Vehicle status analysis mechanism

A computer implemented method of dynamically generating instructional inputs for feeding into a generative model for presenting damage to a vehicle, comprising:

• using at least one processor executing a code for:
• receiving a plurality of images of the vehicle acquired by a plurality of image sensors positioned at a plurality of different heights and/or angles relative to the vehicle;
• accessing a data storage device storing the generative model;
• generating a first instructional input including first a combination of a first classification of a plurality of classifications and the plurality of images of the vehicle indicating the damage, wherein the plurality of classifications denote increasing levels of severity of damage to the vehicle;
• sending the first instructional input to an input communication element for input into the generative model;
• obtaining from the generative model, a first set of a plurality of natural language elements corresponding to the first classification, the first set of natural language elements associated with a first indication for structural reconstitution of the damage corresponding to the first classification;
• generating a first state of a presentation of a template with a plurality of fields populated with the first set of natural language elements and including at least one interactive element for selecting a second classification of the plurality of classifications,
• presenting the presentation on an endpoint device accessing the at least one processor;
• and
• in real-time, detecting a selection of a second classification of the plurality of classifications made by a user interacting with the at least one interactive element of the presentation;
• generating a second instructional input including a second combination of the second classification of the plurality of classifications and the plurality of images of the vehicle;
• sending the second instructional input to the input communication element for input into the generative model;
• obtaining from the generative model, a second set of a plurality of natural language elements corresponding to the second classification, the second set of natural language elements associated with a second indication for structural reconstitution of the damage corresponding to the second classification,
• wherein the first state of the presentation corresponding to the first classification and a second state of the presentation corresponding to the second classification are for the same plurality of images of the vehicle of the same damage to the same vehicle;
• and
• dynamically updating the presentation on the endpoint device for presenting the second state of the presentation by replacing the plurality of fields of the template with the second set of natural language elements.

A non-transitory medium storing program instructions for dynamically generating instructional inputs for feeding into a generative model for presenting damage to a vehicle, which when executed by at least one processor, cause the at least one processor to:

• receive a plurality of images of the vehicle acquired by a plurality of image sensors positioned at a plurality of different heights and/or angles relative to the vehicle;
• access a data storage device storing the generative model;
• generate a first instructional input including first a combination of a first classification of a plurality of classifications and the plurality of images of the vehicle indicating the damage, wherein the plurality of classifications denote increasing levels of severity of damage to the vehicle;
• send the first instructional input to an input communication element for input into the generative model;
• obtain from the generative model, a first set of a plurality of natural language elements corresponding to the first classification, the first set of natural language elements associated with a first indication for structural reconstitution of the damage corresponding to the first classification;
• generate a first state of a presentation of a template with a plurality of fields populated with the first set of natural language elements and including at least one interactive element for selecting a second classification of the plurality of classifications,
• present the presentation on an endpoint device accessing the at least one processor;
• and
• in real-time, detect a selection of a second classification of the plurality of classifications made by a user interacting with the at least one interactive element of the presentation;
• generate a second instructional input including a second combination of the second classification of the plurality of classifications and the plurality of images of the vehicle;
• send the second instructional input to the input communication element for input into the generative model;
• obtain from the generative model, a second set of a plurality of natural language elements corresponding to the second classification, the second set of natural language elements associated with a second indication for structural reconstitution of the damage corresponding to the second classification,
• wherein the first state of the presentation corresponding to the first classification and a second state of the presentation corresponding to the second classification are for the same plurality of images of the vehicle of the same damage to the same vehicle;
• and
• dynamically update the presentation on the endpoint device for presenting the second state of the presentation by replacing the plurality of fields of the template with the second set of natural language elements.