Methods and systems to automatically respond to a request for product information are described. In one embodiment, a data collection module may collect product data over a network, identify that the product data is related to a particular product, and store the product data and an association between the product data and the particular product in a database. An instant messaging engine may receive a request for the product data after the product data is stored in the database and an interpreter module may identify, in response to the request, the request to correspond to the particular product and retrieve the product data based on the association between the particular product and the product data. The instant messaging engine may also transmit, using an instant messaging protocol, a response to the request, the response including the product data.

A morphing interface system updates, that is, morphs, a display on a client device as a user provides portions of input. The system receives a first portion of user input and selects an intent related to a predicted intent of the user that matches or can be associated with the portion of user input. The system displays an interface associated with the intent at the client device. The system additionally extracts entity values associated with execution of the intent from the first portion of the user input. As the user provides additional portions of input, the morphing interface system reevaluates the user input and determines whether the user input still indicates the same user intent, and thus the same intent for execution. Upon determining that the additional input indicates a different user intent, the system updates the display to show an interface associated with the new intent.

This application relates to apparatus and methods for training machine learning models, and applying the trained models to determine an item's relevance to a search query. In some examples, features are generated based on item engagement data associated with a search query. First values are generated for each of a plurality of items based on the search query, a first attribute of each of the items, and the item engagement data. Second values are generated for the items based on the search query, the first attributes, a second attribute of each item, and the item engagement data. Third values are generated for the items based on the search query, the first attributes, a third attribute of each item, and the item engagement data. Features are generated based on the first, second, and third values, and a machine learning model is applied to the features to rank items.

Automating the selecting, completing, and e-signing of real estate forms is disclosed. With third party data, and minimal user input, the user is able to create a complete a compliant contract or form package; include financial qualification information, if applicable; as well as obtain properly placed electronic signatures with minimal time and effort. Where documents require additional third-party action, the document is sent directly to that third party for immediate action.

A server automatically generates a response on behalf of second users to a request received from a first user. A first stage of matching is performed that includes determining which of the second users match a requested location and request type. A second stage of matching is performed for each second user that includes computing a value that quantifies a likelihood of the first user selecting that second user to fulfill the request, and determining a capacity of the second user to fulfill the request. Some of the second users are selected based at least in part on the computed value and the determined capacity of each of the second users. A response is automatically generating for each of the selected second users. The generated responses are transmitted to the first user.

A system, platform, and method including a user interface adapted to receive a user input and communicatively connected with a communications module, a foodstuff database stored in memory including a set of available foodstuff transactions, and a controller module adapted to receive the user input receive at the user interface by way of the communications module, and configured to query the foodstuff database based on the user input, identify at least a subset of available foodstuff transactions based on the query, and send the identified subset of available foodstuff transactions to the user interface by way of the communications module.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving a plurality of snippets of a plurality of user reviews for a product, each respective snippet of the plurality of snippets relating to at least one respective user attribute category of a plurality of user attribute categories; creating a score for each respective snippet of the plurality of snippets based on: a probability of association between at least one user attribute category and one or more seed words, the one or more seed words describing one or more qualities of the product; and facilitating displaying, on a user device of a user, a first snippet of the plurality of snippets, the first snippet of the first plurality of snippets having a higher score of the scores for the plurality of snippets than another score of the scores for the plurality of snippets. Other embodiments are disclosed herein.

Embodiments of the disclosure provide for a knowledge search engine platform to analyze a search query initiated by a user device to identify a natural language inquiry associated with questions about a particular business, (e.g., product brand data such as products, services, employees, events, etc.). This may include searching through large amounts of data for hidden patterns and relationships of events, and presenting this knowledge data in a readily understandable format for users. The knowledge data is then presented in a structured form that is easily digestible to allow users via the user device to find the information they are looking to retrieve. The knowledge data may also include competitor intelligence that allows users to identify competitors, by searching a local pack to return results of potential competitors. The local pack may be within proximity of an indication of location of where the user is searching using with user device.

Provided is a product information processing system for facilitating registration of appropriate similar items in a post related to items. A determination receiver receives determination of one item among at least one candidate item having been selected to present details. The register registers a post containing a photo image in association with at least one candidate item, item information, and sales information on the item of which determination is received. When no sales information is associated with the registered post, or when sales information is associated with the post but the item indicated by the sales information is sold out on the e-commerce website, the similarity search result retriever retrieves a similarity search result through similarity searching in items now being sold on the e-commerce website on the basis of the at least one candidate item and the item information. The publisher publishes the post and sales information on items contained in the similarity search result.

A method and system for intelligent guided shopping. The system includes a computing device. The computing device has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to provide inference models based on user search data. Each of the inference models includes nodes and edges between the nodes. Each of the edges pointing from a first node to a second node is characterized with a probability value. The probability value represents a probability of searching a second keyword corresponding to the second node after searching a first keyword corresponding to the first node.