US 12,346,918 B2
Determining locations for offerings using artificial intelligence
Vikas C. Raykar, Bangalore (IN); Surya Shravan Kumar Sajja, Bangalore (IN); Nupur Aggarwal, Bangalore (IN); and Vivek Sharma, Bangalore (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Mar. 9, 2022, as Appl. No. 17/690,194.
Prior Publication US 2023/0289832 A1, Sep. 14, 2023
Int. Cl. G06Q 30/02 (2023.01); G06N 20/20 (2019.01); G06Q 30/0201 (2023.01); G06Q 30/0202 (2023.01)
CPC G06Q 30/0202 (2013.01) [G06N 20/20 (2019.01); G06Q 30/0201 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
determining an initial sub-set of one or more enterprise locations, from a set of multiple enterprise locations, for providing at least one enterprise offering;
determining at least one sequential ordering of one or more additional ones of the multiple enterprise locations for providing the at least one enterprise offering by processing data related to the initial sub-set of one or more enterprise locations using an artificial intelligence engine, that utilizes one or more artificial intelligence techniques, wherein determining the at least one sequential ordering comprises:
(i) implementing one or more machine learning-based bootstrapping techniques which generate multiple distinct versions of portions of the data related to the initial sub-set, wherein at least a plurality of the multiple distinct versions of the portions of the data are processed by the artificial intelligence engine as part of determining the at least one sequential ordering; and
(ii) generating one or more matrix factorization-based offering forecasts using one or more artificial intelligence-based matrix factorization techniques in conjunction with: information pertaining to one or more features of the at least one enterprise offering and the multiple enterprise locations, at least one weight value assigned to the at least one enterprise offering, weight values assigned to the multiple enterprise locations, at least one non-zero bias value, at least one transpose of at least one matrix generated in connection with the one or more artificial intelligence-based matrix factorization techniques, one or more feature vectors associated with the at least one enterprise offering, and one or more feature vectors associated with the multiple enterprise locations; and
automatically training at least a portion of the artificial intelligence engine that utilizes the one or more artificial intelligence techniques based at least on feedback data pertaining to provision of the at least one enterprise offering to the at least one sequential ordering of one or more additional enterprise locations, wherein the feedback data comprises sales data associated with at least a portion of the one or more additional enterprise locations and cost data associated with at least a portion of the one or more additional enterprise locations;
wherein the method is carried out by at least one computing device.