Amazon Technologies, Inc.

Aol Inc.

Saurabh Sohoney

Vineet Shashikant Chaoji

Bigyan Bhar

Vineet Khare

Amber Roy Chowdhury

Pranav Garg

Gourav Roy

Rakesh Nigam

Santhosh Baramasagara Chandrasekharappa

Sivakumar Ekambaram

Andrew Dennis Willingham

Sayan Putatunda

Govind

Sivaramakrishnan Kaveri

Techniques for improving last mile transportation and planning execution are described. In an example, a description of an address is received. Whether the address is not associated with a known geocode is determined. Using an ML model, an embedding vector of the address in an embedding space is generated, the ML model trained to embed addresses and geocodes in the embedding space. Based on the embedding vector, one or more known geocodes is determined. The address is then associated with the one or more known geocodes.

Machine learning model-based resolution of location information

Disclosed are various embodiments for automatically identifying whether applications are static or dynamic. In one embodiment, code of an application is analyzed to determine instances of requesting data via a network in the application. Characteristics of the instances of requesting data via the network are provided to a machine learning model. The application is automatically classified as either dynamic or static according to the machine learning model.

Automatically identifying dynamic applications

Features are provided for the analysis of collections of data and automatic grouping of data having certain similarities. A collection of data regarding user interactions with item-specific content can be analyzed. The analysis can be used to identify groups of items that are of interest to groups of similar users and/or to identify groups of users with demonstrated interests in groups of similar items. Data may be analyzed in a 'bottom-up' manner in which correlations within the data are discovered in an iterative manner, or in a 'top-down' manner in which desired top-level groups are specified at the beginning of the process. A bottom-up process may also be distributed among multiple devices or processors to more efficiently discover groups when using large collections of data.

Distributed grouping of large-scale data sets

Techniques for determining a maturity level of a text string may be provided. For example, the text string may be associated with an electronic book or story, script or closed captioning of a movie or television show, or other text associated with media. The system may analyze the text string to identify actions in the text (e.g., concepts of death, degree of violence, etc.), story complexity (e.g., number of characters, linear/non-linear story flow, etc.), vocabulary (e.g., unique words, complexity of terms, etc.), and other metrics to determine the objective maturity level associated with the text string. The maturity level and/or references to electronic books associated with the particular maturity level may be provided to users and/or used in additional processing and analysis.

Calculating a maturity level of a text string

Systems and methods are provided for identifying unsolicited or unwanted electronic communications, such as spam. The disclosed embodiments also encompass systems and methods for selecting content items from a content item database. Consistent with certain embodiments, computer-implemented systems and methods may use a clustering based statistical content matching anti-spam algorithm to identify and filter spam. Such a anti-spam algorithm may be implemented to determine a degree of similarity between an incoming e-mail with a collection of one or more spam e-mails stored in a database. If the degree of similarity exceeds a predetermined threshold, the incoming e-mail may be classified as spam. Further, in accordance with other embodiments, systems and methods may be provided to determine a degree of similarity between a query or search string from a user and content items stored in a database. If the degree of similarity exceeds a predetermined threshold, the content item from the database may be identified as a content item that matches the query or search string provided by the user.

Systems and methods for providing a content item database and identifying content items

Growing community of inventors

Bengaluru, India

Saurabh Sohoney