An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.

An online system receives a list of ingredients and corresponding quantities of each ingredient. Based on an item catalog of specific items offered by a source, the online system retrieves items offered by the source matching the ingredients and selects an item for an ingredient. Because the source may not offer an item in the same quantity specified by the list of items, the online system also maps a quantity of an ingredient in the list to a quantity of the selected item in a unit in which the source offers the corresponding item. The online system may convert a quantity of an ingredient to a quantity of an item through application of one or more rules or through application of one or more trained models to the quantity of the ingredient.

An information processing method according to one aspect is characterized in that a plurality of service providers who provide services are registered, business information indicating the details of business is output to a terminal device (3) of each registered service provider, a service provider with whom an order for the business is to be placed, among the service providers, is specified in a single auction format on a first-come, first-served basis in relation to the business information, and a process for paying a predetermined referral fee from the specified service provider to an orderer who places an order for the business is executed.

Described herein is a system, method, and non-transitory computer readable medium related to a service provider using a third party identity provider to authenticate a user with improved security. An authentication token is received from the identity provider, and can be verified against internal configuration information. The internal configuration information includes data that is not included in the authentication token, and therefore, is not vulnerable to some security attacks, such as a man-in-the-middle attack. After the authentication token is verified, the internal configuration information and authentication token may be used to create a custom identifier, referred to as an identity ID. The identity ID may be used by the service provider to verify user access to resources.

Disclosed is a system for AI-based auction bidding and a method thereof. The method includes receiving a sign-up request for an auction. The method includes initiating, by an AI bidding bot, a phone call to a mobile device of the client before the initiating the auction process. The method also includes presenting a disclaimer to the client specifying bid finality, bidding currency, urgency of bidding, and liability limitations. The method further includes presenting bid amounts to the client and requesting confirmation of the bid amounts. The method also includes receiving the bid confirmation from a client as a voice prompts. The method includes sending the voice prompt to an NLP engine and converting voice prompt to text data. The method further includes updating, by the AI bidding bot, the database with the confirmed bid. The method also includes automatically transmitting the confirmed bid to a central site via Webhook.

Various implementations described herein are able to leverage the interaction from one or more potential buyers relative to a digital image to automatically create a sales listing for items that appear to be of interest to the buyers. This reduces or eliminates all together the manual effort previously required of sellers in researching and collecting data on each item they wish to sell. Because of their technical nature, the innovative solutions described herein are also readily scalable which, in turn, greatly improves the seller's experience. Based on buyer interaction experiences, sales listings for each item for sale can be automatically created and listed.