Systems and methods for implementing a serial advisor

1 . A computer-implemented method comprising:

receiving, at an application platform associated with a computer server, a request to programmatically generate a unique context-based serial identifier for a product;

obtaining by a serial identifier generation component associated with the computer server, context data associated with the product;

generating, by a serial identifier generation component using a machine learning model and based on the context data, output comprising a recommended serial identifier and indication of a strength of the recommended serial identifier, wherein the machine learning model is trained using a training dataset that is generated by:

identifying training context data,

performing normalization operation on the training context data to generate normalized context data,

determining a classification label for the training context data based at least in part on the normalized context data, wherein the classification label is indicative of a likelihood of counterfeit activity, and

generating the training dataset based at least in part on the normalized context data and the classification label,

wherein training the machine learning model comprises applying the training dataset to a machine learning based algorithm to train an untrained machine learning model for determining configuration parameters for context-based serialization task based at least in part on the likelihood of counterfeit activity,

electronically transmitting the output comprising the recommended serial identifier to a portal corresponding to the application platform;

providing, in response to determining that the recommended serial identifier is associated with a complex set of configuration parameters, an alternate product distribution path that avoids geographic locations of high counterfeit activity; and

establishing the recommended serial identifier as the unique context-based serial identifier for the product.

2 . The computer-implemented method of claim 1 , wherein the establishing comprises:

transmitting, using the computer server, instructions to a user computing device hosting the application platform to display the recommended serial identifier; and

establishing, responsive to receiving user authorization input, the recommended serial identifier as the serial identifier for the product.

3 . The computer-implemented method of claim 1 , wherein the establishing comprises:

transmitting, using the computer server, instructions to a user computing device hosting the application platform to display the recommended serial identifier;

detecting, from the user computing device, a rejection indication of the recommended serial identifier provided a user;

providing, based on the detected rejection indication, instructions to the serial identifier generation component to generate another recommended serial identifier;

transmitting, using the computer server and upon receiving the another recommended serial identifier from the serial identifier generation component, instructions to the user computing device to display the another recommended serial identifier; and

establishing, responsive to receiving user authorization input, the another recommended serial identifier as the serial identifier for the product.

4 . The computer-implemented method of claim 1 , wherein the establishing comprises automatically establishing, without receipt of any additional user input, the recommended serial identifier as the serial identifier for the product.

5 . The computer-implemented method of claim 1 , wherein the product is a developed product or an anticipated product.

6 . The computer-implemented method of claim 1 , further comprising initiating a duplication protocol on the recommended serial identifier, wherein the initiating the duplication protocol comprises:

accessing a database of active serial identifiers associated with other products;

comparing the recommended serial identifier to any of the active serial identifiers;

determining whether a match exists between the recommended serial identifier and any of the active serial identifiers; and

providing, responsive to determining that the match exists, an instruction to the serial identifier generation component to generate another recommended serial identifier.

7 . The computer-implemented method of claim 1 , wherein the machine learning model is configured to:

programmatically classify one or more products as potential counterfeit targets in response to the one or more products having context data patterns indicating that they are one or more of (i) a value above a predetermined monetary threshold indicative of increased counterfeit risk, (ii) rare, being transported or distributed through suspect geographic locations.

8 . The computer-implemented method of claim 7 , wherein training the machine learning model further comprises:

programmatically configuring the machine learning model to tailor a complexity of the recommended serial identifier based upon the likelihood of counterfeit activity associated with the product as identified in the context data.

9 . The computer-implemented method of claim 8 , wherein the machine learning model is trained to:

programmatically assign a stronger and/or more complicated serial identifier to the product determined to be a likely candidate for counterfeiting based on the context data including counterfeit data.

10 . The computer-implemented method of claim 9 , wherein the computer-implemented method further comprises:

in response to programmatically identifying that the assigned stronger and/or more complicated serial identifier for the product has a complex set of configuration parameters, initiating one or more context-based serial identifier based on the context data and/or the recommended serial identifier.

11 . The computer-implemented method of claim 10 , wherein the computer-implemented method comprising operations further including:

enabling providing additional security for the product based on the programmatically initiated the one or more context-based serial identifier recommendations.

12 . The computer-implemented method of claim 11 , wherein enabling providing additional security for the product further including:

avoiding the suspect geographic locations of the counterfeit activity according to the alternate product distribution path.

13 . The computer-implemented method of claim 1 , wherein the computer-implemented method comprising operations further including:

implementing a serial advisor for the serial identifier generation component to generate the unique context-based serial identifier for the product;

deploying context-based product serialization in the serial advisor to generate the unique context-based serial identifier as a robust serial number which is resistant to one or more counterfeit attempts; and

training the machine learning model to provide an output recommendation for the unique context-based serial identifier that is optimized based on input context data.

