Considering the number of OSS components and the number of OSS license types available today, the number of license attributes to be considered for analyzing a product at a granular level is a challenge to perform manually, prudently considering legal implications of non-compliance and contamination and also within the limited time available today before going to market in the software industry. Systems and methods of the present disclosure intelligently facilitates a matrix which is able to identify OSS components in a software product and also facilitates the product owner to identify proprietary IP that can be suitably protected and licensed without contamination by the accompanying OSS components and generated components in the software product under consideration. License attributes of the OSS components are mapped suitably, and a final attribute is derived for each OSS component embedded in the product under consideration.

Systems, methods and apparatus relating to asset-class based tokens are described. An example apparatus may include a tokenization controller to implement a tokenization user interface where the tokenization controller includes a token definition circuit to interpret a token description in response to user operations. The example apparatus may further include an asset definition circuit to interpret an asset-class description in response to user operations and an asset backing circuit to interpret a token link description including a token performance representation corresponding to a beneficial characteristic of at least one asset of the asset-class description, and to generate at least one asset-class linked token in response to the token link description and the asset-class description. The example apparatus may further include a token deployment circuit to expose the at least one asset-class linked token to a token marketplace system.

Examples of the present disclosure describe systems and methods for providing control of input, output, and processing of an AI model. In examples, a request to execute an AI model implemented by a client device is received, where the AI model is associated with one or more licenses that specify a protection level that is applied to one or more portions of the AI model during the AI model runtime. In response to the request, the AI model is translated to a first set of commands in an intermediate language. The first set of commands is translated into a second set of commands for a hardware device of the client device. The second set of commands is translated into microcode that is executable by the hardware device. The hardware device then executes the microcode to generate an output in furtherance of the request.

Aspects of the technology disclosed herein related to a distributed architecture for securely delivering AI models and/or training data sets to client devices for local use. Model creators are able to provide encrypted versions of their models to a centralized server that in turn distributes copies of the encrypted models to a plurality of distribution servers. When a request for a particular model is received from a client device, the best-suited distribution server is identified to deliver the model. The distributed architecture further includes a licensing server that controls access to and decryption of the models. The licensing server controls the distribution of licensing packages for the different models delivered by the distribution server.

Aspects of the technology disclosed herein related to a distributed architecture for securely delivering AI models and/or training data sets to client devices for local use. The distributed includes a licensing server that controls access to and decryption of the models. The licensing server controls the distribution of licensing packages for the different models delivered by the distribution server. The client device transmits a license request to the licensing server. The licensing request may include device-level details about the client device itself, and such details may be provided in a secure, trusted manner, such as through a hardware root of trust (HROT) of the client device. If the details in the license request satisfy the security requirements for the model, a license package for the model is delivered to the client device. The license package includes a license for the model and a decryption key for the model.

Aspects of the technology disclosed herein related to a distributed architecture for securely delivering AI models and/or training data sets to client devices for local use. The distributed includes a licensing server that controls access to and decryption of the models. The licensing server controls the distribution of licensing packages for the different models delivered by the distribution server. The client device transmits a license request to the licensing server. The licensing request may include device-level details about the client device itself, and such details may be provided in a secure, trusted manner, such as through a hardware root of trust (HROT) of the client device. If the details in the license request satisfy the security requirements for the model, a license package for the model is delivered to the client device. The license package includes a license for the model and a decryption key for the model.