US 11,769,120 B2
Systems and methods for improving user experience during damage appraisal
Jennifer Lyons, San Diego, CA (US); Marcel de Neve, San Diego, CA (US); and Beau Sullivan, San Diego, CA (US)
Assigned to Mitchell International, Inc., San Diego, CA (US)
Filed by Mitchell International, Inc., San Diego, CA (US)
Filed on Dec. 14, 2020, as Appl. No. 17/121,437.
Claims priority of provisional application 63/091,907, filed on Oct. 14, 2020.
Prior Publication US 2022/0114558 A1, Apr. 14, 2022
Int. Cl. G06Q 10/20 (2023.01); G06Q 30/02 (2023.01)
CPC G06Q 10/20 (2013.01) [G06Q 30/0278 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for conducting a virtual appraisal, the system comprising:
a user computing device;
an appraisal management computing apparatus comprising:
a processor; and
a memory storing computer-executable instructions that, when executed by the processor, cause the appraisal management computing apparatus to perform operations comprising:
receive a plurality of damage evidence files each depicting one or more damaged parts of a vehicle damaged during an adverse incident;
determine a likelihood of relevance of each damage evidence file to an appraisal of an individual damaged part depicted in the file by using a first machine learning algorithm trained on historic relevance data comprising a plurality of damage evidence files associated with corresponding damaged parts previously identified as relevant;
present, in a graphical user interface (GUI) of an appraisal application associated with the user computing device, a set of relevant damage evidence files for each damaged part of the vehicle, each of the damage evidence files having the likelihood of relevance exceeding a threshold value;
generate a first set of training data comprising previously determined repair recommendations for restoring damaged parts;
train a second machine learning algorithm in a first stage with the first set of training data;
responsive to a user selection comprising an individual damaged part, present a repair recommendation for restoring the damaged part by using the second machine learning algorithm;
responsive to the user selection comprising a rejection of the recommendation, generate a second set of training data based on corrections provided by the user, and train the second machine learning algorithm in a second stage with the second set of training data;
wherein each of the sets of relevant damage evidence files associated with each of the damaged parts is presented upon receiving the user selection comprising a corresponding damaged part of the vehicle; and
responsive to the user selection comprising an acceptance of the recommendation, generate a repair estimate line.