14 . The computer-implemented method of claim 13 , wherein the computer-implemented method comprising operations further including:

leveraging one or more normalization algorithms to bring all data attributes in the context data onto a same scale to improve performance of the machine learning model in the serial advisor by:

reducing an impact of any outliers, and

improving an accuracy of the machine learning model associated with the one or more normalization algorithms.

15 . A computer system comprising:

a computer server;

a serial identifier generation component;

one or more computer processors; and

a non-transitory computer-readable storage medium storing instructions executable by the one or more computer processors, the instructions when executed by the one or more computer processors causing the one or more computer processors to perform operations including:

receiving, at an application platform associated with a computer server, a request to programmatically generate a unique context-based serial identifier for a product;

obtaining by a serial identifier generation component associated with the computer server, context data associated with the product;

generating, by a serial identifier generation component using a machine learning model and based on the context data, output comprising a recommended serial identifier and indication of a strength of the recommended serial identifier, wherein the machine learning model is trained using a training dataset that is generated by:

identifying training context data,

performing normalization operation on the training context data to generate normalized context data,

determining a classification label for the training context data based at least in part on the normalized context data, wherein the classification label is indicative of a likelihood of counterfeit activity, and

generating the training dataset based at least in part on the normalized context data and the classification label,

wherein training the machine learning model comprises applying the training dataset to a machine learning based algorithm to train an untrained machine learning model for determining configuration parameters for context-based serialization task based at least in part on the likelihood of counterfeit activity,

electronically transmitting the output comprising the recommended serial identifier to a portal corresponding to the application platform;

providing, in response to determining that the recommended serial identifier is associated with a complex set of configuration parameters, an alternate product distribution path that avoids geographic locations of high counterfeit activity; and

establishing the recommended serial identifier as the unique context-based serial identifier for the product.

16 . The computer system of claim 15 , wherein the establishing comprises:

transmitting, using the computer server, instructions to a user computing device hosting the application platform to display the recommended serial identifier; and

establishing, responsive to receiving user authorization input, the recommended serial identifier as the serial identifier for the product.

17 . The computer system of claim 15 , wherein the establishing comprises:

transmitting, using the computer server, instructions to a user computing device hosting the application platform to display the recommended serial identifier;

detecting, from the user computing device, a rejection indication of the recommended serial identifier provided a user;

providing, based on the detected rejection indication, instructions to the serial identifier generation component to generate another recommended serial identifier;

transmitting, using the computer server and upon receiving the another recommended serial identifier from the serial identifier generation component, instructions to the user computing device to display the another recommended serial identifier; and

establishing, responsive to receiving user authorization input, the another recommended serial identifier as the serial identifier for the product.

18 . The computer system of claim 15 , wherein the establishing comprises automatically establishing, without receipt of any additional user input, the recommended serial identifier as the serial identifier for the product.

19 . The computer system of claim 15 , further comprising initiating a duplication protocol on the recommended serial identifier, wherein the initiating the duplication protocol comprises:

accessing a database of active serial identifiers associated with other products;

comparing the recommended serial identifier to any of the active serial identifiers;

determining whether a match exists between the recommended serial identifier and any of the active serial identifiers; and

providing, responsive to determining that the match exists, an instruction to the serial identifier generation component to generate another recommended serial identifier.

20 . A non-transitory computer-readable medium storing instructions executable by one or more computer processors of a computer server, the instructions when executed by the one or more computer processors cause the one or more computer processors to perform operations comprising:

receiving, at an application platform associated with a computer server, a request to programmatically generate a unique context-based serial identifier for a product;

obtaining by a serial identifier generation component associated with the computer server, context data associated with the product;

generating, by a serial identifier generation component using a machine learning model and based on the context data, output comprising a recommended serial identifier and indication of a strength of the recommended serial identifier, wherein the machine learning model is trained using a training dataset that is generated by:

identifying training context data,

performing normalization operation on the training context data to generate normalized context data,

determining a classification label for the training context data based at least in part on the normalized context data, wherein the classification label is indicative of a likelihood of counterfeit activity, and

generating the training dataset based at least in part on the normalized context data and the classification label,

wherein training the machine learning model comprises applying the training dataset to a machine learning based algorithm to train an untrained machine learning model for determining configuration parameters for outputting context-based serialization task based at least in part on the likelihood of counterfeit activity,

electronically transmitting the output comprising the recommended serial identifier a portal corresponding to the application platform;

providing, in response to determining that the recommended serial identifier is associated with a complex set of configuration parameters, an alternate product distribution path that avoids suspect geographic locations of high counterfeit activity; and

establishing, the recommended serial identifier as the unique context-based serial identifier for the product.