Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 6 is objected to because of the following informalities: “the raw” should be –-the raw dataset--. Appropriate correction is required.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
With respect to claims 1,8,15:
2A Prong 1:
generate a first input dataset by processing a raw dataset, wherein the processing the raw dataset comprises audio sampling to identify particular waveforms within audio data to determine a structure of the first input dataset indicating one or more features within the raw data (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation such as FFT calculations or other);
determine a first machine learning classifier based on the first input dataset; (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can determine data or select a classifier);
obtain a historical statistical distribution generated based on the first input dataset (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
calculate a statistical distribution based on a first output dataset (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
determine a change in the distribution of values between the historical statistical distribution and the statistical distribution (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
[automatically] determine, based on the first output dataset, a second machine learning classifier (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can determine data or select a classifier);
obtain a second output dataset generated based on the execution of the second machine learning classifier(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
determine a third machine learning classifier based on the second output dataset(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can determine data or select a classifier).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a processor; and memory storing computer-executable instructions that (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
execute the first machine learning classifier to process the first input dataset and determine labels and/or confidence metrics for the features (using a generic classifier, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction);
receive a first output dataset generated based on execution of the first machine learning classifier; (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
execute the second machine learning classifier, wherein the execution of the second machine learning classifier is based on the first output dataset(using a generic classifier, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction);
Claim 15 readable storage medium for storing (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a processor; and memory storing computer-executable instructions that (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
execute the first machine learning classifier to process the first input dataset and determine labels and/or confidence metrics for the features (using a generic classifier, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction);
receive a first output dataset generated based on execution of the first machine learning classifier; (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
execute the second machine learning classifier, wherein the execution of the second machine learning classifier is based on the first output dataset(using a generic classifier, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction);
Claim 15 readable storage medium for storing (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Further, the receiving/transmitting steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer. The claim is not patent eligible.
2, 9, 16. (New) The apparatus of claim 1, the memory storing computer-executable instructions that, when executed by processor, further cause the apparatus to:
identify a location of the raw dataset (Mental process of detecting);
retrieve the raw dataset from the location(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); and
transmit the first input dataset to a cloud processing system hosting the first machine learning classifier(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
3, 10, 17. (New) The apparatus of claim 1, wherein the instructions, when executed by the processor, further cause the apparatus to:
execute the third machine learning classifier, wherein the execution of the third machine learning classifier is based on the second output dataset; and
obtain a third output dataset generated based on the execution of the third machine learning classifier(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation such as FFT calculations or other).
4, 11. (New) The apparatus of claim 1, wherein the instructions, when executed by the processor, further cause the apparatus to:
transmit a notification indicating the change in distribution of values based on the change exceeding a threshold value, wherein the change in distribution of values is between the historical statistical distribution based on the first input dataset and the statistical distribution based on the first output dataset(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
5, 12, 18. (New) The apparatus of claim 1, the memory storing computer-executable instructions that, when executed by processor, further cause the apparatus to:
determine that a portion of the raw dataset is to be used as input by one or more additional machine learning classifiers(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation such as FFT calculations or other); and
based on the determination that the portion of the raw dataset is to be used as input by the one or more additional machine learning classifiers, generate at least a second input dataset associated with the one or more additional machine learning classifiers with the portion of the raw dataset(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation such as FFT calculations or other).
6. (New) The apparatus of claim 1, wherein the instructions, when executed by the processor, further cause the apparatus to format the raw using a first data format(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation such as FFT calculations or other).
7, 14, 20. (New) The apparatus of claim 1, wherein the instructions, when executed by processor, further cause the apparatus to generate an aggregate dataset based on the first output dataset and the second output dataset(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation such as FFT calculations or other).
13, 19. (New) The method of claim 8, further comprising:
transmitting, by the computing device, a notification indicating the change in distribution of values based on the change exceeding a threshold value, wherein the change in distribution of values is between the historical statistical distribution based on the first input dataset and the statistical distribution based on the first output dataset(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation such as FFT calculations or other).
18/360,483
11,755,949
Claims 1, 8, 15
a processor; and
memory
generate a first input dataset by processing a raw dataset, wherein the processing the raw dataset comprises audio sampling to identify particular waveforms within audio data to determine a structure of the first input dataset indicating one or more features within the raw data;
determine a first machine learning classifier based on the first input dataset;
execute the first machine learning classifier to process the first input dataset and determine labels and/or confidence metrics for the features;
obtain a historical statistical distribution generated based on the first input dataset;
calculate a statistical distribution based on a first output dataset;
determine a change in the distribution of values between the historical statistical distribution and the statistical distribution;
receive a first output dataset generated based on execution of the first machine learning classifier;
automatically determine, based on the first output dataset, a second machine learning classifier;
execute the second machine learning classifier, wherein the execution of the second machine learning classifier is based on the first output dataset;
obtain a second output dataset generated based on the execution of the second machine learning classifier; and
determine a third machine learning classifier based on the second output dataset.
a processor; and memory
generate a first input dataset by processing the raw dataset, wherein the first input dataset is formatted using a common data format, wherein the processing the raw dataset comprises determining a structure of the first input dataset indicating one or more features within the raw data;
determine a first machine learning classifier based on the first input dataset;
trigger execution the first machine learning classifier to process the first input dataset and determine labels and/or confidence metrics for the features;
obtain a historical statistical distribution generated based on the first input dataset;
calculate a statistical distribution based on a first output dataset;
determine a change in the distribution of values between the historical statistical distribution and the statistical distribution;
receive a first output dataset generated based on execution of the first machine learning classifier;
automatically determine, based on the first output dataset, a second machine learning classifier;
trigger execution the second machine learning classifier, wherein the execution of the second machine learning classifier is based on the first output dataset;
obtain a second output dataset generated based on the execution of the second machine learning classifier;
and
determine a third machine learning classifier based on the second output dataset.
2,9,16
2
3,10,17
4, 11
Not rejected under DP
5,12,18
7
8
Not rejected
13,19
Not rejected
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bharitkar (WO 2018/199997) teaches processing audio waves using classifiers (“An audio signal classifier including a feature extractor to extract metadata from an audio signal, the metadata defining a plurality of features of the audio signal, the feature extractor to generate a feature vector including selected features of the audio signal, the selected features including a duration of the audio signal, and each selected feature having a feature value. A machine learning model trained to classify the audio signal as one of a plurality of audio signal classes based on the feature vector.”, abstract, 0006, 0022, 0024).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R VINCENT whose telephone number is (571)272-3080. The examiner can normally be reached ~Mon-Fri 12-8:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at 5712703428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123
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