System and method for custom label embedding

A system to identify test user intent during evaluation of user interaction with a digital asset comprising:

• processor circuitry;
• and
• a memory storing instructions which, when executed by the processor circuitry, cause the processing circuitry to perform operations comprising:
• receiving data indicative of test user interaction with the digital asset;
• producing a vector representing the received data;
• using a trained machine learning model to project the vector to a first non-custom label embedding vector space, stored in a storage memory;
• using the trained machine learning model to project the vector to a second custom label embedding vector space, stored in the storage memory;
• determining a starting label associated with the vector based at least in part upon a nearest neighbor search within the first non-custom label embedding vector space for a label nearest to the vector and a nearest neighbor search within the second custom label embedding vector space for a label nearest to the vector;
• receiving user input associating a custom label with the starting label;
• and
• adjusting the second custom label embedding vector space based upon the user input.

A digital experience analysis system comprising:

• a storage memory database including a plurality of different respective storage memory regions corresponding to a plurality of different respective groups, wherein different respective storage memory regions associated with a different respective groups, store different respective copies of a global embedding vector space and store different respective custom embedding vector spaces;
• processor circuitry;
• and
• a memory storing instructions which, when executed by the processor circuitry, cause the processor circuitry to perform operations comprising:
• receiving data identifying a group from among the different respective groups, wherein the data is indicative of test user interaction with a digital asset;
• producing a vector representing the received data;
• using a trained machine learning model to project the vector to an instance of a global label embedding vector space at a storage region associated with the identified group;
• using the trained machine learning model to project the vector to a custom label embedding vector space at a storage region associated with the identified group;
• determining a starting label associated with the vector based at least in part upon a nearest neighbor search within the instance of the global embedding vector space associated with the identified group for a label nearest to the vector and a nearest neighbor search within the custom embedding vector space associated with the identified group for a label nearest to the vector;
• receiving user input associating a custom label with the determined starting label;
• and
• adjusting the custom embedding vector space associated with the identified group based upon the received user input.

A digital experience analysis system comprising:

• a storage memory database that stores one or more instances of a global label embedding vector space and that includes a plurality of different respective storage memory regions corresponding to a plurality of different respective groups, wherein different respective storage memory regions store different respective custom embedding vector spaces associated with a different respective groups;
• processor circuitry;
• and
• a memory storing instructions which, when executed by the processor circuitry, cause the processor circuitry to perform operations comprising:
• receiving data identifying a group from among the different respective groups, wherein the data is indicative of test user interaction with a digital asset;
• producing a vector representing the received data;
• using a trained machine learning model to project the vector to an instance of the one or more instances of the global label embedding vector space stored within the database;
• using the trained machine learning model to project the vector to a custom label embedding vector space at a storage region associated with the identified group;
• determining a starting label associated with the vector based at least in part upon a nearest neighbor search within the instance of the one or more instances of the global label embedding vector space for a label nearest to the vector and a nearest neighbor search within the custom embedding vector space associated with the identified group for a label nearest to the vector;
• receiving user input associating a custom label with the determined starting label;
• and
• adjusting the custom embedding vector space associated with the identified group based upon the received user input.

A computer-implemented method of digital experience analysis comprising:

• storing one or more instances of a global label embedding vector space in a storage memory;
• storing multiple different respective custom embedding vector spaces at different respective storage memory regions within the storage memory, the different respective custom embedding spaces associated with different respective groups having different respective language domains;
• receiving data identifying a group from among the different respective groups, wherein the data is indicative of test user interaction with a digital asset;
• producing a vector representing the received data;
• using a trained machine learning model to project the vector to an instance of the one or more instances of the global label embedding vector space stored within the database;
• using the trained machine learning model to project the vector to a custom label embedding vector space at a storage region associated with the identified group;
• determining a starting label associated with the vector based at least in part upon a nearest neighbor search within the instance of the one or more instances of the global label embedding vector space for a label nearest to the vector and a nearest neighbor search within the custom embedding vector space associated with the identified group for a label nearest to the vector;
• receiving user input associating a custom label with the determined starting label;
• and
• adjusting the custom embedding vector space associated with the identified group based upon the received user input.