US 12,340,410 B1
System and method for recommending resale alternatives for retail goods
Phoebe Gates, New York, NY (US); Sophia Kianni, New York, NY (US); and Silas Alberti, Stanford, CA (US)
Assigned to Phia Holdings Inc., New York, NY (US)
Filed by Phia Holdings Inc., New York, NY (US)
Filed on Jan. 27, 2025, as Appl. No. 19/038,238.
Application 19/038,238 is a continuation in part of application No. 18/429,245, filed on Jan. 31, 2024, granted, now 12,248,974.
Int. Cl. G06Q 30/00 (2023.01); G06F 16/9538 (2019.01); G06F 40/279 (2020.01); G06F 40/40 (2020.01); G06Q 30/0601 (2023.01); G06T 7/11 (2017.01); G06Q 30/0201 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06F 16/9538 (2019.01); G06F 40/279 (2020.01); G06F 40/40 (2020.01); G06T 7/11 (2017.01); G06Q 30/0206 (2013.01); G06Q 30/0641 (2013.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30176 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for identifying resale good alternatives for retail goods, the method comprising:
identifying a retail good on a retail goods webpage;
extracting metadata of the retail good from the retail goods webpage by:
extracting the metadata of the retail good using metadata heuristics that analyze metadata structures of the retail goods webpage;
performing large language model (LLM)-based extraction to transform unstructured webpage content into structured retail good metadata; and
identifying and caching selectors that target HTML tags containing the metadata of the retail good for re-use during subsequent extractions;
classifying the retail good into a retail category using keyword heuristics and LLM-based classification;
cropping an image of the retail good to isolate the retail good using the retail category and a segmentation foundation model;
determining a descriptive color word for the retail good using clustering algorithms and color space mapping;
generating image vector embeddings for the retail good using a machine learning (ML) image embedding model;
generating text vector embeddings for the retail good using a ML text embedding model;
retrieving multiple ranked result sets from a vector database of resale goods including a first result set generated from the image vector embeddings and a second result set generated from the text vector embeddings;
merging the multiple ranked result sets into a unified result set;
re-ranking the unified result set by:
applying heuristics-based re-ranking;
applying ML language model-based re-ranking; and
applying preference-aware re-ranking; and
returning the re-ranked unified result set to the user.