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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/14/2026 has been entered.
Claim Objections
Claims 1, 3, 14, and 16 objected to because of the following informalities:
In claim 1 and 14, “the course of the long term wear” should read “the course of the long-term wear.”
In claim 3, “potential sleep disorder to a
In claim 16, “disorder to a communication device of a
Appropriate correction is required.
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 1 and 14 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 1 and 14 introduce claim limitation “detect patient routine data.” In the applicant’s remark, the applicant highlights paragraphs 89, 115, 130, 194, 198, 210-211, 215, and 240 provide support for the amended claim limitations within independent claims. However, these paragraphs do not provide clarification on how the patient routine is defined. Is routine data historical data from previous cycles that is compared to the current data collected? Is routine data the output data after the analysis from the processor?
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, 3-7, 9-11, 13, 14, and 16-24 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 Claims 1 and 14, the claim limitation “detect patient routine data” renders the claim indefinite because the limitation is unclear. It is unclear whether the data is from continuous wear or a specific data that is measured from the user. For purposes of examination, the claim limitation is interpreted as the data measured from long-term continuous wear from the patient. Due to the rejection of the independent claim, the dependent claims are also rejected because they are failing to cure the deficiencies as stated in the independent claim.
In Claims 1 and 14, the claim limitation “sleep sensor” and “sleep detector” renders the claim indefinite because the limitation is unclear. It is unclear whether the elements of a sleep sensor and a sleep detector are two different elements that measure the same sleep data, or different sleep data. For purposes of examination, the claim limitation is interpreted as the same element, wherein that element is an accelerometer which measures the sleep data.
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 therefore, subject to the conditions and requirements of this title.
Claims 1, 3-7, 9-11, 13, 14, and 16-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Each of Claims 1, 3-7, 9-11, 13, 14, and 16-24 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1, 3-7, 9-11, 13, 14, and 16-24 recites at least one step or instruction for estimating the user’s sleep disorder, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1, 3-7, 9-11, 13, 14, and 16-24 recites an abstract idea.
Specifically, Claims 1 and 14 recite:
Claim 1 | “A medical system prescribed for cardiac monitoring of a patient comprising:
a support structure configured for long-term continuous wear on the patient to collect health data and detect patient routine data (Observation), the support structure including:
electrodes positioned to detect electrical signals to generate electrocardiogram (ECG) data (Observation); and
two or more sleep sensors each to produce different types of sleep signals captured during the course of the long-term continuous wear (Observation);
a respiration detector to generate respiration data (Observation);
one or more sleep detectors to generate sleep data from the sleep signals (Observation); and
at least one processor configured to use logic to perform operations (Judgement) comprising:
analyzing a combination of sleep data to detect a plurality of sleep periods during the course of the long term wear according to the detect patient routine data (Judgement);
determining a cardiac condition based, at least in part, on the ECG data; determining at least one respiratory disturbance during at least two sleep periods based, at least in part, on the respiration data (Judgement);
defining at least one sleep disorder indicator each including a numerical representation by:
assigning individual numerical values to sleep factors associated with two or more predefined data selected from a group consisting of: respiration data, ECG data, and sleep data (Judgement); and
determining the numerical representation for each of the at least one sleep disorder indicator based on the individual numerical values (Judgement);
in response to determining the cardiac condition and/or at least one respirator disturbance:
applying the sleep factors to the health data including two or more of the respiration data, the ECG data, and the sleep data of the patient to determine two or more sleep disorder indexes for the patient over the sleep periods of the patient; and determining a quantitative risk value for at least one potential sleep disorder of the patient, selected from a group of disorders comprising: sleep apnea, hypopnea, and respiratory effort related arousal (RERA) (Judgement) by at least:
identifying a first trend of the two or more sleep disorder indexes across the sleep periods (Judgement); and comparing the first trend with the at least one sleep disorder to determine that the first trend is within a threshold amount (Judgement).”
