case are determined to be violating change sets.

Abstract: A method of operation of a navigation system comprising: determining a node including a node attribute for a maneuver along a path; determining a node presentation based on the node attribute including a node proximity meeting or exceeding a node relevance threshold; generating a route bar including the node based on the node presentation, the node proximity, or a combination thereof; determining a safe location with a control unit for displaying the route bar based on a current surrounding for presenting on a device.

Abstract: Various aspects and embodiments of dwelling automation administration are described. Among other aspects or features of the embodiments, a dwelling automation system in a computing device authenticates a user. A management interface in the dwelling automation system generates a user interface for administering a plurality of automation devices at different dwellings based on access and control rights of the user. The user interface can grant the user access to at least one automation device associated with a hub associated with at least one dwelling.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of the natural language content is generated by identifying latent logical terms 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 associated with 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. The propagating of the at least one knowledge value generates transaction records in a transaction knowledgebase data structure. A reasoning operation is executed based on the transaction knowledgebase data structure.

Abstract: Mechanisms for clarifying an input question are provided. A question is received for generation of an answer. A set of candidate answers is generated based on an analysis of a corpus of information. Each candidate answer has an evidence passage supporting the candidate answer. Based on the set of candidate answers, a determination is made as to whether clarification of the question is required. In response to a determination that clarification of the question is required, a request is sent for user input to clarify the question. User input is received from the computing device in response to the request and at least one candidate answer in the set of candidate answers is selected as an answer for the question based on the user input.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of the natural language content is generated by identifying latent logical terms 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 associated with 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. The propagating of the at least one knowledge value generates transaction records in a transaction knowledgebase data structure. A reasoning operation is executed based on the transaction knowledgebase data structure.

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: Systems and methods to determine the status of transmitted audio packets. The audio packets can include digital components that are presented by a client computing device. Based on presentation variables and determinations of presentation status, the system can generate a predictive model that can be used to dynamically determines presentation values of audio packets prior to their transmission to a client computing device.

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: 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.