A human expert creates sentences in a formal grammar to describe the state of a physical system through aspects of the behavior of such systems. A software process combines these sentences with historical data about physical systems of the same type and uses machine learning to generate a model that detects this state in such systems. These models are able to detect important states of physical systems, such as states that are predictive of future failures, without needing precise guidance from a human user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating a predictive model for operation of a physical system, comprising: obtaining definitions of a formal language capable of describing a plurality of data patterns significant to the operation of the physical system; monitoring sensor data associated with the physical system over a period of time; iteratively: (1) displaying a representation of the monitored sensor data to a user via the interface, (2) receiving, via the interface, a description from the user describing the presented sensor data, (3) identifying one data pattern out of the plurality of data patterns by parsing the description using the definitions of the formal language, and (4) generating an association between the identified data pattern and a portion of the sensor data displayed to the user via the interface; calculating a parameter to be used in a machine learning algorithm based on the generated associations between the data patterns and the different portions of the sensor data; and generating a predictive model for the operation of the physical system by applying the estimated parameter to the machine learning algorithm. 2. The method of claim 1, wherein the physical system comprises at least one of a machinery, an organic matter, a person. 3. The method of claim 1, wherein the description comprises a noun and a verb. 4. The method of claim 1, wherein the description comprises a noun and an adjective. 5. The method of claim 1, wherein calculating the parameter comprising estimating the parameter based on descriptions and characteristics of associated historical sensor data. 6. The method of claim 1, wherein calculating the parameter comprises deriving the parameter from historical sensor data retrieved at different instances in time, wherein a subset of the historical sensor data is associated with a description of failure or suboptimal performance. 7. The method of claim 1, wherein calculating the parameter comprises estimating the parameter from the description associated with sensor data and optimizing the parameter using a machine learning algorithm. 8. The method of claim 1, further comprising: applying the predictive model to a set of current sensor data; and triggering an alert when the predictive model indicates an occurrence of an event. 9. The method of claim 1, further comprising: applying the predictive model to a set of current sensor data; and adjusting an operating parameter of a part within the physical system when the predictive model indicates an occurrence of an event. 10. The method of claim 1, wherein the representation of the sensor data comprises a graphical representation of the sensor data. 11. The method of claim 1, further comprising selecting a feature of the sensor data that is significant to the operation of the physical system. 12. The method of claim 11, wherein estimating a parameter comprises estimating the parameter based on the selected features. 13. The method of claim 1, wherein the parameter is a common characteristic of the portions of sensor data. 14. A predictive model generation system for generating a predictive model for a physical system, comprising: a plurality of sensors configured to obtain environmental data associated with the physical system; a user interface comprising a display device and an input device; a database storing a plurality of data pattern templates; and a predictive engine comprising a processor and memory storing software instructions that when executed by the processor perform the following steps: obtaining definitions of a formal language capable of describing the plurality of data patterns significant to the operation of the physical system, iteratively: (1) retrieving sensor data from the plurality of sensors, (2) configuring the user interface to present a representation of the retrieved sensor data to a user, (3) receiving, via the user interface, a description from the user describing the presented sensor data, (4) identifying one data pattern out of the plurality of data patterns by parsing the description using the definitions of the formal language, and (5) generating an association between the identified data pattern and a portion of the sensor data displayed to the user via the user interface, calculating a parameter to be used in a machine learning algorithm based on the generated associations between the data patterns and the different portions of the sensor data, and generating a predictive model for the operation of the physical system by applying the estimated parameter to the machine learning algorithm. 15. The system of claim 14, wherein the software instructions further perform the step of calculating the parameter by estimating the parameter based on descriptions and characteristics of associated historical sensor data. 16. The system of claim 14, wherein the software instructions further perform the step of calculating the parameter by deriving the parameter from historical sensor data retrieved at different instances in time, wherein a subset of the historical sensor data is associated with a description of failure or suboptimal performance. 17. The system of claim 14, wherein the software instructions further perform the step of calculating the parameter by estimating the parameter from the description associated with sensor data and optimizing the parameter using a machine learning algorithm. 18. The system of claim 14, wherein the software instructions further perform the steps of: applying the predictive model to a set of current sensor data; and triggering an alert when the predictive model indicates an occurrence of an event. 19. The system of claim 14, wherein the software instructions further perform the steps of: applying the predictive model to a set of current sensor data; and adjusting an operating parameter of a part within the physical system when the predictive model indicates an occurrence of an event. 20. The system of claim 1, wherein the parameter is a common characteristic of the portions of sensor data.