Claim 14 | “14. A method to monitor health of a patient with a wearable medical device prescribed for cardiac monitoring of the patient, the method comprises: providing a support structure of the wearable medical device configured for long-term continuous wear on the patient to collect health data and detect patient routine data (Observation), wherein the support structure includes:
electrodes positioned to detect electrical signals to generate electrocardiogram (ECG) data (Observation); and
two or more sleep sensors each to produce different types of sleep signals captured during the course of the long-term continuous wear (Observation);
providing a respiration detector to generate respiration data (Observation);
providing a sleep detector to acquire sleep data from the sleep signals (Observation);
analyzing a combination of the sleep data to detect a plurality of sleep periods during the course of the long term wear according to the detect patient routine data (Judgment);
determining a cardiac condition based, at least in part, on the ECG data; determining at least one respiratory disturbance during at least two sleep periods based, at least in part, on the respiration data (Judgment);
defining at least one sleep disorder indicator each including a numerical representation by:
assigning individual numerical values to sleep factors associated with two or more predefined data selected from a group consisting of: respiration data, ECG data, and sleep data (Judgment); and
determining the numerical representation for each of the at least one sleep disorder indicator based on the individual numerical values (Judgment);
in response to determining the cardiac condition and/or at least one respirator disturbance:
applying the sleep factors to the health data including two or more of the respiration data, the ECG data, and the sleep data of the patient to determine two or more sleep disorder indexes for the patient over the sleep periods of the patient (Judgment); and
determining a quantitative risk value for at least one potential sleep disorder of the patient, selected from a group of disorders comprising: sleep apnea, hypopnea, and respiratory effort related arousal (RERA) (Judgment) by at least:
identifying a first trend of the two or more sleep disorder indexes across the sleep periods; and comparing the first trend with the at least one sleep disorder to determine that the first trend is within a threshold amount (Judgment).”
Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrowing idea is still an abstract idea and an abstract idea with additional well-known equipment/functions are not significantly more than the abstract idea.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
Regarding Claims 1 and 14 (and their respective dependent claims) meets Step 2A, Prong 2 because the above-identified abstract idea in each of independent claims are not integrated into a practical application. The above-identified abstract ideas do not improve the following: function of a particular machine, manufacture or other technology; treatment or prophylaxis for a disease or medical condition; or transforming or reducing of a particular article to a different state or thing (MPEP 2106.04(d)).
Step 2B
Lastly, the claims as a whole are analyzed to determine whether any elements, or in combination, to ensure that they amount to significantly more than the judicial exception itself. However, these claims do not appear to recite additional elements that amount to significantly more than the judicial exception.
The recited additional elements, more specifically, wearable device, electrodes, and accelerometer [Examiner’s note, the wearable device is analogous to support structure; the respiration detector is analogous to the electrodes because the electrodes measures both the ECG data and respiration data; and accelerometer is analogous to two or more sleep sensors.], are not significantly more because US Reference 9372533 B1 provides evidence within Figure 12 and Column 13 lines 4-12 that they are well-known, routine, and conventional.
The above-identified additional elements, more specifically the processor, are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Therefore, none of the Claims 1, 3-7, 9-11, 13, 14, and 16-24 amounts to significantly more than the abstract idea itself. Accordingly, claims 1, 3-7, 9-11, 13, 14, and 16-24 are not patent eligible and rejected under 35 U.S.C. 101.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 11, 14, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kaib (US 20130231711 A1 - used in 12/04/23 IDS) in view of Parker et al. (US 20170161462 A1), Abeyratne et al. (US 20120004749 A1), Li et al. (“Detection of Upper Airway Status and Respiratory Events by a Current Generation Positive Airway Pressure Device”, 2015), and Nakajima et al. (US 20190159723 A1).
