Systems and methods for validating added components of a vehicle

1 . A system for validating added components for a vehicle, the system comprising:

one or more processors; and

one or more non-transitory computer-readable media storing instructions that when executed by the one or more processors cause operations comprising:

receiving, from a computing device, a validation request, wherein the validation request comprises a vehicle identifier and a list of added components;

accessing vehicle data, from a plurality of cryptographic tokens related to a plurality of vehicles, wherein each cryptographic token of the plurality of cryptographic tokens corresponds to a vehicle of the plurality of vehicles, and wherein the vehicle data comprises information related to added components for a corresponding vehicle and associated outcome for whether the added components were determined to be credible for the corresponding vehicle, wherein an added component being credible comprises the added component having a probability of being physically included in the corresponding vehicle that is above a threshold;

training a first machine learning model based on the vehicle data to predict whether an added component for a corresponding vehicle is credible;

determining, from the plurality of cryptographic tokens, a cryptographic token corresponding to the validation request based on the vehicle identifier;

determining whether the list of added components from the validation request is compatible with base components for the vehicle from the cryptographic token, wherein the base components comprise features with which the vehicle is manufactured, wherein added components comprise features integrated with the vehicle in addition to the base components, and wherein an added component being compatible with a base component of the vehicle comprises the added component being capable of being integrated with the vehicle in presence of the base component;

in response to determining that the list of added components is compatible with the base components, determining, using the first machine learning model, whether each added component of the list of added components is credible;

in response to determining that one or more added components are not credible, generating an alert indicating the one or more added components as not credible; and

transmitting, to the computing device, the alert.

2 . The system of claim 1 , wherein the instructions when executed by the one or more processors cause further operations comprising:

prior to receiving the validation request, receiving, from a user device, a generation request to generate the cryptographic token, wherein the generation request comprises vehicle data and a user identifier;

determining, based on the user identifier, an address associated with a cryptography-based storage application corresponding to the user device, wherein the cryptography-based storage application comprises a private key; and

generating, using an on-chain program, the cryptographic token assigned to the address associated with the cryptography-based storage application, wherein the cryptographic token comprises a vehicle make, a vehicle model and a vehicle year.

3 . A method for validating added components for a vehicle, the method comprising:

receiving, from a computing device, a validation request, wherein the validation request comprises a vehicle identifier and a list of added components;

accessing vehicle data, from a plurality of cryptographic tokens related to a plurality of vehicles, wherein the vehicle data comprises information related to added components for a corresponding vehicle and associated outcome for whether the added components were determined to be credible for the corresponding vehicle;

determining, from the plurality of cryptographic tokens, a cryptographic token corresponding to the validation request based on the vehicle identifier;

determining whether the list of added components from the validation request is compatible with base components for the vehicle from the cryptographic token, wherein an added component being compatible with a base component of the vehicle comprises the added component being capable of being integrated with the vehicle in presence of the base component;

in response to determining that the list of added components is compatible with the base components, determining, using a first machine learning model, whether each added component of the list of added components is credible; and

in response to determining that one or more added components are not credible, generating an alert indicating the one or more added components as not credible.

4 . The method of claim 3 , wherein determining, using a first machine learning model, whether each added component is credible, based on each associated outcome further comprises retrieving the information relating to the vehicle from the cryptographic token, and parsing the information to generate a list of updated components related to the vehicle and the associated outcome.

5 . The method of claim 3 , further comprising, in response to determining that the list of added components is not compatible with the base components, determining whether the base components are credible based on the vehicle identifier.

6 . The method of claim 3 , wherein determining, using a first machine learning model, whether each added component of the list of added components is credible, further comprises:

transmitting, to a database, a first request for a plurality of cryptographic tokens, wherein the database stores a plurality of cryptographic tokens, and wherein the first request comprises a search vehicle identifier; and

comparing the plurality of cryptographic tokens to determine probability of whether each added component of the list of added components is credible.

7 . The method of claim 3 , further comprising:

in response to determining the list of added components from the validation request is compatible with base components, requesting a user to upload a set of images;

receiving the set of images from the user, wherein an image in the set of images corresponds to the added component from the list of added components; and

determining whether the image in the set of images accurately captures the added components.

8 . The method of claim 3 , wherein determining whether the list of added components from the validation request is compatible with base components further comprises determining, using a second machine learning model, an added component as a customization or a modification, wherein the customization comprises a change in an appearance in the vehicle, and wherein the modification comprises a change in functionality of the vehicle.

9 . The method of claim 8 , further comprising, in response to determining the added component as the customization, determining the added component is credible.

10 . The method of claim 8 , further comprising, in response to determining the added component as the modification, verifying the cryptographic token for the added component by searching for a timestamp associated with the modification.

11 . The method of claim 3 , further comprising:

receiving a first update, wherein the first update comprises a new added component to the vehicle; and

updating, using an on-chain program, the cryptographic token, wherein the cryptographic token comprises new vehicle data, and wherein the new vehicle data comprises the new added component.

12 . The method of claim 3 , further comprising:

in response to determining, using a first machine learning model, whether each added component of the list of added components is credible, requesting an appraisal; and

updating, using an on-chain program, the cryptographic token, wherein the cryptographic token comprises the appraisal.

13 . The method of claim 3 , wherein determining, using a first machine learning model, whether each added component of the list of added components is credible further comprises:

searching the plurality of cryptographic tokens and identifying the added component as a new added component;

requesting a user to upload a requested image for the new added component; and

determining, based on the requested image, whether the new added component is credible.

14 . The method of claim 3 , wherein each cryptographic token of the plurality of cryptographic tokens corresponds to a vehicle of the plurality of vehicles.

15 . The method of claim 3 , wherein an added component being credible comprises the added component having a probability of being physically included in the corresponding vehicle that is above a threshold.

16 . The method of claim 3 , wherein the base components comprise features with which the vehicle is manufactured, and wherein added components comprise features integrated with the vehicle in addition to the base components.

17 . The method of claim 3 , further comprising:

training a first machine learning model based on the vehicle data to predict whether an added component for a corresponding vehicle is credible.

18 . One or more non-transitory computer-readable media storing instructions that when executed by one or more processors cause operations comprising:

determining, from a plurality of cryptographic tokens related to a plurality of assets, a cryptographic token corresponding to a validation request for an asset and received from a computing device;

determining whether one or more added components from the validation request are capable of being integrated with the asset in presence of one or more base components for the asset from the cryptographic token;

in response to determining that the one or more added components are capable of being integrated, determining, using a first machine learning model, whether the one or more added components are credible; and

in response to determining that at least one component of the one or more added components is not credible, generating an alert indicating the at least one component as not credible.

19 . The one or more non-transitory computer-readable media of claim 18 , wherein the instructions when executed by the one or more processors cause further operations comprising:

accessing asset data, from the plurality of cryptographic tokens, wherein the asset data comprises information related to added components for a corresponding asset and associated outcome for whether the added components were determined to be credible for the corresponding asset, wherein an added component being credible comprises the added component having a probability of being integrated in the corresponding asset that is above a threshold; and

training the first machine learning model based on the asset data to predict whether an added component for a corresponding asset is credible.

20 . The one or more non-transitory computer-readable media of claim 18 , wherein the instructions when executed by the one or more processors cause further operations comprising:

prior to receiving the validation request, receiving, from a user device, a generation request to generate the cryptographic token, wherein the generation request comprises asset data and a user identifier;

determining, based on the user identifier, an address associated with a cryptography-based storage application corresponding to the user device, wherein the cryptography-based storage application comprises a private key; and

generating, using an on-chain program, the cryptographic token assigned to the address associated with the cryptography-based storage application.