Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: Methods and systems for targeted advertisement include transmitting a pre-filter to a user device, responsive to contextual information supplied by the user device to determine one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. One or more targeted advertisements are determined, using a processor, based on the one or more inferences. The one or more targeted advertisements are transmitted to the user device.

Abstract: An input dataset for training a new machine learning model is received by a processor. For each of a plurality of trained machine learning models, a hash function and a sketch of a training dataset used to train the machine learning model is retrieved. A sketch of the input dataset is computed based on the hash function and the input dataset, along with a distance between the sketch of the training dataset and the sketch of the input dataset. The computed distances of the trained machine learning models are ranked from smallest to largest, and a seed machine learning model for the input dataset is selected from the trained machine learning models based at least in part on the ranking. A training process of the new machine learning model using the selected seed machine learning model and the input dataset is initiated.

Abstract: Pixel image data of a scene is received in which the pixel image data includes a two-dimensional representation of an object in the scene. Point cloud data including three-dimensional point coordinates of a physical object within the scene corresponding to the two-dimensional representation of the object is received. The three-dimensional point coordinates include depth information of the physical object. The point cloud data is mapped to an image plane of the pixel image data to form integrated pixel image data wherein one or more pixels of the pixel image data have depth information integrated therewith. A three-dimensional bounding box is predicted for the object using a convolutional neural network based upon the integrated pixel image data.

Abstract: A data mining method, system, and non-transitory computer readable medium, include defining a set of filter constraints as a filter function for clustering users' private records of data of a private domain, selecting a subset of users' public records of data from a filtered set of data from a public domain that is common with the users' private records of data, and creating a data file including the matched user of the private domain to the public records of the user of the private domain, where the set of the filter constraints comprises a function that captures the subset of the users' public records of data who are of interest to the private domain, and only performs data mining with that set of information from the public domain.

Abstract: A data mining method, system, and non-transitory computer readable medium, include defining a set of filter constraints as a filter function for clustering users' private records of data of a private domain, obtaining a set of data from a public domain by applying the filter function to users' public records of data of the public domain, selecting a subset of the users' public records of data that is common with the users' private records of data, and performing data mining on the selected subset of the users' public records of data in combination with the users' private records of data to match a user of the private domain to public records of the user of the private domain.

Abstract: Various embodiments determine a dynamic price of a combination of items for sale via a smart price tag attached to one of the items. A back-end price server receives a request to register a user and product combination. The product is associated with a first item for sale. The user and product combination is registered as a unique gesture at the back-end server. The back-end price server further receives a request for a current price for a combination of the first item for sale and a second item for sale and determines the current price of the combination of the first item for sale and the second item for sale for a user profile handle mapped to the unique gesture.

Abstract: A neural network classification method, system, and computer program product include selecting a set of models for combination based on scenario descriptors of existing models that best match with a requirement to generate a new model without requiring training data for the new model, where the existing models are trained for different data features.

Abstract: Various embodiments determine a dynamic price of a first item for sale via an attached first smart price tag. The back-end price server receives a request for a current price of the first item for sale and a representation of a user signature motion from the first smart price tag, the first smart price tag associated with the first item for sale. The back-end price server determines the current price of the first item for sale for a user profile handle mapped to the user signature motion. The current price of the first item for sale is sent to at least one of the first smart price tag or a mobile device associated with the user profile handle. The request for a current price of the first item for sale is initiated by shaking the first smart price tag using the user signature motion.

Abstract: An embodiment of the invention identifies a positive reaction to an advertisement and sends the advertisement to a mobile and/or wearable electronic device of a user. The advertisement can be sent to the mobile and/or wearable electronic device of the user only when the user is within a first threshold distance to a store of the advertiser, the user is within a second threshold distance to a store that is within a third threshold degree of similarity to the advertisement, the user is within the second threshold distance to a store that is within the third threshold degree of similarity to the advertiser, the user is browsing a website of the advertiser, the user is browsing a website that is within a fourth threshold degree of similarity to the advertisement, and/or the user is browsing a website that is within the fourth threshold degree of similarity to the advertiser.

Abstract: Various embodiments determine a sale price of an item via a smart price tag associated with the item. In one embodiment, the smart price tag is detected as being shaken. Responsive to detecting the smart price tag as being shaken, a request for a current sale price of the item for sale is sent to a back end price server. The current sale price of the item for sale is received from the back end price server and displayed for a predetermined period of time. The current sale price may be displayed on the smart price tag or a separate display sign. The current price is displayed until expiration of a predetermined period of time unless a lock request is detected which locks the price of the item for sale at the current sale price. If a price unlock button is pressed, the sale price of the item is unlocked.

