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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 16 January 2026 has been entered.
Claims 1-8 and 10-18 are pending, claims 1, 4, 11, 14 are amended, and claims 16-18 are newly added.
Response to Arguments
Applicant's arguments filed 12 January 2026 have been fully considered but are moot in view of new grounds of rejection. A new reference, Chiu et al. (US 2023/0051939), is applied to the rejection to address the newly added limitations.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 16 and 18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 16 and 18 require first and second predetermined time periods that are non-overlapping. However, the applicant’s specification indicates that each of the first and second predetermined time periods is “the time since a most recent snore [p. 4, lines 12-13 and p. 6, lines 12-13].” Since both the first and second predetermined time periods are measured from the same starting point, there is no disclosure of non-overlapping time periods.
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.
Claim 16-18 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.
Claim 16 recites the limitation "the second predetermined time period" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the second predetermined time period" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "the second predetermined time period" in line 1. There is insufficient antecedent basis for this limitation in the claim.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5-7, 10-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2017/0087050) in view of Chiu et al. (US 2023/0051939) in view of Sayadi et al. (US 2019/0201268) and further in view of Sundstrom et al. (US 2020/0401944).
[Claims 1, 13-15] Lee discloses a system, a processing system (processing circuit assembly, #15), a method and computer program product, for controlling a pulse oximetry sensor (composite sensor, #12, including photodiodes, #31, of a pulse oximeter) [pars. 0033, 0035], the processing system being configured to:
obtain, from a detector (vibration sensor, #32) different to the pulse oximetry sensor, breathing data responsive to a subject's breathing (vibration of an airway inlet) [pars. 0006, 0018];
process the breathing data to determine one or more breathing characteristics (frequencies are derived from the measured vibration) [par. 0018]; and
control the pulse oximetry sensor responsive to the one or more breathing characteristics by:
processing the one or more breathing characteristics to determine whether the subject is snoring (thresholds of measured vibrations indicate primary or secondary snoring) [par. 0018]; and, in response to a determination that the subject is snoring:
processing the one or more breathing characteristics to identify a type of snoring (primary or secondary snoring) [par. 0018]; and
controlling the pulse oximetry sensor according to the identified type of snoring (means to turn on either only the vibration sensor for subjects with primary snoring only, or both the vibration sensor and the pulse oximeter for subjects having primary snoring and lung disease or secondary snoring) [pars. 0022-0023].
Lee implies the processing system controls the pulse oximetry sensor but the “means” for selectively turning on the pulse oximeter is not explicitly identified as the processing system. Lee further does not disclose processing the one or more breathing characteristics to determine a time since a most recent snore; and in response to a determination that the time since the most recent snore exceeds a first predetermined time period, controlling the pulse oximetry sensor to stop acquiring pulse oximetry data.
Chiu discloses an analogous diagnostic processing system for controlling a pulse oximetry sensor (PPG sensor, #11) wherein an PPG controller (#14) activates (turns “ON”) the pulse oximetry sensor in response to abnormal events detected during sleep (e.g. apnea) and deactivates (turns “OFF”) the pulse oximetry sensor when the abnormal event is no longer detected for a period of time in order to save power [pars. 0027-0028; Fig. 5].
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It would have been obvious to one having ordinary skill in the art before the effective filing date to configured the processing system of Lee to automatically control the pulse oximetry sensor based on predetermined times since the detection of abnormal sleeping events (such a primary and secondary snoring) as suggested by Chiu in order to conserve energy by only activating the pulse oximetry sensor when necessary [Lee: par. 0023].
Lee in view of Chiu does not explicitly disclose the processing system is configured to identify a type of snoring based on an active reinforcement machine learning algorithm.
Sayadi discloses a snore detection system using user-specific training data for a machine learning algorithm so that snore categorization may be tailored to specific users and more accurately detect and categorize snore events by the user [par. 0007].
It would have been obvious to one having ordinary skill in the art before the effective filing date to configure the processing system rendered obvious by Lee in view of Chiu to use user-specific training data from a specific user and a machine learning algorithm in order to more accurately detect and categorize snore events by the user.
