A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for using machine-learning techniques to identify attribution of small signal stimulus in noisy response channels, comprising: receiving electronic data records of stimuli data associated with at least one channel, wherein the stimuli data comprises a plurality of event notifications for an event propagated through the channel over a period of time to a plurality of users, the event notifications being associated with a plurality of small signal attributes specified at a sub-channel level of the channel; receiving electronic data records of aggregated response data associated with the event, that captures a plurality of responses, elicited over time, from the users to the event; generating, in a computer, from the stimuli data, a plurality of time series stimuli data vectors from the event notifications propagated over time, and generating, from the aggregated response data, a plurality of time series response data vectors from the responses elicited overtime, generating, using machine-learning techniques in a computer, a small signal correlation engine, to correlate at least one of the time series stimuli data vectors to one of the time series response data vectors, and to generate a plurality of correlation coefficients that identify contributions of the event notifications, including at least one of the small signal attributes, to the aggregated response data; and generating, using machine-learning techniques in a computer, a learning model to receive electronic data records of stimuli and aggregated response data and to simulate variations of the stimuli data to predict user responses using the correlation coefficients, including computing a contribution of at least one of the small signal attributes of at least one of the event notifications. 2. The computer implemented method as set forth in claim 1, further comprising filtering the aggregated response data with a transfer function, h(n), that includes a first portion of the response data vectors and excludes a second portion of the response data vectors. 3. The computer implemented method as set forth in claim 2, wherein the transfer function, h(n), is based on a geographical origin of the responses, the IP address of the users, a response time, or a response channel. 4. The computer-implemented method as set forth in claim 1, wherein generating, using machine-learning techniques in a computer, a small signal correlation engine in accordance with the expression. r = n  ∑ xy - ( ∑ x )  ( ∑ y ) n  ( ∑ x 2 ) - ( ∑ x ) 2  n  ( ∑ y 2 ) - ( ∑ y ) 2 where: x represents components of the time series stimuli data vectors, y represents components of the time series response data vectors, and n is a number of {x, y} pairs used. 5. The computer-implemented method as set forth in claim 1, wherein the small signal attributes comprise sequences of attributes, values and measurements of the event notification. 6. The computer-implemented method as set forth in claim 1, wherein the event notifications comprise marketing messages deployed across television channels. 7. The computer-implemented method as set forth in. claim 6, wherein the small signal attributes comprise attributes that define a television spot. 8. The computer-implemented method as set forth in claim 1, wherein the channels comprise channels associated with an Internet of Things System. 9. A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process, the process comprising: receiving electronic data records of stimuli data associated with at least one channel, wherein the stimuli data comprises a plurality of event notifications for an event propagated through the channel over a period of time to a plurality of users, the event notifications being associated with a plurality of small signal attributes specified at a sub-channel level of the channel; receiving electronic data records of aggregated response data associated with the event, that captures a plurality of responses, elicited over time, from the users to the event; generating, in a computer, from the stimuli data, a plurality of time series stimuli data vectors from the event notifications propagated over time, and generating, from the aggregated response data, a plurality of time series response data vectors from the responses elicited over time; generating, using machine-learning techniques in a computer, a small signal correlation engine, to correlate at least one of the time series stimuli data vectors to one of the time series response data vectors, and to generate a plurality of correlation coefficients that identify contributions of the event notifications, including at least one of the small signal attributes, to the aggregated response data; and generating, using machine-learning techniques in a computer, a learning model to receive electronic data records of stimuli and aggregated response data and to simulate variations of the stimuli data to predict user responses using the correlation coefficients, Including computing a contribution of at least one of the small signal attributes of at least one of the event notifications. 10. The computer program product as set forth in claim 9, further comprising filtering the aggregated response data with a transfer function, h(n), that includes a first portion of the response data vectors and excludes a second portion of the response data vectors. 11. The computer program product as set forth in claim 10, wherein the transfer function, h(n), is based on a geographical origin of the responses, the IP address of the users, a response time, or a response channel. 12. The computer program product as set forth in claim 9, wherein generating, using machine-learning techniques in a computer, a small signal correlation engine in accordance with the expression: r = n  ∑ xy - ( ∑ x )  ( ∑ y ) n  ( ∑ x 2 ) - ( ∑ x ) 2  n  ( ∑ y 2 ) - ( ∑ y ) 2 where: x represents components of the time series stimuli data vectors, y represents components of the time series response data vectors, and n is a number of {x, y} pairs used. 13. The computer program product as set forth in claim 9, wherein the small signal attributes comprise sequences of attributes, values and measurements of the event notification. 14. The computer program product as set forth in claim 9, wherein the event notifications comprise marketing messages deployed across television channels. 15. The computer program product as set forth in claim 14, wherein the small signal attributes comprise attributes that define a television spot. 16. The computer program product as set forth in claim 9, wherein the channels comprise channels associated with an Internet of Things System. 17. A computer system comprising: a computer processor to execute a set of program code instructions; and a memory to hold the program code instructions, in which the program code instructions comprises program code to perform: receiving electronic data records of stimuli data associated with at least one channel, wherein the stimuli data comprises a plurality of event notifications for an event propagated through the channel over a period of time to a plurality of users, the event notifications being associated with a plurality of small signal attributes specified at a sub-channel level of the channel; receiving electronic data records of aggregated response data associated with the event, that captures a plurality of responses, elicited over time, from the users to the event; generating, in a computer, from the stimuli data, a plurality of time series stimuli data vectors from the event notifications propagated over time, and generating, from the aggregated response data, a plurality of time series response data vectors from the responses elicited over time; generating, using machine-learning techniques in a computer, a small signal correlation engine, to correlate at least one of the time series stimuli data vectors to one of the time series response data vectors, and to generate a plurality of correlation coefficients that identify contributions of the event notifications, including at least one of the small signal attributes, to the aggregated response data; and generating, using machine-learning techniques in a computer, a learning model to receive electronic data records of stimuli and aggregated response data and to simulate variations of the stimuli data to predict user responses using the correlation coefficients, including computing a contribution of at least one of the small signal attributes of at least one of the event notifications. 18. The computer system as set forth in claim 17, further comprising filtering the aggregated response data with a transfer function, h(n), based on a geographical origin of the responses, the IP address of the users, a response time, or a response channel. 19. The computer system as set forth in claim 17, wherein the event notifications comprise marketing messages deployed across television channels. 20. The computer system as set forth in claim 19, wherein the small signal attributes comprise attributes that define a television spot.