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 § 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(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 8-20 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.
In regards to claim 8 it is unclear if the “a machine learning model” in line 13 is the same or different than the “a machine learning model” stored in the memory in lines 4-5. It is unclear if the “machine learning model” being executed in lines 14 and 16 is the one in lines 4-5, or the one in line 13. The same issue is present in claim 13. “it is unclear if the “a machine learning model” in in the claim is the same or different than the “a machine learning model” stored in the memory in lines 4-5 of claim 8. If it’s the machine learning model that’s stored on the memory of the computing device, the it is unclear how is it executed on some other device (edge computing device, mobile phone device, server).
In claim 15 it is unclear what is meant by “machine learning model being executed by a computing device for the portable pulse oximetry device”. It is unclear how a machine learning model can be executed for a pulse oximetry device by a computing device, when the computing device is already part of the pulse oximetry device. It is recommended that the claim be amended to say “the machine learning model being executed by a computing device of the portable pulse oximetry device”.
Claims not explicitly rejected above are rejected because they depend from claims rejected above as indefinite
Claims 2 and 14 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 2 fails to further limit claim 1,Claim 1 teaches that “the pulse oximetry device is a high resolution continuous pulse oximetry device that has a signal resolution of at least 0.1% for an oxygen saturation level measurement” and claim 2 teaches the same thing. Claim 14 fails to further limit claim 8. Claim 8 teaches “a sensor that includes a light detector and a light emitting diode” and claim 14 teaches the same thing. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a concrete thing, consisting of parts, including a processor and memory. Thus, the claim is directed to a machine, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The step performed by the processor of generating oximetry characteristics based at least in part on the pulse oximetry data and a plurality of parameter thresholds, the oximetry characteristics comprising a plurality processed parameters sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites selecting a plurality of sleep apnea indicators based at least in part on a sleep apnea profile associated with a patient, the patient data, and the oximetry characteristics. This step is also drawn to an abstract idea in the form of a mental process. Claim 1 recites training machine learning module based on the sleep apnea indicators and deploying the machine learning model to the pulse oximetry device, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The training and deploying of the machine learning module does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the trained machine learning module or the deployment of said model, nor does the method use a particular machine to perform the Abstract Idea.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of receiving patient data that comprises at least one of demographic or clinical information associated with a patient and receiving pulse oximetry data that was measured by a pulse oximetry device worn by the patient. Obtaining data is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the receiving steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claims 8 and 15. Claim 8 does not include a step of training the machine learning module, instead in incudes a step of using a machine learning module to classify the sleep apnea indicators. which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). Claim 15 is a method that does not include a step of training the machine learning module, instead in incudes a step of using a machine learning module to classify the sleep apnea indicators, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data gathering, the training of the machine learning module, and displaying a classification. The gathering, training, and displaying steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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.
Claims 1-4, 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20190076098 A1- previously cited) in view of Shariff (US 20160302671 A1- previously cited), in view of John (US 20040079372 A1- previously cited), in view of Baker Jr (US 20100292548 A1- previously cited), further in view of Shelton (US 20220233119 A1).
In regards to claim 1, Li teaches a system, comprising: a computing device that comprises a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least ([0041] and [0047-0048]):
receive patient data that comprises at least one of demographic or clinical information associated with a patient ([0015]);
receive pulse oximetry data that was measured by a pulse oximetry device worn by the patient ([0014]);
generate oximetry characteristics based at least in part on the pulse oximetry data and a plurality of parameter thresholds, the oximetry characteristics comprising a plurality processed parameters ([0022]);
and training a machine learning model for a sleep apnea prediction, wherein the sleep apnea prediction representing an apnea classification for the patient ([0026] sleep apnea is a type of sleep disordered breathing, “these neural networks were trained based on a set of training data that included records corresponding to a plurality of persons and that included, for each person, information about at least one metric descriptive of blood oxygen saturation during a clinical assessment and at least one health-related status”).