Regarding Claim 1, Kaib discloses a medical system prescribed for cardiac monitoring of a patient (Abstract; Paragraphs 0009, 0053) comprising:
a support structure (wearable treatment device – element 100) configured for long-term continuous wear on the patient to collect health data (Paragraphs 0008, 0044) and detect patient routine data (Paragraph 0053), the support structure including:
electrodes positioned to detect electrical signals to generate electrocardiogram (ECG) data (Sensor 110; Paragraphs 0046, 0051, 0080; [Examiner’s note, the sensor (110) contains a plurality of sensor (110), where one of the groups of sensor, element 101, is an ECG sensor and another group is a respiration sensor.]); and
two or more sleep sensors (accelerometer 135; Paragraph 0055) each to produce different types of sleep signals captured during the course of the long-term continuous wear (Paragraph 0054; [Examiner’s note, the different types of sleep signals captured are physical activity, body positions, and sleep conditions.]);
a respiration detector to generate respiration data (Sensor 110; Paragraphs 0046, 0051, 0080; [Examiner’s note, the sensor (110) contains a plurality of sensor (110), where one of the groups of sensor 101 is an ECG sensor and another group is a respiration sensor.]);
one or more sleep detectors to generate sleep data from the sleep signals (accelerometer 135; Paragraphs 0054-0055, 0080-0081; [Examiner’s note, as mentioned above under 35 U.S.C 112b, sleep sensors and sleep detectors are an accelerometer measuring the user’s motion data. When a sleeping person moves less, this indicates that the user is sleeping or is at a resting.]); and
at least one processor configured to use logic to perform operations (Paragraph 0045) comprising:
analyzing a combination of sleep data to detect a sleep period during the course of the long term wear according to the detect patient routine data (Paragraphs 0053-0054);
determining a cardiac condition based, at least in part, on the ECG data (Figure 6; Paragraphs 0118, 0121);
determining at least one respiratory disturbance during the sleep period based, at least in part, on the respiration data (Paragraphs 0054, 0078, 0080, 0206).
Kaib is silent in teaching a sleep period is at least two sleep periods and plurality of sleep periods. Parker teaches at least two sleep periods and plurality of sleep periods (Parker | Figure 5; Paragraph 0049). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib to incorporate teachings of a plurality of sleep periods from Parker because by capturing multiple sleep periods over several days provides the necessary data about the patient's daily sleep patterns (Parker | Paragraphs 0058-0060).
Kaib in view of Parker is silent in teaching defining at least one sleep disorder indicator each including a numerical representation by: assigning individual numerical values to sleep factors associated with two or more predefined data selected from a group consisting of: respiration data, ECG data, and sleep data; and determining the numerical representation for each of the at least one sleep disorder indicator based on the individual numerical values, in response to determining the cardiac condition and/or at least one respirator disturbance: applying the sleep factors to the health data including two or more of the respiration data, the ECG data, and the sleep data of the patient to determine two or more sleep disorder indexes for the patient over the sleep periods of the patient; and determining value for at least one potential sleep disorder of the patient, selected from a group of disorders comprising: sleep apnea, hypopnea, and respiratory effort related arousal (RERA), by at least: identifying a first trend of the sleep disorder indexes across the sleep periods; and comparing the first trend with the at least one sleep disorder to determine that the first trend is within a threshold amount.
Abeyratne teaches defining at least one sleep disorder indicator each including a numerical representation (Abeyratne | Paragraph 0003) by: assigning individual numerical values to sleep factors associated with two or more predefined data selected from a group consisting of: respiration data, ECG data, and sleep data (Abeyratne | Paragraph 0196); and determining the numerical representation for each of the at least one sleep disorder indicator based on the individual numerical values (Abeyratne | Paragraph 0196),
in response to determining the cardiac condition and/or at least one respirator disturbance: applying the sleep factors to the health data including two or more of the respiration data, the ECG data, and the sleep data of the patient to determine sleep disorder indexes for the patient over the sleep periods of the patient (Abeyratne | Paragraphs 0003, 0196); and determining value for at least one potential sleep disorder of the patient, selected from a group of disorders comprising: sleep apnea, hypopnea, and respiratory effort related arousal (RERA) (Abeyratne | Table 4; Paragraphs 0003, 0196; [Examiner’s note, the article from John Hopkins Medicine, “Obstructive Sleep Apnea” published on April 3, 2019, clarifies that an AHI Score < 10 means the user is experiencing less than 10 episodes of sleep apnea per hour which equates to mild obstructive sleep apnea. Whereas an AHI Score > 10 means the user is experiencing more than 10 episodes of sleep apnea per hour which equates to moderate to severe obstructive sleep apnea. The user’s AHI score determines the severity of their sleep disorder.]), by at least: identifying a first trend of the sleep disorder indexes across the sleep periods (Abeyratne | Figure 17; [Examiner’s note, the figures illustrate the relationship between non-Gaussianity Index (NGI) vs AHI. AHI is the Apnea-Hypopnea-Index which computes the number of Sleep Apnea and Hypopnea events occur per hour.]); and comparing the first trend with the at least one sleep disorder to determine that the first trend is within a threshold amount (Abeyratne | Figures 17; Table 7; Paragraphs 0216; [Examiner’s note, Figure 17 shows a trend (dark-colored circles and hollow circles) compared to two thresholds (horizontal Ψ threshold and vertical AHI score). The Ψ threshold is calculated and shown in Table 7. The purpose of the two “thresholds” on the graph is to show which values indicate the presence of OSAHS vs which don’t. For example, the dark-colored circles denote false-positive and false-negative values. Hollow circles located above the two threshold lines indicate the Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS), while hollow circles below the two threshold lines indicate of non-OSAHS.]) One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system from Kaib in view of Parker to incorporate the teachings of AHI and sleep disorder thresholds from Abeyratne because the user’s Apnea-Hypopnea Index (AHI) score above a specific threshold determines the severity diagnosis of a sleep disorder (Abeyratne | Paragraphs 0002-0003).
