Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: An attention application measures a user's attention focused on publisher content and advertisements to create an attention metric. Attention can be measured via hardware sensors or by user interactions with input/output hardware. A user attention metric profile can be used to modify content, content presentation, and/or match ads. Aggregate attention metrics can be used by publishers or third parties. Attention consumers may reward attention with a digital asset. A proof-of-attention can be made based on secure attention sensor hardware and/or a zero-knowledge proof.

Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: An attention application includes a digital asset wallet. The attention application can estimate the attention a user has spent such as on media content created by a publisher or ads sent by an advertiser. The attention application can pay an amount of the digital asset to the publishers based on the attention spent by the user or receive a payment of the digital asset if the user devoted sufficient attention to a paying advertiser.

Abstract: An attention application measures a user's attention focused on publisher content and advertisements to create an attention metric. Attention can be measured via hardware sensors or by user interactions with input/output hardware. A user attention metric profile can be used to modify content, content presentation, and/or match ads. Aggregate attention metrics can be used by publishers or third parties. Attention consumers may reward attention with a digital asset. A proof-of-attention can be made based on secure attention sensor hardware and/or a zero-knowledge proof.

Abstract: A JavaScript enhancement is provided that increases performance of the JavaScript by avoiding the use of locking mechanisms in multi-threaded sessions. To do so, an object is deemed owned by a thread exclusively until the script is ended or suspended. Because scripts are typically short and complete quickly, the overhead to start and end a request containing a script is much less than the overhead of locking each and every object access done by the script, on average.

Abstract: A JavaScript enhancement is provided that increases performance of the JavaScript by avoiding the use of locking mechanisms in multi-threaded sessions. To do so, an object is deemed owned by a thread exclusively until the script is ended or suspended. Because scripts are typically short and complete quickly, the overhead to start and end a request containing a script is much less than the overhead of locking each and every object access done by the script, on average.