Walmart Apollo, LLC

Amlan J Das

Debanjana Banerjee

Srujana Kaddevarmuth

Somedip Karmakar

Mohit Batham

Bodhisattwa Prasad Majumder

Arunita Das

Tulika Dutta

Aloka Sudhodanan

Apurva Sinha

Mark A Hardy

Denila B Philip

Subhasish Misra

Abin Abraham

A system can include a database and a computing device. The computing device is configured to receive an item recommendation request corresponding to an asset from an analyst device and select a set of item identifiers of a plurality of item identifiers. An associated published timeframe of the selected item identifiers is related to a present timeframe. The computing device is further configured to determine a composite similarity value for each item identifier of the set of item identifiers comparing a similarity of the asset to each item identifier of the set of item identifiers. The computing device is also configured to generate an item recommendation list including each item identifier of the set of item identifiers with a corresponding composite similarity value above a threshold value and transmit the item recommendation list to the analyst device for display.

System, method, and non-transitory computer readable medium for generating recommendations

Some embodiments provide systems to determine contextual information comprising: an intent mining system that receive inquiry content that does not include personal identification information (PII) and is configured to determine an estimated intent information being sought by an intended recipient, identify a mapping to a sub-set of supplemental keywords corresponding to the intent information; and identify historic inquiries associated with actual historic product purchases relevant to the inquiry content and supplemental keywords, and obtain a listing of products associated with the inquiry content; a product association system that identifies a set of multiple products that each have a purchase threshold relationship with one or more products from the determined listing of products, and generate an enhanced listing of products; a topic extraction system that evaluates associations between product parameters of the enhanced listing of products to identify multiple associated topics and corresponding topic confidence scores.

Systems and methods of providing enhanced contextual intelligent information

Examples provide a multi-stage cluster component that performs a multi-stage clustering analysis on a plurality of items in a category associated with a selected item using a set of interrelationship factors. The multi-stage cluster component generates a cluster of non-substitute item-pairs, a cluster of traditional substitute item-pairs, and a cluster of variety item-pairs. The set of interrelationship factors includes at least one of measure of association, brand similarity, pack-size similarity, demographic similarity, item description similarity, lift, and/or percentage same-basket variable. A propensity score is generated for each item-pair. The propensity score is utilized to identify traditional substitute items and variety substitute items. Each substitute item is ranked based on the generated propensity score. The ranking is used to identify potential low-performance items for removal from inventory.

System for calculating competitive interrelationships in item-pairs

Systems, methods, and computer-readable storage media for categorizing items based on attributes of the item and a shape of the item, where the shape of the item is determined from an image of the item. An exemplary system configured as disclosed herein can receive a request to categorize an item, the item having a plurality of attributes, and receive an image of the item. The system can identify, via a processor configured to perform image processing, a shape of the item based on the image, and transform the plurality of attributes and the shape of the item, into a plurality of quantifiable values. The system can then categorize the item based on the quantifiable values.

System and method for clustering products by combining attribute data with image recognition

Systems and methods for auto-naming nodes in a behavior tree are provided. An example method can include: providing a hierarchy of tree nodes by a computing device; generating a first corpus for each node at a final level; creating a first term-document matrix associated with the first corpus; identifying a first group of high-frequency words in the first term-document matrix; removing the first group of the high-frequency words obtain a second corpus; creating a second term-document matrix based on each of a set of predefined rules; identifying a second group of high-frequency words to represent node names; selecting a best set of the predefined rules based on an automatic evaluation model; generating a node name by removing a duplicate word in each node; incorporating feedback to generate a predicted name for each node; and selecting a final name for each node from the predicted name and the generated node name.

Artificial intelligence system and method for auto-naming customer tree nodes in a data structure

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Bengaluru, India

Amlan J Das