US 12,223,405 B2
Bid value determination for a first-price auction
Shengjun Pan, San Jose, CA (US); Tian Zhou, Sunnyvale, CA (US); Brendan Kitts, Seattle, WA (US); Hao He, Milpitas, CA (US); Bharatbhushan Shetty, Sunnyvale, CA (US); Djordje Gligorijevic, San Jose, CA (US); Junwei Pan, Sunnyvale, CA (US); Tingyu Mao, Sunnyvale, CA (US); San Gultekin, San Jose, CA (US); Balaji Srinivasa Rao Paladugu, San Jose, CA (US); Jianlong Zhang, San Jose, CA (US); Sneha Thomas, San Jose, CA (US); and Aaron Flores, Menlo Park, CA (US)
Assigned to Yahoo Ad Tech LLC, New York, NY (US)
Filed by Yahoo Ad Tech LLC, New York, NY (US)
Filed on May 21, 2023, as Appl. No. 18/199,953.
Application 18/199,953 is a continuation of application No. 16/994,976, filed on Aug. 17, 2020, granted, now 11,657,326.
Prior Publication US 2023/0289662 A1, Sep. 14, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/00 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 30/0273 (2023.01)
CPC G06N 20/00 (2019.01) [G06N 5/04 (2013.01); G06Q 30/0275 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
training a machine learning model using a plurality of sets of auction information to generate a first machine learning model comprising a plurality of feature parameters associated with a plurality of features of the plurality of sets of auction information, wherein the first machine learning model comprises (i) a first bid parameter comprising a first bid weight and (ii) a first bias parameter comprising a first bias weight;
loading the first machine learning model onto a bid shading module of a demand-side platform (DSP);
receiving, by the DSP, a bid request, wherein:
the bid request is associated with a request for content associated with a client device;
the bid request is indicative of a set of features comprising one or more features associated with the request for content;
a set of feature parameters, of the plurality of feature parameters, are associated with the set of features;
a first feature parameter, of the set of feature parameters, is associated with a first feature of the set of features;
a set of weights, of the set of feature parameters, is associated with the set of features; and
the first feature parameter comprises a first weight, of the set of weights, associated with the first feature;
determining a bid value associated with a content item;
inputting, into the bid shading module of the DSP, the bid value;
determining, based upon the set of feature parameters and using the first bid parameter and the first bias parameter of the first machine learning model loaded onto the bid shading module of the DSP, a plurality of win probabilities associated with a plurality of shaded bid values, wherein:
each shaded bid value of the plurality of shaded bid values does not exceed the bid value; and
a first win probability of the plurality of win probabilities is associated with a first shaded bid value of the plurality of shaded bid values and corresponds to a probability that the content item wins an auction associated with the request for content responsive to submitting the first shaded bid value to an auction module associated with the request for content;
determining, based upon the plurality of win probabilities associated with the plurality of shaded bid values, a second shaded bid value, wherein the determining the second shaded bid value is performed based upon (i) the set of weights, (ii) the first bid weight and (iii) the first bias weight; and
submitting the second shaded bid value to a first auction module for participation in a first auction associated with the request for content,
wherein one or more content items are provided for presentation on the client device associated with the request for content based upon a determination that the one or more content items are a winner of the auction.