Kaib in view of Parker and Abeyratne is silent in teaching the sleep disorder index is two or more sleep disorder indexes. Li teaches two or more sleep disorder indexes (Li | Table 2; Figures 4-5; Page 599, Statistical Analysis through ROC Analysis; [Examiner’s note, the sleeps factors, EEG and respiration data, are used to determine two or more sleep disorder indexes which are AHI, apnea index (AI), hypopnea index (HI), and RERA index (RERAI).]) One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker and Abeyratne to incorporate the teachings of determining a potential sleep disorder based on two or more sleep disorder indexes from Li. Doing so would allow the physician to receive comprehensive patient data to produce an accurate sleep disorder diagnosis and tailored treatment (Li | Page 597 under Conclusions).
Kaib in view of Parker, Abeyratne, and Li are silent in teaching the values is a quantitative risk value. Nakajima teaches a quantitative risk value (Nakajima | Paragraphs 0110-0111). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, and Li to incorporate the teachings of the quantitative risk value from Nakajima because the quantitative risk value warns users when their sleep disorder indicates dangerous times for apnea (Nakajima | Paragraph 0110).
Regarding Claim 3, Kaib in view of Parker, Abeyratne, Li, and Nakajima teach the system of claim 1. Kaib teaches further comprising a communication component (Kaib | update agent component – element 912; [Examiner’s note, the update agent component is within the user’s treatment device.]), wherein the operations of the at least one processor (Kaib | Paragraph 0138, The treatment device 906 may include any programmable device (a device including memory for storing data and at least one processor coupled to the memory) configured to administer therapeutic measures to mobile patients, such as the treatment devices described above with reference to FIGS. 1-4 and 8.) includes: in response to the determining the potential sleep disorder, automatically generating and causing the communication component to transmit a risk warning of the potential sleep disorder to a remote device of a medical provide to administer treatment of the potential sleep disorder (Kaib | Paragraphs 0138-0139, 0179-0180).
Kaib in view of Parker, Abeyratne and Li are silent in teaching the risk warning includes a representation of the quantitative risk value. Nakajima teaches the risk warning includes a representation of the quantitative risk value (Nakajima | Paragraphs 0110-0111). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, and Li to incorporate the teachings of the risk warning as the quantitative risk value from Nakajima because the quantitative risk value warns users when their sleep disorder indicates dangerous times for apnea (Nakajima | Paragraph 0110).
Regarding claim 11, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1, wherein the one or more sleep sensors comprise an accelerometer and the sleep data detected by the accelerometer includes patient motion data and/or torso position data (Kaib | Paragraphs 0046, 0054).