Li fails to teach selecting a plurality of sleep apnea indicators based at least in part on a sleep apnea profile associated with a patient, the patient data, and the oximetry characteristics and using those indicators to train the machine learning module. Li teaches using data from clinical assessments from a plurality of persons ([0026]). Shariff teaches training a machine learning module using a patient’s own labeled historical patient data (Shariff [0054 and 0063]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the training data of Li to include labeled historical data (sleep apnea profile data) specific to the patient. Doing so would merely be combining prior art elements in order to obtain the predictable result of creating a patient specific machine learning module. MPEP 2143.I
Li in view of Shariff fails to teach selecting a plurality of sleep apnea indicators based at least in part on the patient data, and the oximetry characteristics. John teaches analyzing a patient’s data at the time of the measurements to create a self-norm baseline for the patient (John [0016]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the oximetry characteristics and demographic inputs of Li to create a self-norm baseline for a patient and labeling that baseline based on the severity of apnea the patient is known to be experiencing in order to create labeled training data. Doing so would merely be combining prior art elements in order to obtain the predictable result of optimizing a patient specific machine learning module. MPEP 2143.I
Li in view of Shariff, further in view of John fails to teach a device, wherein the pulse oximetry device is a high resolution continuous pulse oximetry device that has a signal resolution of at least 0.1% for an oxygen saturation level measurement and the pulse oximetry device comprising a light detector and a light emitting diode for taking an oxygen measurement. Baker Jr teaches a pulse oximetry device that has a high resolution of a precision of 0.1%, 0.01%, or 0.001% in order to be used in pattern detection and a light emitting diode ([0017] [0022]). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to substitute the wearable pulse oximeter of Li in view of Shariff, further in view of John with the pulse oximetry device of Baker Jr with a resolution of 0.1% and light emitting diode. Doing so would merely be a simple substitution of one known pulse oximetry device for another to obtain predictable results. MPEP 2141.I
Li in view of Shariff, in view of John, in view of Baker Jr fails to teach deploying the machine learning model to the pulse oximetry device. Shelton teaches deploying a trained machine learning model to a wearable device to improve the operation of the wearable device ([2080]). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify the system of modified Li to include a step of deploying the trained model to the pulse oximetry device in order to improve the function of the device like the system of Shelton.
In regards to claim 2, modified Li teaches the device of claim 1, including collecting data using a wearable pulse oximeter wherein the pulse oximetry device is a high resolution continuous pulse oximetry device that has a signal resolution of at least 0.1% for an oxygen saturation level measurement (Baker Jr [0017]).
In regards to claim 3 modified Li teaches the system of claim 1, wherein the plurality of sleep apnea indicators comprises at least one oxygen saturation level (li [0022] minimum blood oxygenation saturation is an oxygen saturation level).
In regards to claim 4 modified Li teaches the system of claim 1, wherein the apnea classification is determined based at least in part on a severity threshold (Li [0014]).
In regards to claim 6 modified Li teaches the system of claim 1, wherein the apnea classification comprises a hypoxia-related condition (Li [0002] apnea is a hypoxia-related condition).
In regards to claim 7 modified Li teaches the system of claim 1, wherein the sleep apnea profile comprises oximetry data of a prior sleep study. ([0026] Clinical assessment would be for a sleep study). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use a sleep study of the patient with an identified sleep apnea diagnosis. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success. MPEP 2143.I
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20190076098 A1- previously cited) in view of Shariff (US 20160302671 A1- previously cited), in view of John (US 20040079372 A1- previously cited), in view of Baker Jr (US 20100292548 A1- previously cited), further in view of Shelton (US 20220233119 A1) as applied to claim 1, further in view of Mortz (US 67148030 B1- previously cited).
In regards to claim 5, modified Li teaches the device of claim 1. Modified Li fails to teach to teach a device, wherein the oximetry characteristics is generated based at least in part on extracting an oxygen saturation level at a sample rate of less than three seconds. Mortz teaches extracting an oxygen saturation level at a sample rate of less than a second in order to permit faster response of the pulse oximeter to changing values of the blood oxygen (Mortz Col 3, lines 25-30). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify the wearable pulse oximetry device of modified Li to extract an oxygen saturation level at a sample rate of less than three seconds like the device of Mortz. Doing so would permit faster response of the pulse oximeter to changing values of the blood oxygen.
