The present invention relates to a method for classifying food items. The method includes the steps of: capturing one or more sensor data relating to a food item event; and classifying the food item, at least in part, automatically using a model trained on sensor data. A system and software are also disclosed.

Disclosed herein are systems and methods for personal taste recommendation. In one implementation, an image set is obtained at a recommendation system. The image set has at least one image of a wine list menu having one or more wines for a dining location, and the image set is captured using a camera of a user device. An identified wine is generated for each of the one or more wines on the wine list menu based on a match to a known wine. A personalized wine menu unique to a particular user set for the dining location is generated by generating a personalized taste match of the particular user set for each of the one or more identified wines.

Disclosed are systems and methods utilizing neural contextual bandit for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to make item recommendations using latent relations and latent representations, which can improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods use neural network modeling in automatic selection of a number of items for recommendation to a user and using feedback in connection with the recommendation for further training of the model(s).

A rental self-service (“RSS”) computer system, including a processor and a memory, is provided. The processor is programmed to: (i) verify that a policyholder is eligible to receive a replacement rental; (ii) transmit a customized link to the eligible policyholder; (iii) receive an access request for access of a rental self-service portal, (iv) retrieve policyholder data using information extracted from the customized link; (v) pre-populate a portion of a searchable interface of the rental self-service portal using the retrieved policyholder data; (vi) cause rental vehicle data to be displayed to enable the policyholder to view available rental options and select a vehicle class; (vii) calculate a policyholder cost for renting a selected vehicle class; and (viii) cause to be displayed on the user device (a) the calculated policyholder cost and (b) an option to confirm the rental reservation.

An electronic device includes a user interface, a memory, and one or more processors. In response to the one or more processors detecting commencement of an interactive session in an electronic shopping application operating on the processors, the one or more processors initiate a timer for each search string category detected. In response to the interactive session ceasing, the one or more processors present a compilation of timer data for the interactive session. The compilation can itemize each search string category and a corresponding amount of time associated with each search string category.

A query for one or more resources is received. One or more tokens associated with the query is identified based on running the query through a learning model. The one or more tokens correspond to one or more terms that the query shares context similarity to based on a history of user selections. One or more search result candidates are scored based at least on the context similarity between the one or more tokens and the query.

A method of analyzing images by a computing device to generate an e-commerce interface, comprising receiving, from a computing device associated with a first human user, a list of items associated with the first human user; receiving, from a computing device associated with a merchant, purchasing information for each item of the list of items; receiving, from a second human user, one or more images depicting the first human user using one or more items from the list of items; automatically analyzing the one or more images to identify the one or more items; and generating a user interface comprising at least one of the one or more images, links to a computing system of the merchant from which the second human user may obtain the one or more items identified.

A method includes displaying and capturing image data containing an object and accessing a plurality of records related to objects, selecting a record related to the captured object, obtaining an identifier of a vendor of the object of the selected data record, combining the selected data record and the vendor identifier to form a search record, displaying, based on the search record, an augmented reality interface to receive a first interactive action for saving the search record, receiving the first interactive action, saving, in response to receiving the first interactive action, the search record into a searchable data structure, receiving a second interactive action, retrieving, in response to receiving the second interactive action, the search record from the searchable data structure, updating the vendor identifier based on the retrieved search record, and displaying information related to the search record.

Techniques are described for automatically extracting items from a user's browse history on one or more e-commerce websites, and automatically searching other e-commerce websites and sources for offers (including coupons, deals, and/or promotions) for those items and/or related items and/or stores. Such automated techniques allow users to gain the benefit of online offers from various sources, without having to go through the effort of manually searching for such offers. Instead, the system and method automatically locate such offers based on the user's browse history, without requiring any action to be taken on the part of the user.

Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.