An online tool, including a technology application that connects workers with customers that have immediate service needs. This tool provides immediate solutions to customers in need of a service and an economic opportunity to workers around the world. This tool seeks to connect expertise with opportunity at massive scale.

In some embodiments, apparatuses and methods are provided herein useful to providing an image-based shopping system. In some embodiments, a system for helping a customer find a desired product based on an image includes an electronic input device configured to receive, from the customer, the image, and a control circuit coupled to the electronic user input device and configured to identify one or more items in the image, identify, from a product data structure, a product that is similar to at least one of the one or more items in the image, and present, via an electronic display device, the product and an option to purchase the product.

An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system maintains user embeddings for users and recipe embeddings for recipes. For users who have not placed orders, recipes are recommended based on global user interactions with recipes. Users who have previously ordered items from recipes are suggested recipes selected based on a similarity of their user embedding to recipe embeddings. Users who have purchased items but not from recipes are compared to a set of similar users based on the user embeddings, and recipes with which users of the set of similar users interacted are used for identifying recipes to the users. A recipe graph may be maintained by the online concierge system to identify similarities between recipes for expanding candidate recipes to suggest to users.

A method for comparing decision options automatically determines factors for a set of decision options. Each of the factors defines a respective attribute of the set of decision options. The method provides the factors to a graphical user interface. The method receives a user preference value for at least one factor. The method then normalizes an attribute value of each factor. The method calculates a user preference score for each decision option as a function of the user preference value and the normalized attribute value for each factor. The method ranks the set of decision options based on the calculated user preference score for the subject decision option relative to each other decision option of the set of decision options. The method provides a ranked list of the set of decision options to the graphical user interface in response to ranking the set of decision options.

A method for use in a hosting system of a retail website, comprising: receiving respective first values for a plurality of parameters of an auto-buy campaign, the respective first values being specified by an administrator of the retail website hosting system; calculating a configuration score for the auto-buy campaign based on the respective first values for the plurality of parameters of the auto-buy campaign, the configuration score being indicative of a percentage of auto-buy bids submitted as part of the auto-buy campaign that are expected to result in a purchase; and outputting an indication of the configuration score for presentation to the administrator.

Methods and systems are disclosed for determining resource suitability based at least in part on physical geographic mapping data. A learning/learning engine may be trained to determine such resource suitability using training data. The trained learning/learning engine may then me used to generate suitability indicators. The suitability indicators may be rendered in association with a map comprising the resource. The resource may be configurable to be shared amongst a plurality of physical resource users in a time displaced manner. learning

An artificial intelligence (AI) engine assists in the creation of product descriptions. For example, a system that includes the AI engine receives raw data including a description of a product and generates a search query based on the raw data to search the internet for possible product descriptions that satisfy the query. The system can parse the product descriptions into categories of content that map to sections of a template and creating the custom product description based on a selection and combination of the categories of content of the product descriptions matched to sections of the template. An AI-engine is trained based on the selection and combination of the categories of content and ranks the content based on a frequency of being included in custom product descriptions such that the system can later recommend content in accordance with the ranking for creating an optimal custom product.

Systems and methods thereof, of generating a unique identification code for an industrial commodity. The method includes receiving a user query indicative of at least one constructional and operational characteristic of the commodity, inspecting the user query to determine whether the user query is complete for identification of the commodity, updating the user query based on the inspection, identifying at least one attribute of the commodity from the updated user query, based on a list of predefined attributes of the commodity, mapping the at least one attribute to at least one of predefined attribute types, predefined regional standards, predefined commodity rules, and predefined commodity types, and generating the unique identification code for the commodity, based on the mapping. The predefined attribute types may be a predefined commodity group and a predefined commodity part.

One example method includes generating personalized content for online sales. Recorded calls and other sources are converted to text. The text is semantically processed and correlated with customer data including customer order data to identify content that impacted the purchase of a product. The content may be incorporated into an online product page to facilitate online sales.

Methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing social data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, social data may be collected and analyzed using one or more machine learning datasets to generate a social profile. The sensor data and social profile may be used together to generate an output.