Lee in view of Chiu and Sayadi discloses using machine learning but does not specify an active reinforcement learning algorithm.
Sundstrom discloses active reinforcement learning is a known concept in machine learning to optimize decision making of the machine learning [par. 0059].
It would have been obvious to one having ordinary skill in the art before the effective filing date to use an active reinforcement learning algorithm in order to improve decision making and further optimize the automated control of the sensor based on detect snoring.
[Claim 2] Lee discloses the detector is a vibration sensor and Chiu specifically discloses an accelerometer which measures vibration.
[Claim 3] Lee discloses the processing system is configured to, in response to the identified type of snoring being a first type of snoring that occurs during hypopnea: control the pulse oximetry sensor to start acquiring pulse oximetry data (the pulse oximetry data is collected at the time snoring starts which would include snoring caused by hypopnea) [par. 0022].
[Claim 5] Lee discloses the processing system is configured to, in response to the identified type of snoring being a third type of snoring that occurs after an obstruction is resolved: control the pulse oximetry sensor to start acquiring pulse oximetry data (the pulse oximetry data is collected at the time snoring starts which would include snoring of a third type that occurs after obstruction is resolved) [par. 0022].
[Claim 6] Lee as modified does not explicitly disclose the processing system is configured to identity a type of snoring based on subject-specific data obtained during at least one previous sleep session for the subject.
Sayadi discloses a snore detection system using user-specific training data for a machine learning algorithm so that snore categorization may be tailored to specific users and more accurately detect and categorize snore events by the user [par. 0007].
It would have been obvious to one having ordinary skill in the art before the effective filing date to configure the processing system rendered obvious by Lee in view of Chiu to use user-specific training data from a specific user and a machine learning algorithm in order to more accurately detect and categorize snore events by the user.
[Claim 7] Based on the combination of references presented in claim 6, it would have been obvious to one having ordinary skill in the art before the effective filing date to ensure the subject-specific data comprises wherein the subject-specific data comprises features of one or more breathing characteristics for the subject for each of a plurality of types of snoring, wherein the plurality of types of snoring comprise one or more of: a first type of snoring that the subject exhibits during hypopnea events; a second type of snoring that the subject exhibits before an obstructive apnea event; a third type of snoring that the subject exhibits after an obstruction is resolved; and/or a fourth type of snoring that the subject exhibits during regular breathing. Doing so would ensure the most accurate detection and categorization of all snoring events.
[Claim 10] Lee as modified renders obvious the processing system is further configured to control the pulse oximetry sensor by, in response to a determination that the subject is snoring: processing the one or more breathing characteristics to determine a running variance for one or more snore features (the frequency of the measured vibration of the airway inlet) [Lee: par. 0006]; in response to a determination that each running variance exceeds a predetermined running variance threshold (a frequency range of 20 Hz to 500 Hz indicates primary snoring and a frequency range of 500 Hz to 2000 Hz indicates secondary snoring), controlling the pulse oximetry sensor to acquire pulse oximetry data (activated based on the snore detection via frequency); and in response to a determination that the running variance for one or more snore features does not exceed the predetermined threshold, controlling the pulse oximetry sensor not to acquire pulse oximetry data (not activated when snoring not detected via frequency) [Lee: pars. 0006, 0018].
[Claims 11, 17] Lee as modified renders obvious wherein the first predetermined time period is determined based on the subject-specific data obtained during at least on previous sleep session for the subject.
Sayadi discloses a snore detection system using user-specific training data for a machine learning algorithm so that snore categorization may be tailored to specific users and more accurately detect and categorize snore events by the user [par. 0007].
Chiu discloses a vital signs detector (#13) for estimating the respiration rate of a user wherein the respiration rate is sent to the PPG controller (#14) and when the respiration rate is not within normal range, the PPG controller detects a specific event is happening and generates a control signal to activate the PPG sensor [par. 0035].
It would have been obvious to one having ordinary skill in the art before the effective filing date to configure the processing system rendered obvious by modified Lee to use user-specific training data from a specific user and a machine learning algorithm in order to more accurately detect and categorize snore events by the user, as taught by Sayadi, in combination with detected respiration rate, as taught by Chiu, in order to more accurately determine the timing of the occurrence of snoring events in order to determine the first predetermined time period.
