US 12,241,753 B2
Systems and methods for personalized ground transportation processing and user intent predictions
Evangelos Simoudis, Menlo Park, CA (US)
Assigned to Synapse Partners, LLC, Menlo Park, CA (US)
Filed by Synapse Partners, LLC, Menlo Park, CA (US)
Filed on Jul. 8, 2022, as Appl. No. 17/811,438.
Application 17/811,438 is a continuation of application No. PCT/US2021/015020, filed on Jan. 26, 2021.
Claims priority of provisional application 62/969,472, filed on Feb. 3, 2020.
Prior Publication US 2022/0341746 A1, Oct. 27, 2022
Int. Cl. G01C 21/36 (2006.01); G01C 21/34 (2006.01); G06F 18/214 (2023.01); G06F 18/23 (2023.01); G06N 20/00 (2019.01)
CPC G01C 21/3617 (2013.01) [G01C 21/3423 (2013.01); G01C 21/3484 (2013.01); G06F 18/214 (2023.01); G06F 18/23 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for predicting a trip intent of a user trip taken by a user to determine information to provide to the user during the user trip, comprising:
(a) receiving a starting geographic location of a travel route and data about an identity of the user;
(b) training a trip abstractor by:
(1) obtaining a training plurality of prior trip records, wherein a prior trip record of the training plurality of prior trip records comprises a trip intent and trip characteristics of a prior trip for which an intent was determined;
(2) generating a plurality of models, wherein for at least two models of the plurality of models, each of the at least two models has an associated trip intent different from the trip intent associated with the other of the at least two models and each of the at least two models comprises a trip classifier that is trained on a subset of the training plurality of prior trip records that are prior trip records having a trip intent that matches the associated trip intent; and
(3) storing the plurality of models into a trip classifier database;
(c) identifying, using a trip identification engine, a trip dataset from location data collected from the user trip, wherein the location data provides geospatial points visited by the user during the user trip, with at least some visited points provided to the trip identification engine in real-time;
(d) applying the trip dataset and the trip classifier database to an intent predictor, to determine a determined trip intent of the user trip, wherein the intent predictor uses at least the at least two models of the plurality of models for determining the determined trip intent of the user trip; and
(e) presenting, to the user, on an electronic device, while the user is traveling in a vehicle along at least a portion of the travel route, one or more transactional options identified based, at least in part, on the determined trip intent of the user trip.