Abstract: A method, computer program product, and system for generating flavor profile models are provided. An alpha flavor model is determined. An electrode signal is transmitted to a taste simulator based on the alpha flavor model. User feedback is received. A variance between the alpha flavor model and the user feedback is determined. The variance is compared to a predetermined threshold. In response to determining that the variance is greater than the predetermined threshold, a beta flavor model is generated.

Abstract: A method, computer program product, and system for generating flavor profile models are provided. An alpha flavor model is determined. An electrode signal is transmitted to a taste simulator based on the alpha flavor model. User feedback is received. A variance between the alpha flavor model and the user feedback is determined. The variance is compared to a predetermined threshold. In response to determining that the variance is greater than the predetermined threshold, a beta flavor model is generated.

Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.

Abstract: Generating recommendations for an individual based on a mood of the individual. Receiving information corresponding to one or more activities associated with an individual over a period of time. The received information corresponding to the one or more activities associated with the individual is processed to detect a mood of the individual. A recommendation is generated for the individual based on the detected mood of the individual and a future event associated with the individual. The future event has an occurrence at a later time instance.

Abstract: Generating recommendations for an individual based on a mood of the individual. Receiving information corresponding to one or more activities associated with an individual over a period of time. The received information corresponding to the one or more activities associated with the individual is processed to detect a mood of the individual. A recommendation is generated for the individual based on the detected mood of the individual and a future event associated with the individual. The future event has an occurrence at a later time instance.

Abstract: The method includes receiving a location of a client device and one or more items of interest to a user of the client device. The method further includes determining that the location of the client device is within a threshold distance of an item of the one or more items of interest. The method further includes generating a communication with the user of the client device. The method further includes receiving a response from the user of the client device. The method further includes determining if the received response indicates an intent to purchase the item. In one embodiment, the method further includes identifying a sales representative to assist the user of the client device, responsive to determining that the received response indicates an intent to purchase the item.

Abstract: The method includes receiving a location of a client device and one or more items of interest to a user of the client device. The method further includes determining that the location of the client device is within a threshold distance of an item of the one or more items of interest. The method further includes generating a communication with the user of the client device. The method further includes receiving a response from the user of the client device. The method further includes determining if the received response indicates an intent to purchase the item. In one embodiment, the method further includes identifying a sales representative to assist the user of the client device, responsive to determining that the received response indicates an intent to purchase the item.

Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.

Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.

Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.

Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.

Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.

Abstract: A system and method that improves and enhances the customer's in-store shopping experience. The consumer product of purchase interest or intent to buy expresses (or self-announces via technology) it's match (or fit based on known or understood buying habits, customer taste, tendencies, etc) against the customer preferences during an in-store shopping experience.

Abstract: A method that improves and enhances the customer's in-store shopping experience. The consumer product of purchase interest or intent to buy expresses (or self-announces via technology) it's match (or fit based on known or understood buying habits, customer taste, tendencies, etc) against the customer preferences during an in-store shopping experience.

Abstract: A method that improves and enhances the customer's in-store shopping experience. The consumer product of purchase interest or intent to buy expresses (or self-announces via technology) it's match (or fit based on known or understood buying habits, customer taste, tendencies, etc) against the customer preferences during an in-store shopping experience.