US 12,271,866 B2
Methods and systems for creating software ecosystem activity score from multiple sources
Ashok Balasubramanian, Chennai (IN); Karthikeyan Krishnaswamy Raja, Chennai (IN); Meenakshisundaram Chinnappan, Chennai (IN); and Lakshmipathy Ganesh Eswaran, Chennai (IN)
Assigned to Open Weaver Inc., Miami, FL (US)
Filed by Open Weaver Inc., Miami, FL (US)
Filed on Feb. 25, 2022, as Appl. No. 17/680,683.
Claims priority of provisional application 63/154,362, filed on Feb. 26, 2021.
Prior Publication US 2022/0277270 A1, Sep. 1, 2022
Int. Cl. G06F 16/00 (2019.01); G06F 8/77 (2018.01); G06Q 10/0639 (2023.01); G06Q 10/10 (2023.01)
CPC G06Q 10/103 (2013.01) [G06F 8/77 (2013.01); G06Q 10/0639 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A system for providing software downloads, the system comprising:
one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving criteria from a user;
determining a plurality of software projects satisfying the criteria provided by the user;
automatically generating, for each of the plurality of software projects, an ecosystem activity score by:
receiving data from one or more repositories and one or more developer forums related to a software project;
calculating a popularity score of the software project based on the received data;
calculating a project activity score of the software project based on the received data;
calculating a defect score based on the received data;
calculating a community score by evaluating posts of the one or more developer forums on the software project;
calculating a solution score by evaluating solutions provided as a response to queries on the software project;
calculating a sentiment score by applying natural language processing and machine learning classification techniques to classify natural-language questions, answers, and comments in a developer forum relating to the software project into positive and negative sentiment categories;
calculating a userbase score by evaluating metadata of user posts, reputation score, and trends in metadata of users; and
calculating the ecosystem activity score based on the popularity score, the project activity score, the defect score, the community score, the solution score, the sentiment score, and the userbase score;
providing a recommended software project of the plurality of software projects to the user based on the recommended software project having a maximum activity score of the ecosystem activity scores; and
providing a link to download the recommended software project.