Abstract: A computer-implemented wardrobe packing method, system, and a computer program product, include selecting a plurality of items stored in a memory to pack into a container, generating a packing order of the plurality of items based on packing preference data and item data stored in the memory, creating and displaying a video of step-by-step packing instructions for the plurality of items in the packing order, monitoring and comparing an actual packing process to a generated packing order, and controlling the displaying of a step of the step-by-step packing instructions, in response to the monitoring and comparing.

Abstract: A method augments a video with snippets. The method transmits, to a video reviewer, a request for one or more snippets created by the video reviewer for a video, where the one or more snippets provide additional information about one or more video passages in the video. The method receives a response to the request, which includes the one or more snippets; retrieves a video reviewer profile for the video reviewer; receives a request for the video from a viewer; and compares the video reviewer profile to a viewer profile of the viewer. In response to the viewer profile matching the video reviewer profile within a predetermined range of similarities, the method augments the video with the one or more snippets to create an augmented video, and transmits the augmented video to the viewer.

Abstract: A computer-implemented method overlays an alert over a pre-recorded video regarding an upcoming segment of the pre-recorded video. One or more processors receive a pre-recorded video that is directed to a main topic, and then partition the pre-recorded video into sub-topic segments, where each sub-topic segment is about a sub-topic of the main topic. The processor(s) identify previous entities' responses while previous entities viewed precursory sub-topic segments from the pre-recorded video, where the precursory sub-topic segments were presented before an upcoming sub-topic segment. The processor(s) receive a current entity's responses that occurred while a current entity viewed one or more of the precursory sub-topic segments from the pre-recorded video, then compare the current entity's responses to the previous entities' responses.

Abstract: A method augments a video with snippets. The method transmits, to a video reviewer, a request for one or more snippets created by the video reviewer for a video, where the one or more snippets provide additional information about one or more video passages in the video. The method receives a response to the request, which includes the one or more snippets; retrieves a video reviewer profile for the video reviewer; receives a request for the video from a viewer; and compares the video reviewer profile to a viewer profile of the viewer. In response to the viewer profile matching the video reviewer profile within a predetermined range of similarities, the method augments the video with the one or more snippets to create an augmented video, and transmits the augmented video to the viewer.

Abstract: A video augmenting computer augments a video with snippet information. The video augmenting computer transmits, to a first reviewing computer, a video and a request for one or more snippets created by a video reviewer, where the one or more snippets provide additional information about one or more video passages in the video; receives a response to the request, where the response comprises the one or more snippets; retrieves a video reviewer profile for the video reviewer; receives a request from a viewer to view the video; and compares the video reviewer profile to a viewer profile of the viewer. In response to the viewer profile matching the video reviewer profile within a predetermined range of similarities, the video augmenting computer augments the video with the one or more snippets to create an augmented video and transmits the augmented video to a client computer associated with the viewer.

Abstract: A computer-implemented method overlays an alert over a pre-recorded video regarding an upcoming segment of the pre-recorded video. One or more processors receive a pre-recorded video that is directed to a main topic, and then partition the pre-recorded video into sub-topic segments, where each sub-topic segment is about a sub-topic of the main topic. The processor(s) identify previous entities' responses while previous entities viewed precursory sub-topic segments from the pre-recorded video, where the precursory sub-topic segments were presented before an upcoming sub-topic segment. The processor(s) receive a current entity's responses that occurred while a current entity viewed one or more of the precursory sub-topic segments from the pre-recorded video, then compare the current entity's responses to the previous entities' responses.

Abstract: A system executes a particular response for a particular sub-topic segment in a pre-recorded video. One or more processors receive a pre-recorded video, where the pre-recorded video is directed to a main topic, and then partition the pre-recorded video into multiple sub-topic segments, where each sub-topic segment is about a sub-topic of the main topic. The processor(s) identify previous entities' responses to sub-topic segments from the pre-recorded video. The processor(s) then compare a current entity's responses to the previous entities' responses that occurred while one or more precursory sub-topic segments are presented before a particular sub-topic segment in the pre-recorded video. In response to a match, the particular response is executed on behalf of the current entity in response to the one or more precursory sub-topic segments being presented to the current entity.

Abstract: A video augmenting computer augments a video with snippet information. The video augmenting computer transmits, to a first reviewing computer, a video and a request for one or more snippets created by a video reviewer, where the one or more snippets provide additional information about one or more video passages in the video; receives a response to the request, where the response comprises the one or more snippets; retrieves a video reviewer profile for the video reviewer; receives a request from a viewer to view the video; and compares the video reviewer profile to a viewer profile of the viewer. In response to the viewer profile matching the video reviewer profile within a predetermined range of similarities, the video augmenting computer augments the video with the one or more snippets to create an augmented video and transmits the augmented video to a client computer associated with the viewer.