Regarding Claim 14, Kaib discloses a method to monitor health of a patient with a wearable medical device (wearable treatment device – element 100) prescribed for cardiac monitoring of the patient (Abstract; Paragraphs 0009, 0053), the method comprises:
providing a support structure of the wearable medical device (wearable treatment device – element 100) configured for long-term continuous wear on the patient to collect health data (Paragraphs 0008, 0044) and detect patient routine data (Paragraph 0053), wherein the support structure includes:
electrodes positioned to detect electrical signals to generate electrocardiogram (ECG) data (Sensor 110; Paragraphs 0046, 0051, 0080; [Examiner’s note, the sensor (110) contains a plurality of sensor (110), where one of the groups of sensor, element 101, is an ECG sensor and another group is a respiration sensor.]); and
two or more sleep sensors (accelerometer 135; Paragraph 0055) each to produce different types of sleep signals captured during the course of the long-term continuous wear (Paragraph 0054; [Examiner’s note, the different types of sleep signals captured are physical activity, body positions, and sleep conditions.]);
providing a respiration detector to generate respiration data (Sensor 110; Paragraphs 0046, 0051, 0080; [Examiner’s note, the sensor (110) contains a plurality of sensor (110), where one of the groups of sensor 101 is an ECG sensor and another group is a respiration sensor.]);
providing a sleep detector to acquire sleep data from the sleep signals (accelerometer 135; Paragraphs 0054-0055, 0080-0081; [Examiner’s note, as mentioned above under 35 U.S.C 112b, sleep sensors and sleep detectors are an accelerometer measuring the user’s motion data. When a sleeping person moves less, this indicates that the user is sleeping or is at a resting.]);
analyzing a combination of the sleep data to detect a sleep period during the course of the long term wear according to the detect patient routine data (Paragraphs 0053-0054);
determining a cardiac condition based, at least in part, on the ECG data (Figure 6; Paragraphs 0118, 0121; [Examiner’s note, if a treatment must be applied to the patient, then there is a cardiac condition that is met based upon the ECG data collected.]);
determining at least one respiratory disturbance during the sleep period based, at least in part, on the respiration data (Paragraphs 0054, 0078, 0080, 0206; [Examiner’s note, the continuous use of the wearable treatment device indicates that respiratory disturbances are captured throughout the entire time period the user wears it. Consequently, respiratory disturbances will have been measured across two or more sleep periods.]).
Kaib is silent in teaching a sleep period is at least two sleep periods and plurality of sleep periods. Parker teaches at least two sleep periods and plurality of sleep periods (Parker | Figure 5; Paragraph 0049). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib to incorporate teachings of a plurality of sleep periods from Parker because by capturing multiple sleep periods over several days provides the necessary data about the patient's daily sleep patterns (Parker | Paragraphs 0058-0060).
Kaib in view of Parker is silent in teaching defining at least one sleep disorder indicator each including a numerical representation by: assigning individual numerical values to sleep factors associated with two or more predefined data selected from a group consisting of: respiration data, ECG data, and sleep data; and determining the numerical representation for each of the at least one sleep disorder indicator based on the individual numerical values, in response to determining the cardiac condition and/or at least one respirator disturbance: applying the sleep factors to the health data including two or more of the respiration data, the ECG data, and the sleep data of the patient to determine two or more sleep disorder indexes for the patient over the sleep periods of the patient; and determining value for at least one potential sleep disorder of the patient, selected from a group of disorders comprising: sleep apnea, hypopnea, and respiratory effort related arousal (RERA), by at least: identifying a first trend of the sleep disorder indexes across the sleep periods; and comparing the first trend with the at least one sleep disorder to determine that the first trend is within a threshold amount.