Claim(s) 8-10, and 12-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20190076098 A1 - previously cited) in view of Mortz (US 67148030 B - previously cited).
In regards to claim 8 Li teaches portable pulse oximetry system, comprising: a sensor ([0014] wearable pulse oximeter contains a sensor); a computing device that comprises a processor and a memory, the memory storing a machine learning model ([0047]), the computing device being attached to a patient for a sleep apnea diagnosis; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least ([0041] and [0047-0048]):
generate pulse oximetry data by measuring pulse oximetry of a patient with the sensor ([0022]);
identify a plurality of sleep apnea indicators from the pulse oximetry data and from patient information associated with the patient ([0015] demographic data and metrics from a photoplethysmograph are sleep apnea indicators);
provide the plurality of sleep apnea indicators to a machine learning model trained for sleep apnea prediction ([0021] the ANNs are the machine learning module);
the machine learning model being executed locally by the computing device ([0005])
and receive a sleep apnea classification from the machine learning model (Figure 1, SBD severity is a sleep apnea classification).
Li fails to teach a sensor that includes a light detector and a light emitting diode for taking an oxygen measurement and a sampling rate of less than three seconds. Mortz teaches a sensor with a light detector and a light emitting diode for taking an oxygen measurement (Mortz FIG. 1 light emitting diodes 111, 112 and corresponding light detector 113) and extracting an oxygen saturation level at a sample rate of less than a second in order to permit faster response of the pulse oximeter to changing values of the blood oxygen (Mortz Col 3, lines 25-30). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to substitute the wearable pulse oximetry device of Li with the pulse oximetry device of Mortz. Doing so would permit faster response of the pulse oximeter to changing values of the blood oxygen.
In regards to claim 9 Li in view of Mortz teaches the portable system of claim 8, including the system indicating the determined SDB level to the person and/or to their physician (Li [0038]). Li in view of Mortz fails to teach a display where the classification is rendered on. Mortz teaches a display for display a determined index (Fig 1 Graphic display 114). ]). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify the system of Li to include a display like the one of Mortz, in order to communicate the sleep apnea classification visually to the user. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of displaying a classification output.
In regards to claim 10 Li in view of Mortz teaches the portable system of claim 8, wherein the machine-readable instructions, when executed by the processor, cause the computing device to at least: transmit the sleep apnea classification to a mobile phone device via a wireless communication channel (Li [0038] “The remote computing system could then provide the determined SDB level to the person and/or to their physician (e.g. via a phone call or text message)”).
In regards to claim 12 Li in view of Mortz teaches the portable system of claim 10, wherein the mobile phone device is configured to display a severity level of hypoxia and a user interface indicator referencing one of the plurality of hypoxia related conditions (Li [0038]). Li in view of Mortz fails to teach displaying a hypoxia level. However, Li on view of Mortz teaches determining “a percent of the period of time during which the blood oxygenation saturation is below a specified level (e.g., 70%, 75%, 80%, 85%, 90%, or 95%)” (Li [0022]) these percentages are severity levels of hypoxia). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify Li in view of Mortz to display the level that the blood oxygenation saturation is below for the longest amount of time in order to display a hypoxia level.
In regards to claim 13 Li in view of Mortz teaches the portable system of claim 8, wherein the machine learning model is executed on a server (Li [0040] SDB level is determined on a cloud computing system).
In regards to claim 14 Li in view of Mortz teaches the portable system of claim 8, wherein the senor comprises a light detector and a light emitting diode (Mortz FIG. 1 light emitting diodes 111, 112 and corresponding light detector 113).
