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 Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 16, 17, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Karoly (US 20220095993 A1).
Regarding Claim 1, Karoly teaches:
determining heart rate variability (HRV) data of a subject over a period of time that includes time segments; (¶11 The non-EEG physiological data may comprises cardiac output recorded over the first time period. The cardiac output may comprise one or more of: (i) heart rate, and (ii) heart rate variability; ¶34 suitable non-EEG physiological data may include cardiac data, such as heart rate and/or heart rate variation; ¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
generating, from the HRV data, values for an HRV feature, wherein each one of the values is associated with one of the time segments; (¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
generating, based on the values of the HRV feature and the time segments, a polar representation of the HRV data; and (¶123 FIGS. 10A to 10I show the phase locking of epileptic activity to circadian cycles of heart rate, HRV and time of day. Each figure shows individuals (arrows) with significant phase locking of events to their circadian cycle of heart rate (FIGS. 10A to 10C), HRV (FIGS. 10D to 10F) or time of day (FIGS. 10G to 10I). The length of the arrows indicates the strength of phase locking (radial axis, between 0 and 1), while the direction indicates the preferred phase/time (polar axis).)
outputting the polar representation as an image. (¶31 FIG. 10 graphically illustrates epileptic activity locked to circadian cycles of heart rate, HRV and time of day;)
Regarding Claim 2, Karoly teaches:
determining subject data of the subject, the subject data comprising at least one of demographic data or clinical data; and (¶123 FIGS. 10A to 10I show the phase locking of epileptic activity to circadian cycles of heart rate, HRV and time of day. Each figure shows individuals (arrows) with significant phase locking of events to their circadian cycle of heart rate (FIGS. 10A to 10C), HRV (FIGS. 10D to 10F) or time of day (FIGS. 10G to 10I).; epileptic events fall within the broadest reasonable interpretation of the scope of "clinical data")
representing the subject data in the polar representation, wherein the image shows the values of HRV feature and values that represent the subject data. (Figs. 10A-10I, notably Fig. 10D-F which are directed to HRV)
Regarding Claim 4, Karoly teaches:
segmenting the HRV data into HRV datasets that correspond to the time segments, (¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
wherein each value of the HRV feature corresponds to a time segment and is generated from an HRV dataset that corresponds to the time segment. (¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
Regarding Claim 16, Karoly teaches:
one or more processors; and one or more memory storing computer-readable instructions that, upon execution by the one or more processors, configure the computer system to: (¶14 one or more processors, and memory comprising computer executable instructions, which when executed by the one or more processors, causes the system to perform any one of the described methods.)
determine heart rate variability (HRV) data of a subject over a period of time that includes time segments; (¶11 The non-EEG physiological data may comprises cardiac output recorded over the first time period. The cardiac output may comprise one or more of: (i) heart rate, and (ii) heart rate variability; ¶34 suitable non-EEG physiological data may include cardiac data, such as heart rate and/or heart rate variation; ¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
generate, from the HRV data, values for an HRV feature, wherein each one of the values is associated with one of the time segments; (¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
generate, based on the values of the HRV feature and the time segments, a polar representation of the HRV data; and (¶123 FIGS. 10A to 10I show the phase locking of epileptic activity to circadian cycles of heart rate, HRV and time of day. Each figure shows individuals (arrows) with significant phase locking of events to their circadian cycle of heart rate (FIGS. 10A to 10C), HRV (FIGS. 10D to 10F) or time of day (FIGS. 10G to 10I). The length of the arrows indicates the strength of phase locking (radial axis, between 0 and 1), while the direction indicates the preferred phase/time (polar axis).)
output the polar representation as an image. (¶31 FIG. 10 graphically illustrates epileptic activity locked to circadian cycles of heart rate, HRV and time of day;)
Regarding Claim 17:
Claim 17 is substantially similar to claim 2 and is rejected under the same grounds as those set forth above for claim 2.
Regarding Claim 19, Karoly teaches:
One or more computer-readable storage media storing instructions that, upon execution on a computer system, cause the computer system to perform operations comprising: (¶14 one or more processors, and memory comprising computer executable instructions, which when executed by the one or more processors, causes the system to perform any one of the described methods.)
determining heart rate variability (HRV) data of a subject over a period of time that includes time segments; (¶11 The non-EEG physiological data may comprises cardiac output recorded over the first time period. The cardiac output may comprise one or more of: (i) heart rate, and (ii) heart rate variability; ¶34 suitable non-EEG physiological data may include cardiac data, such as heart rate and/or heart rate variation; ¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
generating, from the HRV data, values of an HRV feature, wherein each one of the values is associated with one of the time segments; (¶105 Following peak detection, a number of beats per minute, with a 5 s sliding window were computed. Heart rate variability (HRV) was also investigated by computing the variance of the peak-to-peak intervals within a 1 m window, updated every 5 s.)
generating, based on the values of the HRV feature and the time segments, a polar representation of the HRV data; and (¶123 FIGS. 10A to 10I show the phase locking of epileptic activity to circadian cycles of heart rate, HRV and time of day. Each figure shows individuals (arrows) with significant phase locking of events to their circadian cycle of heart rate (FIGS. 10A to 10C), HRV (FIGS. 10D to 10F) or time of day (FIGS. 10G to 10I). The length of the arrows indicates the strength of phase locking (radial axis, between 0 and 1), while the direction indicates the preferred phase/time (polar axis).)
outputting the polar representation as an image. (¶31 FIG. 10 graphically illustrates epileptic activity locked to circadian cycles of heart rate, HRV and time of day;)
Claim Rejections - 35 USC § 103
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.
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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Karoly (US 20220095993 A1) in view of Yakida (JP 2021178174 A).
