CTNF 18/819,100 CTNF 84406 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. 07-04-01 AIA 07-04 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, specifically independent claims 1, 15 & 20, is directed to an abstract idea without significantly more. Please see the below analysis providing the details as to why the invention is directed towards non-statutory subject matter. Step 1: Claims 1 & 12 are directed towards a system, which is a product, i.e. a statutory category of invention. Claim 13 is directed towards a computer-implemented method, i.e. a statutory category of invention. Claim 20 is directed towards a computer-readable data storage medium comprising software, i.e. a statutory category of invention. Step 2A, Prong 1: Claim 1 recites the method steps of: “…receive an electrooculography (EOG) signal…” “…identify…a subset of one or more other samples in the EOG signal…” “…using temporal information to identify temporal dependencies…defined by training of the machine learning algorithm…” “…estimate a sleep stage associated with the target sample…” retrieve one or more second electrocardiogram waveforms…” Claim 12 recites the method steps of: “…receive a training EOG signal…” “…receiving a sleep stages reference input…” “…process…samples of the training EOG signal and the sleep stages reference input to determine temporal dependencies…” “…generate the temporal information…” “…identify…a subset of one or more other samples in the EOG signal…” Claim 13 recites the method steps of: “…receive an electrooculography (EOG) signal…” “…identify…a subset of one or more other samples in the EOG signal…” “…using temporal information to identify temporal dependencies…defined by training of the machine learning algorithm…” “…estimate a sleep stage associated with the target sample…” These limitations, under their broadest interpretation, fall within the mental processes (i.e. receive, identify, etc.) and mathematical concepts (i.e. determining, estimate, etc). It would be practical, but for the recitation “at least one processing device” to perform the steps in a human’s mind, or with a pen and paper, to utilize the claimed signals. Step 2A, Prong 2: The claims as a whole fails to integrate the abstract idea into a practical application. Claim 1 recites the following additional elements, which for the reasons set forth below, do not integrate the abstract idea into a practical application. Claims 1 & 12-13 “…EOG electrode…” which is directed to data gathering, see MPEP 2106.05(g). “… processing system…” which is directed to mere instructions to apply an exception, see MPEP 2106.05(f). Therefore, the claims fail to integrate the abstract idea into a practical application. The examiner also notes that the additional elements recited in claims do not apply or use the judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition. The claim is silent to providing any treatment at all to a patient. Step 2B: The claims as a whole fails to recite an inventive concept. The additional elements, when considered individually and in combination, do not recite significantly more than the abstract idea for the reasons as set forth above in Step 2A, Prong 2. Upon re-evaluating the limitation that was previously identified as insignificant extra-solution activity in Step 2A, Prong 2, the following evidence to show that the limitation is well-understood, routine and conventional: real-time discrete data obtained from a medical device/data previously collected from a medical device (i.e. body surface/unipolar electrodes) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; Receiving 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). producing at said computer processor a human-readable output (i.e. processor) of the analysis of the gathered data, this is also WURC, as evidenced by Electric Power Group, LLC v. Alstom S.A., 830F.3d 1350, 119 USPQ2d 1739 (Fed.Cir. 2016), which discusses “conventional computer, network, and display technology” and states that “nothing in the patent contains any suggestion that the displays needed for that purpose are anything but readily available. We have repeatedly held that such invocations of computers and networks that are not even arguably inventive are “insufficient to pass the test of an inventive concept in the application” of an abstract idea”.” Similarly, there is nothing in Applicant’s specification that indicates that the device that is “producing at said computer processor a human-readable output indicating” the findings of the analysis is anything but readily available. Therefore, the claims fail to recite significantly more than the abstract idea and claims 9-28 are rejected under 35 U.S.C 101. The limitations of the dependent claims 2-11 & 14-15 further defines steps of identifying the sample subset, estimating a sleep stag, etc. which further limit claim limitations already indicated above as being directed to an abstract idea. Therefore, the above claims are directed to patient-ineligible subject matter. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim (s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ayers et al. (10,827,972) in view of Tran et al. (US 2020/0077892) . 1. Ayers et al. teaches: determining sleep stages from physiological sleep-session information including physiological biometric parameters derived from measured physiological signals; col 4. Ayers et al. further teaches determining wake, N1-N3, and REM sleep stages from processed physiological data and generating sleep-stage outputs representative thereof; col 5, lines 1-14 Tran et al. teaches: physiological monitoring utilizing EEG or EOG physiological signals during sleep diagnostic monitoring; [0061]. Tran et al. further teaches predictive machine-learning analysis using time-series and temporal Bayesian-network processing of physiological data over time; [0090]. Under BRI, Tran’s time-series and temporal Bayesian-network analysis teaches identifying temporal dependencies among physiological signal samples and utilizing temporal information responsive thereto during predictive analysis. Ayers’ processing of physiological biometric information derived from measured physiological signals teaches utilizing sampled physiological signal information during sleep-stage determination. It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate Tran’s known temporal modeling techniques into Ayers’ physiological sleep-stage determination system in order to improve temporal analysis of physiological signal relationship during sleep-stage estimation. Applying known temporal signal-analysis techniques to known physiological monitoring systems represents the predictable use of prior-art elements according to their established function, consistent with KSR. 2. Tran et al. teaches predictive modeling identifying relationships among physiological signal samples over time using temporal Bayesian-network reasoning; [0090]. Under BRI, such predictive relationship analysis teaches identifying related sample subsets using attention-based processing. 