[Claim 12] Lee discloses the processing system is configured to control the pulse oximetry sensor by: obtaining pulse oximetry data from the pulse oximetry sensor; processing the pulse oximetry data to determine a measure of oxygen saturation; and controlling the pulse oximetry sensor to stop acquiring pulse oximetry data in response to a determination that the measure of oxygen saturation exceeds an oxygen saturation threshold throughout a third predetermined time period (the reflectance pulse oximeter is configured to start checking on the tissue oxygen saturation at the time snoring starts and then to continue checking at a regular interval, for an example, every five minutes, until the tissue oxygen saturation improves above 92%) [par. 0022].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2017/0087050) in view of Chiu et al. (US 2023/0051939), Sayadi et al. (US 2019/0201268) and Sundstrom et al. (US 2020/0401944), as applied to claim 1 above, and further in view of Zigel et al. (US 2013/0184601).
[Claim 4] Lee in view of Chiu renders obvious controlling the pulse oximeter based on breathing date indicative of snoring type but does not disclose in response to the identified type of snoring being a second type of snoring that occurs before an obstructive apnea event: process the one or more breathing characteristics to determine a time since a most recent snore; and control the pulse oximetry sensor to start acquiring pulse oximetry data in response to a determination that the time since a most recent snore exceeds a second predetermined time period.
Zigel discloses a device and method for diagnosing obstructive sleep apnea [abstract] comprising determining if snores are a group of snores if a time delay between temporally adjacent snores is less than or equal to a predetermined time [par. 0013; claims 1-3].
It would have been obvious to one having ordinary skill in the art before the effective filing date to configure the processing system to use a time delay threshold to group snores as taught by Zigel in order to determine if there is a gap in time large enough to constitute a cessation in breathing since obstructive sleep apnea is determined based on periods of cessation in breathing.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2017/0087050) in view of Chiu et al. (US 2023/0051939), Sayadi et al. (US 2019/0201268) and Sundstrom et al. (US 2020/0401944), as applied to claim 1 above, and further in view of Beckman et al. (US 10,800,040).
[Claim 8] The previously applied references render obvious the use of active reinforcement learning algorithms to measure oxygen desaturation events (snoring) to minimize the on-time of pulse oximetry sensors but do not indicate that the active reinforcement learning algorithm utilizes a reward function based on both the accuracy of oxygen desaturation event detection and the minimization of pulse oximetry sensor activation time.
Beckman discloses reinforcement learning is an area of machine learning that seeks to learn how to make decisions in order to maximize rewards or minimize costs over a period of time. A reinforcement learning system can be used to model the reward function of the task, which can be considered as a model of the goal of the tasks and may be expressed as weighted factors that influence success at task performance [col 7, lines 47-53].
It would have been obvious to one having ordinary skill in the art before the effective filing date to incorporate a reward function into the active reinforcement learning algorithm as taught by Beckman in order to minimize costs (i.e. sensor power use) over a period of measurement time.
Note on Prior Art
Prior art is not applied to claims 16 and 18. However, there is an outstanding new matter rejection under 35 U.S.C. 112(a) presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(1) Young et al. (US 11503013) discloses to save power, the bedside monitor may not transmit any information to the control system until a certain snore pattern is detected [col. 4, lines 46-67]
(2) Tank et al. (US 2022/02111286) discloses by turning off all non-optimal PPG channels during rest, a substantial amount of power may be saved [par. 0081]
(3) Maclaren et al. (US 2023/0230612) discloses PPG data may be captured using a PPG sensor. In some embodiments, PPG data may be continuously captured; in other embodiments, PPG data may only be captured if the analysis is indicative of speech being present to help save power by not having the PPG sensor operating unless necessary [par. 0055].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN J JENNESS whose telephone number is (571)270-5055. The examiner can normally be reached M-F 8:00-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Edward Lefkowitz can be reached at 571-272-2180. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHAN J JENNESS/Supervisory Patent Examiner, Art Unit 3733 15 April 2026
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