CPC G06Q 10/20 (2013.01) [G06Q 30/0278 (2013.01)] | 20 Claims |
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.
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