In regards to claim 15 Li a method comprising:
generating, by a portable device that includes a sensor and a computing device, pulse oximetry data by measuring pulse oximetry of a patient ([0005] [0022]);
identifying, by the portable device, a plurality of sleep apnea indicators from the pulse oximetry data and from patient information associated with the patient ([0015] demographic data and metrics from a photoplethysmograph are sleep apnea indicators);
providing, by the portable device, the plurality of sleep apnea indicators to a machine learning model trained for sleep apnea prediction ([0021] the ANNs are the machine learning module);
prediction, the machine learning model being executed by a computing device for the portable pulse oximetry device ([0005]);
and receiving, by the portable device, a sleep apnea classification from the machine learning model (Figure 1, SBD severity is a sleep apnea classification).
Li fails to teach a sensor that includes a light detector and a light emitting diode for taking an oxygen measurement and a sampling rate of less than three seconds. Mortz teaches a sensor with a light detector and a light emitting diode for taking an oxygen measurement (Mortz FIG. 1 light emitting diodes 111, 112 and corresponding light detector 113) and extracting an oxygen saturation level at a sample rate of less than a second in order to permit faster response of the pulse oximeter to changing values of the blood oxygen (Mortz Col 3, lines 25-30). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to substitute the wearable pulse oximetry device of Li in view of Shariff, further in view of John with the pulse oximetry device of Mortz. Doing so would permit faster response of the pulse oximeter to changing values of the blood oxygen.
In regards to claim 16 Li in view of Mortz teaches the method of claim 15, further comprising: indicating the determined SDB level to the person and/or to their physician (Li [0038]). Li in view of Mortz fails to teach displaying, by the portable device, the sleep apnea classification. Mortz teaches a display for display a determined index (Fig 1 Graphic display 114). ]). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify the device of Li to include a display like the one of Mortz, in order to communicate the sleep apnea classification visually to the user. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of displaying a classification output.
In regards to claim 17 Li in view of Mortz teaches the method of claim 15, further comprising: indicating the determined SDB level to the person and/or to their physician (Li [0038]). Li in view of Mortz fails to displaying, by the portable device, a severity level for the sleep apnea classification. Mortz teaches a display for display a determined index (Fig 1 Graphic display 114). ]). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify the device of Li to include a display like the one of Mortz, in order to communicate the sleep apnea classification visually to the user. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of displaying a severity classification output.
In regards to claim 18 method of claim 17, further comprising: transmitting, by the portable device, the sleep apnea classification to a mobile phone device via a wireless communication channel (Li [0038] “The remote computing system could then provide the determined SDB level to the person and/or to their physician (e.g. via a phone call or text message)”).
In regards to claim 19 Li in view of Mortz teaches the method of claim 18, wherein the mobile phone device is configured to display a severity level of hypoxia and a user interface indicator referencing one of the plurality of hypoxia related conditions (Li [0038]). Li in view of Mortz fails to teach displaying a hypoxia level. However, Li on view of Mortz teaches determining “a percent of the period of time during which the blood oxygenation saturation is below a specified level (e.g., 70%, 75%, 80%, 85%, 90%, or 95%)” (Li [0022]) these percentages are severity levels of hypoxia). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify Li in view of Mortz to display the level that the blood oxygenation saturation is below for the longest amount of time in order to display a hypoxia level.
Claim(s) 11 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20190076098 A1- previously cited) in view of Mortz (US 67148030 B1- previously cited) as applied to claims 10 and 18, further in view of Gutierrez (US - previously cited).
