US 12,468,765 B2
Machine learning techniques for generating personalized autocomplete prediction
Laura D. Hamilton, Chicago, IL (US); Ayush Tomar, Morgan Hill, CA (US); Vinit Garg, Fremont, CA (US); and Lun Yu, San Francisco, CA (US)
Assigned to Optum, Inc., Minnetonka, MN (US)
Filed by Optum, Inc., Minnetonka, MN (US)
Filed on Sep. 23, 2022, as Appl. No. 17/934,688.
Prior Publication US 2024/0104091 A1, Mar. 28, 2024
Int. Cl. G06F 16/9032 (2019.01); G06F 16/242 (2019.01); G06F 16/2457 (2019.01); G06F 16/9535 (2019.01)
CPC G06F 16/90324 (2019.01) [G06F 16/2428 (2019.01); G06F 16/24578 (2019.01); G06F 16/9535 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, by one or more processors, a search query prefix;
generating, by the one or more processors, a set of session-agnostic autocomplete scores for a set of candidate search results by:
generating a first subset of the set of session-agnostic autocomplete scores for a first subset of the set of candidate search results within an individual curated search corpus based at least in part on an edit distance measure between the search query prefix and each of the first subset of the set of candidate search results, and
generating a second subset of the set of session-agnostic autocomplete scores for a second subset of the set of candidate search results within a general search result corpus based at least in part on a trie-based frequency score that is generated based at least in part on one or more qualifying trie leaf node subsets of a general search result corpus trie data object for the general search result corpus and a general corpus historical frequency score for each of the second subset of the set of candidate search results;
generating, by the one or more processors, a set of session-aware autocomplete scores for the set of candidate search results;
generating (i) a weighted set of session-agnostic autocomplete scores by applying a first weight measure to the set of session-agnostic autocomplete scores and (ii) a weighted set of session-aware autocomplete scores by applying a second weight measure to the set of session-aware autocomplete scores, wherein the first weight measure exceeds the second weight measure; and
wherein (i) a weighted summation, of the set of weighted summations, for the candidate search result comprises a weighted combination of at least one weighted session-agnostic autocomplete score of the weighted set of session-agnostic autocomplete scores and at least one weighted session-aware autocomplete score of the weighted set of session-aware autocomplete scores, and (ii) a weight of the weighted combination is based at least in part on a presence of the candidate search result within the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a set of hybrid autocomplete scores for the set of candidate search results based at least in part on the set of weighted summations between the first subset of the set of session-agnostic autocomplete scores, the second subset of the set of session-agnostic autocomplete scores, and the set of session-aware autocomplete scores, wherein the set of weighted summations is based at least in part on one or more locations of a candidate search result of the set of candidate search results, and the one or more locations comprise the individual curated search corpus or the general search result corpus;
generating, by the one or more processors, a ranked autocomplete prediction based at least in part on the set of hybrid autocomplete scores; and
initiating, using the one or more processors, the performance of one or more prediction-based actions based at least in part on the ranked autocomplete prediction.