DETAILED ACTION
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
Claims 1-2, 7-8, and 13-14 are objected to because of the following informalities:
Claim 1 recites, “wherein the plurality of features comprising:” in lines 16-17. Examiner suggests amending the claims to recite, “wherein the plurality of features comprises[[ing]]:”
Claim 2 recites, “wherein the abnormal class indicates the unseen subject suffering…” in lines 7-8. Examiner suggests amending the claims to recite, “wherein the abnormal class indicates the unseen subject is suffering…”
Claim 7 recites, “wherein the plurality of features comprising:” in line 14. Examiner suggests amending the claims to recite, “wherein the plurality of features comprises[[ing]]:”
Claim 8 recites, “wherein the abnormal class indicates the unseen subject suffering…” in lines 2-3. Examiner suggests amending the claims to recite, “wherein the abnormal class indicates the unseen subject is suffering…”
Claim 13 recites, “wherein the plurality of features comprising:” in lines 14-15. Examiner suggests amending the claims to recite, “wherein the plurality of features comprises[[ing]]:”
Claim 14 recites, “wherein the abnormal class indicates the unseen subject suffering…” in lines 4-5. Examiner suggests amending the claims to recite, “wherein the abnormal class indicates the unseen subject is suffering…”
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 7, and 13, the claims recite, “deriving via the one or more hardware processors, a plurality of features from at least one of the RAW data and the TSA data”, but then proceeds to recite, “a non-linearity feature and a Chebyshev distance feature for the RAW data and the TSA data”, and “a serial correlation feature, a skewness feature and a kurtosis feature for the RAW data and the TSA data”. It is unclear as to whether or not the plurality of features are derived from at least one of the RAW and TSA data, or if the claim requires some of the plurality of features to be derived from both the RAW and TSA data. Dependent claims inherit the same deficiencies.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of determining cardiac abnormalities) without significantly more.
Step 1
The claimed invention in claims 1-18 are directed to statutory subject matter as the claims recite a method/systems for determining cardiac abnormalities.
Step 2A, Prong One
Regarding claims 1-18, the recited steps are directed to mental processes of performing concepts in a human mind or by a human using a pen and paper (See MPEP 2106.05(a)(2) subsection (III)).
Claims 1, 7, and 13, recite the steps of:
segmenting via one or more hardware processors, time series data associated with multi-lead electrocardiogram (ECG) signals captured for each of a plurality of subjects, into a plurality of overlapping windows
decomposing via the one or more hardware processors, the time series data associated with each of the plurality of overlapping windows to generate raw (RAW) data comprising windowed decomposed time series
applying via the one or more hardware processors, de-trending and de-seasonalizing on the windowed decomposed time series data to generate Trend and Seasonally Adjusted (TSA) data
deriving via the one or more hardware processors, a plurality of features from at least one of the RAW data and the TSA data, wherein the plurality of features comprising:
a chaos feature for the RAW data providing a uni-dimensional measure of the cardiac abnormalities present in each windowed decomposed time series
a set of chaos-related statistical features comprising,
a non-linearity feature and a Chebyshev distance feature for the RAW data and the TSA data, and
a spectral flatness feature and a self-similarity feature for the RAW data, to add multiple dimensions to the chaos feature for generating a holistic view of the cardiac abnormalities
a set of statistical features comprising,
a serial correlation feature, a skewness feature and a kurtosis feature for the RAW data and the TSA data,
a trend feature and a seasonality feature for the TSA data, and
a periodicity feature for the RAW data, providing statistical distribution of the cardiac abnormalities
identifying via the one or more hardware processors, a set of significant features from among the plurality of features using a feature importance technique
training via the one or more hardware processors, a chaos-based classification model on the set of significant features derived for each of the plurality of subject to classify the plurality of subjects into one of an abnormal class and a normal class.
As currently written, steps 1-6 are a process, as drafted, that can be performed by a human mind (including an observation, evaluation, and judgment) under the broadest reasonable interpretation but for the recitation of generic computing components. For example, these limitations recited in claims 1-18 are nothing more than a medical professional analyzing gathered data using generic computing components.
Step 2A, Prong Two
For claims 1-18, the judicial exception is not integrated into a practical application. For claims 1, 7, and 13, the additional limitation of “one or more hardware processors”, “a chaos-based classification model”, “a memory”, and “one or more input/output (I/O) interfaces” are recited at a high level of generality and amount to nothing more than parts of a generic computer. The instant disclosure teaches the hardware processors being implemented as generic computer components such as microprocessors and implementing the claimed system on a laptop computer [0029]. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into a practical application.
Step 2B
The recitation of the above-identified additional limitations of “one or more hardware processors”, “a memory”, and “one or more input/output (I/O) interfaces” in claims 1, 7, and 13 amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Dependent claims 2-6, 8-12, and 14-18 recite limitations that are further directed towards the abstract idea and do not introduce any additional elements which amount to significantly more under the Step 2A prong 2 and Step 2B analyses.
Examiner’s Note Regarding Prior Art
While claims 1-18 have been rejected under 35 USC 101 as discussed above, Examiner has not found any prior art that teaches or suggests in combination the limitations recited in claims 1, 7, and 13. Specifically, Examiner has not found any references that teach deriving each of the claimed plurality of features from at least one of the RAW data and the TSA data.
Houben (US 2006/0167364) discloses a processor implemented method for determining cardiac abnormalities, the method comprising:
Segmenting (Fig. 5: R wave detection 32) via one or more hardware processors ([0007] processor), time series data associated with multi-lead electrocardiogram (ECG) signals captured for each of a plurality of subjects (Fig. 5: ECG 30), into a plurality of overlapping windows [0036];
decomposing (Fig. 5: beat extraction 34) via the one or more hardware processors, the time series data associated with each of the plurality of overlapping windows to generate raw (RAW) data comprising windowed decomposed time series [0036]; and
applying via the one or more hardware processors, de-trending and de-seasonalizing (Fig. 5: time alignment and detrending 36) on the windowed decomposed time series data to generate Trend and Seasonally Adjusted (TSA) data [0036];
deriving via the one or more hardware processors, a plurality of features from at least one of the RAW data and the TSA data [0036], wherein the plurality of features comprising:
a chaos feature (Fig. 5: signal averaging 38) for the RAW data providing a uni-dimensional measure of the cardiac abnormalities present in each windowed decomposed time series ([0036] ensemble averaging).
However, Houben fails to disclose:
a set of chaos-related statistical features comprising,
a non-linearity feature and a Chebyshev distance feature for the RAW data and the TSA data, and
a spectral flatness feature and a self-similarity feature for the RAW data, to add multiple dimensions to the chaos feature for generating a holistic view of the cardiac abnormalities; and
a set of statistical features comprising,
a serial correlation feature, a skewness feature and a kurtosis feature for the RAW data and the TSA data,
a trend feature and a seasonality feature for the TSA data, and
a periodicity feature for the RAW data, providing statistical distribution of the cardiac abnormalities;
identifying via the one or more hardware processors, a set of significant features from among the plurality of features using a feature importance technique; and
training via the one or more hardware processors, a chaos-based classification model on the set of significant features derived for each of the plurality of subject to classify the plurality of subjects into one of an abnormal class and a normal class.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Houben (US 2006/0167364) is directed towards detecting cardiac arrhythmia using algorithms.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLOW GRACE WELCH whose telephone number is (703)756-1596. The examiner can normally be reached Usually M-F 8:00am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLOW GRACE WELCH/Examiner, Art Unit 3792
/William J Levicky/ Primary Examiner, Art Unit 3796