Abstract: According to one embodiment of the present invention, a computer implemented method, apparatus, and computer-usable program product for generating unique cohort groups using multimodal sensory device. Multimodal sensory data is received from a set of multimodal sensors in a public environment. The set of multimodal sensors are associated with a network. The multimodal sensory data is received from the set of multimodal sensors over the network. The multimodal sensory data is processed to generate a plurality of attributes to form cohort attributes. A plurality of unique cohort groups is generated using the cohort attributes and the multimodal sensory data. Each member of the cohort group shares at least one common attribute.

Abstract: A computer implemented method, apparatus, and computer-usable program product for identifying marketing trends in unique cohort groups. Information describing a plurality of unique cohort groups associated with a public environment is retrieved. Each member of a cohort group in the plurality of unique cohort groups shares at least one common attribute. Sets of attributes associated with the plurality of unique cohort groups are identified. The sets of attributes are analyzed by a cohort trend detection engine to identify attribute trends associated with the sets of attributes and a frequency of occurrence of the attribute trends in the plurality of unique cohort groups to form current attribute trends. In response to a query to an inference engine requesting inferences associated with the marketing trends, inferences describing future occurrences of the attributes in the plurality of cohort groups are received to form a set of future attribute trends.

Abstract: A computer implemented method, apparatus, and computer program product for selection of meal plans. In one embodiment, a set of prospective guests are identified from at least one of a set of sensors collecting historical attendance data and a calendaring application. A set of nutritional requirements is then identified for the set of prospective guests. Thereafter, a set of meal plans is selected on an availability of ingredients and the nutritional requirements of the set of prospective guests, wherein the availability of ingredients is determined by sensors from the set of sensors monitoring the ingredients.

Abstract: According to one embodiment of the present invention, a computer implemented method, apparatus, and computer usable program product is provided for detecting behavioral deviations in members of a cohort group. A member of a cohort group is identified. Each member of the cohort group shares a common characteristic. Ocular metadata associated with the member of the cohort group is generated in real-time. The ocular metadata describes movements of an eye of the member of the cohort group. The ocular metadata is analyzed to identify patterns of ocular movements. In response to the patterns of ocular movements indicating behavioral deviations in the member of the cohort group, the member of the cohort group is identified as a person of interest. A person of interest may be subjected to an increased level of monitoring and/or other security measures.

Abstract: A computer implemented method, apparatus, and computer-usable program product for validating expected cohort behavior. In one embodiment, sensory data gathered by a set of multimodal sensor devices is processed to form a set of actual cohort behavior data. The sensory data comprises information associated with a cohort group. Each member of the cohort group shares at least one common attribute. The set of actual cohort behavior data is compared to a set of predicted cohort behavior models. The set of actual cohort behavior data comprises information describing actual behavior by members of the cohort group. The set of predicted cohort behavior models comprises information describing an expected behavior of members of the cohort group. A comparison result is generated. The comparison result indicates an accuracy of the set of predicted cohort behavior models.

Abstract: A computer implemented method, apparatus, and computer usable program code for detecting behavioral deviations in members of a cohort group. In one embodiment, a member of a cohort group is identified. Each member of the cohort group shares a common characteristic. Respiratory metadata associated with the member of the cohort group is received in real-time as the respiratory metadata is generated. The respiratory metadata describes respiration associated with the member of the cohort group. Patterns of respiratory changes are identified using the respiratory metadata. The respiratory metadata is analyzed to identify the patterns of respiratory changes. In response to the patterns of respiratory changes indicating behavioral deviations in the member of the cohort group, the member of the cohort group is identified as a person of interest.

Abstract: A computer implemented method, apparatus, and computer program product for identifying positional data for an object moving in an area of interest. Positional data for each camera in a set of cameras associated with the object is retrieved. The positional data identifies a location of each camera in the set of cameras within the area of interest. The object is within an image capture range of each camera in the set of cameras. Metadata describing video data captured by the set of cameras is analyzed using triangulation analytics and the positional data for the set of cameras to identify a location of the object. The metadata is generated in real time as the video data is captured by the set of cameras. The positional data for the object is identified based on locations of the object over a given time interval. The positional data describes motion of the object.

