Amazon Technologies, Inc.

Sachin Midha

Andrew Robert Toner

Nirav Praful Desai

Jai Prakash Chabria

Brad P Bonnett

Meghan Lagoni

Nicholas Stephen Bogert

Thiyagarajan Ramasamy

Chad Pham Driesbach

Rahul Gupta

Melanie C B Gens

Xinhong Zhang

Samuel Stevens Heyworth

Christopher Warren Bryant

Wael Hamza

Anoop Kumar

Sriram Venkatapathy

Pavel Bhowmik

Pooja Sanjay Sonawane

Samuel Harry Ingbar

Md Humayun Arafat

Ambareesha Raghothaman

Jeremy Todd Claeson

Darshan Dev Bildikar

Jwala Dhamala

Jennifer Yuen

Edward Charles Grenier

Dewan Muhammed Ibtesham

Balu Ramu

The systems and methods disclosed herein can increase the speed and ease with which a user can discover compatible replacement parts for a specified model of a product by automatically determining when the user is interested in replacement parts. A replacement part system can conclude that a user is looking for replacement parts based on the user's search queries, the user's browsing behavior, or the user's prior purchases. By automatically deducing an intention to purchase a replacement part, the replacement part system can make it easier for a user to locate suitable replacement parts. In addition, the replacement part system can be configured to tailor listing set of replacement parts search results it provides to a user. The set of search results can be tailored based on a number of criteria to increase the speed with which the user finds a compatible replacement part.

Search query-based replacement part interface

Techniques for tuning parameters for machine learning models are described. Different values for a parameter are tested to determine the value that results in an optimized model. A parameter value may be selected for testing using a search algorithm based on how the model performs with respect to other values for the parameter. Different values may be tested until a stopping criterion (such as time for testing, number of trials, amount of enhancement in performance, etc.) is met. In some embodiments, the techniques may be used to determine parameter values for natural language processing models.

Model configuration

Techniques for evaluating a natural language understanding (NLU) component and determining an action to resolve an issue processing a user input are described. The system determines which component is invoked by a baseline NLU component is processing the user input, and which component is invoked by an updated NLU component. Based on that information, the system selects the action to resolve the updated NLU component generating an undesired response to the user input.

Evaluating natural language processing components

Systems and methods herein can generate sales rankings for parts based on one or more metrics, such as geographic location of purchasers of the parts or based on items owned by users that function in conjunction with the parts. The systems can present the sales rankings to a user based on another item owned by the user. For example, sales rankings of memory can be presented to a user based on a laptop model owned by the user. Thus, users can determine how popular a part is among particular segments of the population, such as among users living in a certain geographic region, or among users who own a particular item designed to operate with the part. Moreover, the sales rankings can be used to generate recommendations of parts based on a corresponding item owned by the user that is configured to function with the part type.

Item-specific sales ranking systems and methods

Growing community of inventors

New Delhi, India

Sachin Midha