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: According to an embodiment of the present invention, a system dynamically captures and stores an image based on the context of the image being captured. Initially, an image capture device receives and analyzes an image to determine a first set of one or more attributes associated with the image. A processor compares the first set of attributes associated with the image with a second set of one or more pre-defined attributes associated with an image context indicating preferences for image capture, and, based on the results of the comparing, instructs the image capture device to store the image. Embodiments of the present invention further include a method and computer program product for capturing an image based on the context of the image in substantially the same manner described above.

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: According to an embodiment of the present invention, a system dynamically captures and stores an image based on the context of the image being captured. Initially, an image capture device receives and analyzes an image to determine a first set of one or more attributes associated with the image. A processor compares the first set of attributes associated with the image with a second set of one or more pre-defined attributes associated with an image context indicating preferences for image capture, and, based on the results of the comparing, instructs the image capture device to store the image. Embodiments of the present invention further include a method and computer program product for capturing an image based on the context of the image in substantially the same manner described above.

Abstract: A computer-implemented method comprising: presenting, by a computing device, an advertisement on a first device; monitoring, by the computing device, viewing activity by a user of the advertisement, wherein the monitoring comprises tracking an amount of time that the user actively views the advertisement on the first device; and continuing, by the computing device, playback of the advertisement on one or more second devices until the user has actively viewed the advertisement for a target impression time based on the monitoring.

Abstract: Advertisement content is received at a processor that manages inventory of a smart home appliance from an advertising distribution device within a home network. A determination is made to add an advertised item of the advertisement content to a spoke shopping cart based upon commodity data of the managed inventory of the smart home appliance. The advertised item is added to the spoke shopping cart.

Abstract: A method for dynamically assembling a mobile application includes the steps of: (i) receiving a plurality of artifacts from a programmer; (ii) storing the received plurality of artifacts in an artifacts database; (iii) receiving, from a user, a request for a dynamically assembled mobile application, the request comprising a plurality of parameters defining a desired output of the dynamically assembled mobile application; (iv) identifying, using the received plurality of parameters, a template for a mobile application configured to achieve the desired output; (v) identifying, using the identified template, a plurality of the artifacts stored in the artifacts database; (vi) assembling the identified plurality of artifacts into a mobile application comprising an installable file, wherein the assembled mobile application was previously not available to the user; and (vii) making the installable file available to the user.

Abstract: Systems and methods for determining advertisement content based on cluster density analysis of advertisement targets within a dynamic visibility fence are disclosed. A computer-implemented method includes determining, by an advertisement determination device, a dynamic visibility fence for a time, the dynamic visibility fence defining a first set of advertisement targets, determining, by the advertisement determination device, an advertisement to display based on cluster analysis of the first set of advertisement targets, and displaying, by the advertisement determination device, the determined advertisement.

Abstract: A method for dynamically assembling a mobile application includes the steps of: (i) receiving, from a user, a request for a mobile application comprising at least one parameter; (ii) searching a profile database for a profile associated with the user; (iii) retrieving, from any identified profiles, a characteristic of the user; (iv) identifying a plurality of artifacts in a database of artifacts, wherein the plurality of artifacts are associated with the at least one parameter; (v) assembling the identified plurality of artifacts into a mobile application installable file; (vi) making the installable file available to the user.

Abstract: Systems and methods for providing a food product recommendation are disclosed. A computer-implemented method includes: determining, by a computing device, that a user with a mobile device enters a store; obtaining, by the computing device, user data that is based on wearable health data, taste data, and preference data of the user; obtaining, by the computing device, store data for plural food products; determining, by the computing device, a recommended food product from the plural food products based on comparing the user data to the store data; and providing, by the computing device, an indication of the recommended one of the plural food products for display on the mobile device of the user. The user data may be that of a single user or plural users, such as a family.

Abstract: A method for dynamically assembling a mobile application includes the steps of: (i) receiving, from a user, a request for a mobile application comprising at least one parameter; (ii) searching a profile database for a profile associated with the user; (iii) retrieving, from any identified profiles, a characteristic of the user; (iv) identifying a plurality of artifacts in a database of artifacts, wherein the plurality of artifacts are associated with the at least one parameter; (v) assembling the identified plurality of artifacts into a mobile application installable file; (vi) making the installable file available to the user.

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: A computer retrieves profile information of a participant of a multi-party communication. The computer identifies an original jargon included in the multi-party communication based, at least in part, on the profile information. The computer generates a translated jargon by translating the original translated jargon, wherein the translated jargon can be understood by the participant of the multi-party communication. The computer sends the translated jargon to the participant of the multi-party communication.