Abstract: Various embodiments determine a sale price of an item via a smart price tag associated with the item. In one embodiment, the smart price tag is detected as being shaken. Responsive to detecting the smart price tag as being shaken, a request for a current sale price of the item for sale is sent to a back end price server. The current sale price of the item for sale is received from the back end price server and displayed for a predetermined period of time. The current sale price may be displayed on the smart price tag or a separate display sign. The current price is displayed until expiration of a predetermined period of time unless a lock request is detected which locks the price of the item for sale at the current sale price. If a price unlock button is pressed, the sale price of the item is unlocked.

Abstract: A model trading method, system, and computer program product include extracting a feature-model list from a first application and a feature-model list from a second application on a device, creating a price model for a price for each feature-model of the feature-model list of the second application that matches a feature-model of the feature-model list for the first application, and facilitating feature-model trading between the first application and the second application based on the feature-model list and the price model.

Abstract: Various embodiments request discount offers on a mobile device within a store. In one embodiment, the mobile device detects it is being shaken and in response, sends a request for a discount offer to a server. The mobile device determines its location within the store. The mobile device receives a discount offer based on the location of the mobile device within the store and information associated with the mobile device. The discount offer is displayed and feedback is communicated to the server based on the detected device movement. The feedback is an indicator that indicates acceptance, rejection or a request for a different discount offer. The detected device movement include at least one of motion intensity, direction of motion and number of shakes. If the motion intensity of being shaken is greater than a predetermined threshold, an indication of objection is emitted.

Abstract: Various embodiments determine a sale price of an item via a smart price tag associated with the item. In one embodiment, the smart price tag is detected as being shaken. Responsive to detecting the smart price tag as being shaken, a request for a current sale price of the item for sale is sent to a back end price server. The current sale price of the item for sale is received from the back end price server and displayed for a predetermined period of time. The current sale price may be displayed on the smart price tag or a separate display sign. The current price is displayed until expiration of a predetermined period of time unless a lock request is detected which locks the price of the item for sale at the current sale price. If a price unlock button is pressed, the sale price of the item is unlocked.

Abstract: Various embodiments determine a dynamic price of a first item for sale via an attached first smart price tag. The back-end price server receives a request for a current price of the first item for sale and a representation of a user signature motion from the first smart price tag, the first smart price tag associated with the first item for sale. The back-end price server determines the current price of the first item for sale for a user profile handle mapped to the user signature motion. The current price of the first item for sale is sent to at least one of the first smart price tag or a mobile device associated with the user profile handle. The request for a current price of the first item for sale is initiated by shaking the first smart price tag using the user signature motion.

Abstract: A subset of a set of image data is input into a trained convolutional neural network (CNN), the subset of image data including several of digital images, each image including a depiction of a real estate property at a different zoom level. By executing the CNN, a set of features is extracted from the subset of image data, a feature in the set of features being unrepresented in the subset of image data, and where the feature is derived from a depiction in the subset of image data. Using a set of node values configured at a set of nodes in a layer of the CNN, and using the set of features, a combined value of the set of features is computed, relative to the real estate property. A predicted price of the real estate property is predicted, by executing the CNN, using the combined value.

Abstract: A recipe recommendation method, system, and non-transitory computer readable medium, include inferring a fine-grained user food profile from user data, recommending a recipe for the user based on a fitness score associated with a user-recipe pairing according to the fine-grained user food profile and recipe data, extracting ingredients from the recommended recipe, and creating a shopping list from the extracted ingredients.

Abstract: A method for presenting advertisements based on emotion of a viewer is provided. A first emotional state of a viewer wearing smart glasses is detected using first sensor data captured by a sensor of the glasses. A rate is set according to the detected first emotional state. A set of advertisements is displayed on the smart glasses according to the set rate while a movie is played so that the advertisements are perceived by the viewer as overlaying the movie.

Abstract: A data mining method, system, and non-transitory computer readable medium, include defining a set of filter constraints as a filter function for clustering users' private records of data of a private domain, obtaining a set of data from a public domain by applying the filter function to users' public records of data of the public domain, selecting a subset of the users' public records of data that is common with the users' private records of data, and performing data mining on the selected subset of the users' public records of data in combination with the users' private records of data to match a user of the private domain to public records of the user of the private domain.