In regards to claim 11, Li in view of Mortz teaches the portable system of claim 10, Li in view of Mortz fails to teach a device wherein the mobile phone device is configured to display a sleep apnea gauge that comprises a plurality of sleep apnea classifications and a user interface indicator referencing one of the plurality of sleep apnea classifications. Gutierrez teaches a digital gauge that indicates a classification (Gutierrez [0113]). ]). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify Li in view of Mortz to display the SBD level to the user by sending an image of a gauge that includes the levels of severity and an arrow indicating what severity level the machine learning module has determined. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of providing a visual indicator of a classification output. MPEP 2143.I
In regards to claim 20, Li in view of Mortz teaches the method of claim 18, Li in view of Mortz fails to teach a device wherein the mobile phone device is configured to display a sleep apnea gauge that comprises a plurality of sleep apnea classifications and a user interface indicator referencing one of the plurality of sleep apnea classifications. Gutierrez teaches a digital gauge that indicates a classification (Gutierrez [0113]). ]). It would have been prima facie obvious to a person of ordinary sill in the art before the effective filing date of the claimed invention to modify Li in view of Mortz to display the SBD level to the user by sending an image of a gauge that includes the levels of severity and an arrow indicating what severity level the machine learning module has determined. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of providing a visual indicator of a classification output. MPEP 2143.I
Response to Arguments
Applicant’s arguments, see pages 9-11, filed 08/21/2025, with respect to the 35 U.S.C 112(a) rejections of claims 1-20 have been fully considered and are persuasive. The 35 U.S.C 112(a) rejections of claims 1-20 been withdrawn.
Applicant’s arguments, see pages 12-13, filed 08/21/2025, with respect to the 35 U.S.C 112(b) rejections of claims 1-20 have been fully considered and are persuasive. The 35 U.S.C 112(b) rejections of claims 1-20 been withdrawn, However, new rejections have been made in response to amendment.
Applicant's arguments filed 08/21/2025, with respect to the 35 U.S.C 101 rejections of claims 1-20 have been fully considered but they are not persuasive. The specification fails to provide ample evidence that an improvement has been made. Portable pulse oximetry devices and at home sleep apnea detectors are well known in the art. For example, Chance (US 20020161290 A1) teaches an at home sleep apnea system using pulse oximetry ([0044]), Lynn (US 20060149144 A1) teaches a portable oximetry device to determine sleep apnea at home ([0322]), and Abdalkhani (US 20130131477 A1) teaches a portable oximetry device that can allow a patient to monitor sleep apnea at home ([0180-0181]). Furthermore, the recited sensor, memory, and computing device are still generic devices even when taking into consideration the senor’s signal resolution of a precision of 0.1%, 0.01%, or 0.001%. In addition, the 0.1% resolution pulse oximeter itself is not positively claimed as part of the system of claim 1. Claim 1 merely states that data is received from a pulse oximetry device.
Applicant’s arguments, see pages 19-22, filed 08/21/2025, with respect to the 103 rejection(s) of claim(s) Claims 1, 3, 4, 6, and 7 under Li in view of Shariff in view of have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of over Li (US 20190076098 A1- previously cited) in view of Shariff (US 20160302671 A1- previously cited), in view of John (US 20040079372 A1- previously cited), in view of Baker Jr (US 20100292548 A1- previously cited), further in view of Shelton (US 20220233119 A1).
Applicant’s arguments, see page 23, filed 08/21/2025, with respect to the 35 U.S.C. 103 rejection(s) of claim(s) 8-10 and 12-14 under Li in view of Mortz have been fully considered, but they are not persuasive. Applicant contends that paragraph [0021] of LI fails to teach “provide the plurality of sleep apnea indicators to a machine learning model trained for sleep apnea prediction”. Examiner contends that paragraph [0021] of Li teaches “one metric descriptive of the photoplethysmographic signal and the at least one health-related status of the person” are sleep apnea indictors, which are provided to the ANN. Said ANN is trained to determine apnea (see Li [0026]). Li also teaches “the machine learning model being executed locally by the computing device” see paragraph [0005].
Applicant’s arguments, see page 23, filed 08/21/2025, with respect to the 35 U.S.C. 103 rejection(s) of claim(s) 15-19 under Li in view of Mortz have been fully considered, but they are not persuasive. Applicant contends that claim 15 is recites similar features to those of claim 1, and therefore the rejections should be withdrawn for similar reasons. However, claim 15 is lacking the “deploying” step that made the argument of claim 1 persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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/LUCY EPPERT/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791