Abstract: A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a security perimeter area surrounding an airport. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.

Abstract: A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within an aircraft. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.

Abstract: A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a first airport. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.

Abstract: A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a pre/post security area within an airport. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives inference data comprising inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.

Abstract: A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a first aircraft. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.

Abstract: A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a gate area of an airport. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.

Abstract: A computer implemented method, apparatus, and computer usable program product for predicting an occurrence of a workplace accident. The process monitors current event data derived from a continuous video stream. The current event data comprises metadata describing events occurring at a workplace. The process then compares the current event data with previous event data that describes a set of previous events associated with a previously identified workplace accident. In response to detecting events in the current event data corresponding to at least one event in the set of previous events, the process generates a notification identifying a potential occurrence of the workplace accident.

Abstract: A computer implemented method, apparatus, and computer usable program product for ranking a potential customer. In one embodiment, external data associated with the potential customer is processed in a set of data models to generate a set of risk assessment factors for the potential customer. The external data comprises dynamic customer data elements generated in real-time as the potential customer is approaching a retail facility. The potential customer is ranked based on the risk assessment factors. The ranking indicates whether the potential customer poses a possible risk to the retail facility. In response to the ranking indicating that the potential customer poses the possible risk, actions are initiated to deter the potential customer from entering the retail facility.

Abstract: A computer implemented method, apparatus, and computer usable program product for optimizing loss leader merchandizing. The process parses event data to identify physical patterns of customer behavior, wherein the event data is derived from a continuous video stream captured at a retail facility, and associates the physical patterns of customer behavior with customer profile data to form dynamic customer profiles. The process then analyzes the dynamic customer profiles to identify types of customers and presents marketing incentives for a set of loss leader items to customers of a selected customer type, wherein the set of loss leader items are selected based on the dynamic profiles.

Abstract: A computer implemented method, apparatus, and computer usable program product for determining profitability of customer groups. The process parses event data to identify dynamic customer data, wherein the event data is derived from a continuous video stream captured at a retail facility. The process then combines the dynamic customer data with customer profile data to form dynamic customer profiles and analyzes the dynamic customer profiles to identify the customer groups. Thereafter, the process ranks the customer groups according to profitability of the customer groups.

Abstract: A computer implemented method, apparatus, and computer usable program product for creating a labor standard for a task. The process automatically detects event data derived from a continuous video stream, wherein the event data comprises metadata describing a sequence of motions for performing the task, and parses the event data to identify appropriate event data describing a discrete set of motions from the sequence of motions. The process then analyzes, using an analysis server, the discrete set of motions to form a labor standard, wherein the labor standard specifies an optimal manner of performing the discrete set of motions. Thereafter, the process generates a set of recommendations for performing the task efficiently according to the labor standard.

Abstract: A computer implemented method. A first synthetic event is received. The first synthetic event is derived from a first cohort comprising a first set of data and a second cohort comprising a second set of data. The first synthetic event comprises a third set of data representing a result of a mathematical computation on the first and second cohorts. A first rule set is created, the first synthetic event being expected as a result of application of the first rule set to the first cohort and the second cohort. The first rule set is applied to the first cohort and the second cohort to achieve a first result. The first result comprises a second event that is compared to the first synthetic event. A comparison is formed, the comparison comprising additional data that can be used to describe a difference between the second event and the first synthetic event.

Abstract: A computer implemented method, apparatus, and computer usable program product for generating an expected behavior model. The process parses event data derived from video data to identify behavioral patterns, wherein the event data comprises metadata describing events occurring in a selected environment. The process analyzes the behavioral patterns to identify a set of expected behavioral patterns occurring in the environment, and generates the expected behavioral model using the expected behavioral patterns.