In various embodiments, a voice command is associated with a plurality of processing steps to be performed. The plurality of processing steps may include analysis of audio data using automatic speech recognition, generating and selecting a search query from the utterance text, and conducting a search of database of items using a search query. The plurality of processing steps may include additional or different steps, depending on the type of the request. In performing one or more of these processing steps, an error or ambiguity may be detected. An error or ambiguity may either halt the processing step or create more than one path of actions. A model may be used to determine if and how to request additional user input to attempt to resolve the error or ambiguity. The voice-enabled device or a second client device is then causing to output a request for the additional user input.

Some embodiments include systems and methods for recall estimation. An exemplary method comprises determining reservoir sampling and size of labeling from each strata; determining recall and variance for each strata; determining sum of sample size for a time period; and determining reservoir sampling for each strata and label the sample items. Other embodiments are described.

Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on a new descriptor referred to as “purchase embeddings.” The purchase embedding may include a tuple in a new multi-dimensional search space for describing and tracking purchases of goods and services. Recommendations may be based on a distance between tuples in the search space, which provides a measure of similarity between items represented by the tuples.

A method performed by a computing device includes determining a set of identigens for each word of words of a product-service query to produce sets of identigens. The method further includes interpreting, using identigen pairing rules of a knowledge database, the sets of identigens to determine a most likely meaning interpretation of the product-service query and produce a query entigen group that includes query entigens. The method further includes identifying one or more characteristic entigen categories for a subjective category entigen of the query entigen group. The method further includes recovering a set of response entigens for the product-service query from the knowledge database utilizing the query entigen group and based on the one or more characteristic entigen categories. The set of response entigens provides an answer for the product-service query.

The disclosed context-based personalization system personalizes enterprise applications. The disclosed application collects and stores user events and user affinity signals during user sessions. By analyzing the captured user events and user affinity signals, the disclosed system can predict the user's preferences and customize settings, selections and options associated with the enterprise application based on the user's preferences.

A search system is provided for presenting diamonds that are available for sale to a user of the search system. The search system includes a graphical interface through which the user may input search parameters corresponding to gemological features. Diamond listings are generated in accordance with the search parameters and arranged according to user preferences.

The present invention relates to a method, a device, and a system for providing fashion information. Particularly, the system for providing fashion information, of the present invention, comprises: a sample data generation unit for generating sample data in which the same fashion items of various sizes are matched according to human body information; a sample data storage unit for storing the sample data; and a predictive fit data providing unit for receiving, from a user device, fashion item information and user body information, and generating predictive fit data in reference to the sample data stored in the sample data storage unit, wherein the predictive fit data can be referred to size or fit when a user selects the fashion items.

A valuation method, pricing method, apparatus and system for a cloud product. The method comprises: obtaining a value of a target billing item of a to-be-valued product and values of impact factors of the target billing item, the impact factors of the target billing item correspond to the target billing item; determining a comprehensive factor corresponding to the target billing item using the values of the impact factors of the target billing item; querying, according to the comprehensive factor corresponding to the target billing item, pricing information of the target billing item from a preset first-type mapping relationship regarding billing items, comprehensive factors, and the pricing information; and calculating a valuation fee for the to-be-valued product using the value and pricing information of the target billing item.

Various methods and systems for providing customized-item-specific interfaces of items on extended interface devices in a search system. A plurality of items—each having an extended interface configuration that indicates customized-item-specific interface instructions for extended presentation of items on extended interface devices—are accessed at a primary interface device associated with a set of extended interface devices including a second-user extended interface device that supports a social mode. Based on the first extended interface configuration of a first item, a determination is made that the first item is extendable to a first extended interface device in the set of extended interface devices. Based on determining that the first item is extendable, automatically causing generation of a customized-item-specific interface of the first item on the first extended interface device. Causing generation of the customized-item-specific interface can be based on communicating item extended interface data to the first extended interface device.

Various methods and systems for providing predicted item conditions for items in an item listing system. A predicted item condition may indicate a calculated estimate of a descriptive state of the item based on item transaction features. Operationally, item transaction features of an item—associated with an item listing interface—are accessed at the item listing system. The item transaction features are communicated to an item condition prediction machine learning model of the item listing system. The item condition machine learning model is trained on historical item transactions comprising item condition features of historical item transactions, the historical item transactions are previous item transactions associated with the item listing system. Based on the item transaction features of the item, the item condition machine learning model is caused to generate a predicted item condition. The predicted item condition is communicated as a recommended item condition or required item condition.