3. Tran et al. teaches representing physiological parameters in distinct dimensions within predictive temporal models and analyzing temporal relationships among samples; [0090]. Under BRI, such temporal and dimensional representations teach positional encoding including relative and absolute temporal positioning among samples. 4. Tran et al. teaches learning temporal relationships using time-series analysis and temporal Bayesian-network reasoning across varying temporal intervals; [0090]. Under BRI, such temporal modeling teaches learning temporal information across arbitrary time scales. 5. Ayers et al. teaches estimating sleep stages from physiological sleep-session information and derived physiological biometric parameters; col. 4 Trans et al. teaches temporal analysis of physiological signal relationships using time-series and Bayesian-network processing; [0090]. Under BRI, Tran et al. teaches determining dependencies between timing relationships of physiological signal samples within a monitored session. 6. Ayers et al. teaches determining sleep stages during sleep session using physiological sleep-session information; col 4. Tran et al. teaches temporal analysis of physiological data over monitored time intervals; [0090]. Under BRI, such temporal analysis teaches estimating sleep stages responsive to timing of a target sample within the monitored session. 7. Tran et al. teaches generating predictive characterizing information from physiological signal dimensions and temporal relationships among physiological samples; [0090]. Under BRI, such predictive characterizations teaches identifying related sample subsets sharing matching characterizing information. 8. Tran et al. teaches predictive machine-learning models utilizing temporal Bayesian-network reasoning and temporal predictive analysis; [0090]. It would have been obvious to utilize known neural-network architectures, including transformer-based architectures, as predictable alternative machine-learning implementations for temporal dependency analysis, consistent with KSR. 9. Tran et al. teaches monitoring EOG physiological sleep-monitoring signals during deep sleep diagnostic analysis; [0061]. Under BRI, utilizing EOG physiological signals for sleep-stage analysis teaches determining sleep stages using only EOG physiological signal inputs. 10. Ayers et al teaches processing physiological sleep-session information after collection of the physiological data during sleep session; col 4. Under BRI, such post-session processing teaches receiving the physiological signal after completion of the sleep session. 11. Ayers et al. teaches generating sleep-stage outputs including wake, N1-N3 and REM sleep stages; col 5., lines 1-14 Under BRI, ordered sleep-stage outputs teaches generating a hypnogram. 12. Claim 12 is rejected for reasons similar to claim 1. Ayers et al. teaches receiving physiological sleep-session information and generating sleep-stage outputs, while Tran et al. teaches temporal analysis of EEG/EOG physiological signals using time-series and Bayesian-network reasoning (Ayers, col. 4-col. 5, lines 1-14; Tran [0061], [0090]). Under BRI, Tran teaches determining temporal dependencies among physiological signal samples and generating temporal information responsive thereto. It would have been obvious to one having ordinary skill in the art to incorporate Tran’s temporal dependency modeling into Ayers’ sleep-stage determination framework to improve temporal physiological signal analysis, consistent with KSR. 13. Claim 13 is rejected for reasons similar to claim 1. Ayers et al. teaches estimating sleep stages from physiological sleep-session information, while Tran et al. teaches predictive time-series and temporal Bayesian-network analysis of physiological signal samples over time (Ayers, col. 4-col. 5, lines 1-14; Tran [0061], [0090]). Under BRI, Tran et al. teaches identifying related sample subsets using temporal dependency information during predictive analysis. It would have been obvious to incorporate Tran’s temporal dependency analysis into Ayers’ sleep-stage estimation process to improve temporal physiological signal analysis and predictive sleep-stage classification, consistent with KSR. 14. Claim 14 is rejected for reasons similar to claim 5. Tran et al. teaches determining timing dependencies among physiological signal samples using time-series and temporal Bayesian-network analysis; [0090]. 15. Claim 15 is rejected for reasons similar to claim 13 because the computer program product performs the method of claim 13. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE F JOHNSON whose telephone number is (571)270-5040. The examiner can normally be reached Monday-Friday 8:00am-5:00pm EST. 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, David Hamaoui can be reached at 571-270-5625. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NICOLE F JOHNSON/Primary Examiner, Art Unit 3796 Application/Control Number: 18/819,100 Page 2 Art Unit: 3796 Application/Control Number: 18/819,100 Page 3 Art Unit: 3796 Application/Control Number: 18/819,100 Page 4 Art Unit: 3796 Application/Control Number: 18/819,100 Page 5 Art Unit: 3796 Application/Control Number: 18/819,100 Page 6 Art Unit: 3796 Application/Control Number: 18/819,100 Page 7 Art Unit: 3796 Application/Control Number: 18/819,100 Page 8 Art Unit: 3796 Application/Control Number: 18/819,100 Page 9 Art Unit: 3796 Application/Control Number: 18/819,100 Page 10 Art Unit: 3796 Application/Control Number: 18/819,100 Page 11 Art Unit: 3796
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