Abstract: A video augmenting computer augments a video with snippet information. The video augmenting computer transmits requests to multiple remote video reviewing computers for a video and a snippet to augment the video. The snippet provides additional information about a particular passage from the video, and each of the requests includes a request for a profile of a reviewer of the video who created the snippet. The video augmenting computer receives the video and multiple versions of the snippet that were created by different reviewers of the video, and then compares profiles of the different reviewers to a profile of a new viewer of the video. In response to the profile of the new viewer matching a profile of a particular reviewer from the different reviewers of the video, the video augmenting computer augments the video with the snippet that was created by the particular reviewer to create an augmented video.

Abstract: A video augmenting computer augments a video with snippet information. The video augmenting computer transmits requests to multiple remote video reviewing computers for a video and a snippet to augment the video. The snippet provides additional information about a particular passage from the video, and each of the requests includes a request for a profile of a reviewer of the video who created the snippet. The video augmenting computer receives the video and multiple versions of the snippet that were created by different reviewers of the video, and then compares profiles of the different reviewers to a profile of a new viewer of the video. In response to the profile of the new viewer matching a profile of a particular reviewer from the different reviewers of the video, the video augmenting computer augments the video with the snippet that was created by the particular reviewer to create an augmented video.

Abstract: A system executes a particular response for a particular sub-topic segment in a pre-recorded video. One or more processors receive a pre-recorded video, where the pre-recorded video is directed to a main topic, and then partition the pre-recorded video into multiple sub-topic segments, where each sub-topic segment is about a sub-topic of the main topic. The processor(s) identify previous entities' responses to sub-topic segments from the pre-recorded video. The processor(s) then compare a current entity's responses to the previous entities' responses that occurred while one or more precursory sub-topic segments are presented before a particular sub-topic segment in the pre-recorded video. In response to a match, the particular response is executed on behalf of the current entity in response to the one or more precursory sub-topic segments being presented to the current entity.

Abstract: Methods, systems, and computer program products for automatically identifying surprising travel events and/or points of interest for a user are provided herein.

Abstract: A cognitive system that automatically assesses subjective answers may be provided. A cognitive engine executing on one or more processors may determine whether a statement parsed from a subjective answer by natural language processing technique is accurate or inaccurate, for each of the plurality of statements based on matching the statement with information associated with a domain of a question from a plurality of data sources, according to an accuracy threshold. An overall assessment of the answer may be automatically determined based on a number of statements determined to be accurate, a number of statements determined to be inaccurate, a number of duplicate statements in the answer relative to a total number of statements in the answer. A visual graphics representing accurate and inaccurate statements may be presented or displayed, allowing a user to interact with the visual graphics to modify the assessment.

Abstract: A cognitive system that automatically assesses subjective answers may be provided. A cognitive engine executing on one or more processors may determine whether a statement parsed from a subjective answer by natural language processing technique is accurate or inaccurate, for each of the plurality of statements based on matching the statement with information associated with a domain of a question from a plurality of data sources, according to an accuracy threshold. An overall assessment of the answer may be automatically determined based on a number of statements determined to be accurate, a number of statements determined to be inaccurate, a number of duplicate statements in the answer relative to a total number of statements in the answer. A visual graphics representing accurate and inaccurate statements may be presented or displayed, allowing a user to interact with the visual graphics to modify the assessment.

Abstract: Methods and systems for creating an action plan for a service delivery system comprising a plurality of operational key performance indicators (KPIs). The system receives predetermined information about at least one relationship between two or more of the KPIs, and weights that information based on its source. The system also receives information about operational constraints and desired outcomes. A KPI relationship map is created using both the weighted information and information about KPI relationships within the service delivery system. Future predictions are made based on the KPI relationship map, and the predictions and map are utilized to create an action plan to achieve the desired outcomes in light of the operational constraints.

Abstract: A system and method of determining operational quality aware staffing requirements in service delivery systems. Optimum staffing requirements are determined by workload based simulation and optimization. Operational quality metrics are periodically measured against benchmarks to determine quality scores based upon the level of performance. The staffing requirements and quality scores are used to perform a population distribution and correlation analysis to devise an operational quality to staffing relationship.

Abstract: Methods and apparatus are provided for recommending one or more existing notes related to a current analytic activity of a user. One or more existing notes related to a current analytic activity of a user are recommended by maintaining a logical record of analytic activity of the user by recording one or more visual analytic actions performed by a user; generating a context model for a plurality of the existing notes, wherein the context model for a given existing note represents information interests of the user; determining a relevance score for each of the plurality of existing notes, wherein a given relevance score characterizes a relevance of a corresponding existing note to the current analytic activity; and recommending one or more existing notes based on the determined relevance scores. The context model for the given existing note represents the information interests of the user at a time surrounding the point when the user recorded the corresponding existing note.

Abstract: Methods and apparatus are provided for recommending one or more concepts related to a current analytic activity of a user. One or more concepts related to a current analytic activity of a user are recommended by maintaining a logical record of analytic activity of the user by recording one or more visual analytic actions performed by a user; generating a context model for a plurality of the existing notes containing the concepts, wherein the context model for a given existing note represents information interests of the user; determining a weight for each of the plurality of concepts, wherein a given weight characterizes a relevance of a corresponding concept to the current analytic activity; and recommending one or more concepts based on the determined weight. The weight for a given concept is based on the context model for the given concept and a context model for the current analytic activity.