Regarding Claim 3:
Karoly teaches:
wherein edges of the polar representation correspond to HRV feature variations between the time segments, (¶123 Each figure shows individuals (arrows) with significant phase locking of events to their ... HRV (FIGS. 10D to 10F) ... The length of the arrows indicates the strength of phase locking (radial axis, between 0 and 1), while the direction indicates the preferred phase/time (polar axis).)
wherein the image includes lines that represent time segments, and (¶123 the direction indicates the preferred phase/time (polar axis).)
Karoly does not teach in particular, but Yakida teaches:
wherein areas between the lines correspond to color-coded values that represent the subject data. (Each generated vector is output so that each vector can be identified on the display unit of the wearable terminal worn by the subject, for example. Being identifiable is not limited as long as it can be visually identified by a person such as a subject or a person who manages the health of the subject. For example, it can be identified by presenting different colors of each vector. Further, when each vector is out of the reference range, for example, warning information indicating that the circadian rhythm is disturbed may be output. The warning information may be textual information, a mark such as an exclamation mark, or an alarm sound.; Fig. 3)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the color coding in the polar plot of Yakida, as well as the other features regarding display and organization of its polar plots, to the polar plots of Karoly, so that the data can be visually identified more easily by a person such as a subject or a person who manages the health of the subject (Yakida, "it can be visually identified by a person such as a subject or a person who manages the health of the subject.", same paragraph as above cited passage)
Allowable Subject Matter
Claims 5-15, 18, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 5 recites: “converting the polar representation into an edge image; converting the edge image into a filled image; and converting the filled image into a scaled image based on a set of maximum possible values that the scaled image can show, wherein the image is outputted as the scaled image.”. In the context of the previous claim limitations characterizing the polar representation from claims 1 and 4, the prior art does not disclose a polar image with HRV values corresponding to time segments being converted to scaled and filled edge images.
Claim 6 recites “masking each time segment of the HRV image to generate a corresponding HRV mask;
masking each time segment of the subject image to generate a corresponding subject mask;
scaling one or more of the subject masks and/or one or more of the HRV masks; and
combining, after the scaling, the HRV masks and the subject masks to generate HRV-subject masks, wherein the image is generated based on the HRV-subject masks.”
This masking is not taught or suggested in the prior art when applied to the HRV images in the context of the claim, which requires that the be polar representations as set forth in claim 1. Claim 7 depends upon claim 6 and inherits these limitations.
Claims 8, 18, and 20 require “generating an input to a machine learning model based on the image or the polar representation”. While determining a heart failure prediction based on output of a machine learning model is disclosed by innumerable references, the above cited language requires that the input to the machine learning model be either the polar representation or the image of that polar representation, and the prior art does not teach or suggest inputting HRV polar representations into machine learning models (rather, HRV data features are usually directly input, such as in ¶103 of Karoly, though this is done for seizures rather than heart failure)
Claims 9-15 inherit the above discussed feature of claim 8, and distinguish over the art for the same reasons.
The closest prior art to these dependent claims is cited below, although the most pertinent remains the Karoly reference relied upon in the above 35 USC 102 and 103 rejections:
US 20180192941 A1 discusses scatter plots of R-R intervals for heart waveforms that are displayed in a polar coordinate system scatter plot, with different axis usage than that of Karoly or Yakida (¶86 FIG. 4B illustrates a scatter plot of RR intervals 421 within a respiratory cycle in a polar coordinate system. Each respiration-synchronized RR interval is represented by a point in the polar coordinate system, as defined by a radius r and an angle θ. The radius r represents the RR interval (e.g., in milliseconds), and the angle θ represents the respiratory phase angle between 0 and 360 degrees.; ¶86 the RR interval variability 453 during inspiration is substantially smaller than the RR interval variability 454 during expiration).
Migeotte (Pierre-Franois, M., & Verbandt, Y. (1999). A novel algorithm for the heart rate variability analysis of short-term recordings: polar representation of respiratory sinus arrhythmia. Computers and biomedical research, 32(1), 56-66.) discloses time domain polar analysis of HRV with polar representations (p.57, we present a new time domain method for the analysis of HRV ...; p.59, Hence, we can apply a polar coordinate representation where the radial coordinate r is the RRI and the angular coordinate u is the phase in breath cycle. ; p.60 Fig. 2, The radius points in the direction of the shortest RRI.; p.62 Note that the polar representation which is presented here takes into account every heartbeat).
US 20170156619 A1 discloses polar representations of HRV and other heart data that can be used for actively monitoring a patient’s heart (¶56 FIG. 7 depicts heart rate and time derivative of heart rate for a healthy subject. The plot on the left provides the doctor with a comprehensive picture of the patient's heart rate and HRV.; ¶58 heart rate clocks can be used for monitoring heart rate variability)
Wang (Wang, F., Tanaka, M., & Chonan, S. (2008, January). Visualization of short-term HRV as an index for mental stress evaluation. In ICMIT 2007: Mechatronics, MEMS, and Smart Materials (Vol. 6794, pp. 812-817). SPIE.) discloses applications of HRV based inferences to analyzing brain activity that involve polar representations of HRV associated data.
Goshvarpour (Goshvarpour, A., & Goshvarpour, A. (2021). Asymmetry of lagged Poincare plot in heart rate signals during meditation. Journal of traditional and complementary medicine, 11(1), 16-21.) disclose a further way of structuring a polar plot to convey HRV and heart rate information, this time in a lagged Poincare plot.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BIJAN MAPAR whose telephone number is (571)270-3674. The examiner can normally be reached Monday - Thursday, 11:00-8:30.
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/BIJAN MAPAR/ Primary Examiner, Art Unit 2189