An application provisioning system and method. A server provides an application provisioning service. A user of a client provides a schema defining an application. The application interacts with peripherals coupled to the client and receives input from sensors coupled to the peripherals. The sensor data is provided to the server for processing, including by neural networks. The application includes a workflow defining a finite state machine that traverses states at least partially based on the response to sensor data. The server may provide dynamic reallocation of compute resources to resolve demand for classifier training job requests; use of jurisdictional certificates to define data usage and sharing; and data fusion. Applications include manufacturing verification, medical diagnosis and treatment, genomics and viral detection.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-34. (canceled) 35. A system for applying a deep learning neural network to data obtained from one or more sensors on a client system, comprising: a. a client system; b. a network accessible server system linked to the client system, the server system configured to provision an application executable on the client system and allocating computing tasks to the server system, the application causing interaction with the sensors; c. one or more peripherals coupled to sensors for transmitting sensor data from the client system to the server system the server system configure to apply a neural network based classification process to the sensor data. 36. The system of claim 35, wherein the sensors comprise image capture devices and the classification process is scene parsing. 37. The system of claim 35, wherein the sensor data is transmitted by the client system to the server system through interfaces that provide anonymization, encryption, compression and jurisdictional stamping. 38. The system of claim 35, wherein the sensor data comprises a plurality of data points aggregated from a plurality of sensors. 39. The system of claim 35, wherein results of the classification process are transmitted to a ground truther to evaluate model accuracy to implement an active learning process. 40. The system of claim 39, wherein the system further provides a confidence score to the ground truther. 41. The system of claim 35, wherein the server system applies a plurality of neural network simultaneously and provides a list of classifiers. 42. The system of claim 41, wherein the classifiers in the list are provided with confidence values. 43. A method of applying a deep learning neural network to data obtained from one or more sensors on a client system, comprising: a. linking the client system to a network accessible server system; b. configuring the server system to provision an application executable on the client system and allocating computing tasks to the server system, the application causing interaction with the sensors; c. transmitting sensor data from the client system to the server system; d. applying a neural network based classification process to the sensor data. 44. The method of claim 41, wherein the sensors comprise image capture devices and the classification process is scene parsing. 45. The method of claim 41, wherein the sensor data is transmitted by the client system to the server system through interfaces that provide anonymization, encryption, compression and jurisdictional stamping. 46. The method of claim 41, wherein the sensor data comprises a plurality of data points aggregated from a plurality of sensors. 47. The method of claim 41, wherein results of the classification process are transmitted to a ground truther to evaluate model accuracy to implement an active learning process. 48. The method of claim 45, further comprising providing a confidence score to the ground truther. 49. The method of claim 45, wherein the server system applies a plurality of neural network simultaneously and provides a list of classifiers. 50. The method of claim 49, wherein the classifiers in the list are provided with confidence values. 51-72. (canceled)