Abeyratne teaches defining at least one sleep disorder indicator each including a numerical representation (Abeyratne | Paragraph 0003) by: assigning individual numerical values to sleep factors associated with two or more predefined data selected from a group consisting of: respiration data, ECG data, and sleep data (Abeyratne | Paragraph 0196); and determining the numerical representation for each of the at least one sleep disorder indicator based on the individual numerical values (Abeyratne | Paragraph 0196),
in response to determining the cardiac condition and/or at least one respirator disturbance: applying the sleep factors to the health data including two or more of the respiration data, the ECG data, and the sleep data of the patient to determine two or more sleep disorder indexes for the patient over the sleep periods of the patient (Abeyratne | Paragraphs 0003, 0196); and determining value for at least one potential sleep disorder of the patient, selected from a group of disorders comprising: sleep apnea, hypopnea, and respiratory effort related arousal (RERA) (Abeyratne | Table 4; Paragraphs 0003, 0196; [Examiner’s note, the article from John Hopkins Medicine, “Obstructive Sleep Apnea” published on April 3, 2019, clarifies that an AHI Score < 10 means the user is experiencing less than 10 episodes of sleep apnea per hour which equates to mild obstructive sleep apnea. Whereas an AHI Score > 10 means the user is experiencing more than 10 episodes of sleep apnea per hour which equates to moderate to severe obstructive sleep apnea. The user’s AHI score determines the severity of their sleep disorder.]), by at least: identifying a first trend of the sleep disorder indexes across the sleep periods (Abeyratne | Figure 17; [Examiner’s note, the figures illustrate the relationship between non-Gaussianity Index (NGI) vs AHI. AHI is the Apnea-Hypopnea-Index which computes the number of Sleep Apnea and Hypopnea events occur per hour.]); and comparing the first trend with the at least one sleep disorder to determine that the first trend is within a threshold amount (Abeyratne | Figures 17; Table 7; Paragraphs 0216; [Examiner’s note, Figure 17 shows a trend (dark-colored circles and hollow circles) compared to two thresholds (horizontal Ψ threshold and vertical AHI score). The Ψ threshold is calculated and shown in Table 7. The purpose of the two “thresholds” on the graph is to show which values indicate the presence of OSAHS vs which don’t. For example, the dark-colored circles denote false-positive and false-negative values. Hollow circles located above the two threshold lines indicate the Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS), while hollow circles below the two threshold lines indicate of non-OSAHS.]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system from Kaib in view of Parker to incorporate the teachings of AHI and sleep disorder thresholds from Abeyratne because the user’s Apnea-Hypopnea Index (AHI) score above a specific threshold determines the severity diagnosis of a sleep disorder (Abeyratne | Paragraphs 0002-0003).
Kaib in view of Parker and Abeyratne is silent in teaching the sleep disorder index is two sleep disorder indexes. Li teaches two or more sleep disorder indexes (Li | Table 2; Figures 4-5; Page 599, Statistical Analysis through ROC Analysis; [Examiner’s note, the sleeps factors, EEG and respiration data, are used to determine two or more sleep disorder indexes which are AHI, apnea index (AI), hypopnea index (HI), and RERA index (RERAI).]) One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker and Abeyratne to incorporate the teachings of determining a potential sleep disorder based on two or more sleep disorder indexes from Li. Doing so would allow the physician to receive comprehensive patient data to produce an accurate sleep disorder diagnosis and tailored treatment (Li | Page 597 under Conclusions).
Kaib in view of Parker, Abeyratne, and Li are silent in teaching the values is a quantitative risk value. Nakajima teaches a quantitative risk value (Nakajima | Paragraphs 0110-0111). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, and Li to incorporate the teachings of the quantitative risk value from Nakajima because the quantitative risk value warns users when their sleep disorder indicates dangerous times for apnea (Nakajima | Paragraph 0110).
Regarding Claim 16, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the method of claim 14. Kaib teaches further comprising: in response to the determining the potential sleep disorder, automatically generating and causing a communication component to transmit a risk warning of the potential sleep disorder (Kaib | Paragraphs 0138-0139, 0179-0180) to a communication device (Kaib | update agent component – element 912; [Examiner’s note, the update agent component is within the user’s treatment device.]) of a to a remote device (Kaib | computer system – elements 908 and 920; [Examiner’s note, based off of the claim interpretation, this is being interpreted to say potential sleep disorder from the communication device to a remote device.]) of a medical provide to administer treatment of the potential sleep disorder (Kaib | Paragraphs 0138-0139, 0179-0180).
Kaib in view of Parker, Abeyratne and Li are silent in teaching the risk warning includes a representation of the quantitative risk value. Nakajima teaches the risk warning includes a representation of the quantitative risk value (Nakajima | Paragraphs 0110-0111). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, and Li to incorporate the teachings of the risk warning as the quantitative risk value from Nakajima because the quantitative risk value warns users when their sleep disorder indicates dangerous times for apnea (Nakajima | Paragraph 0110).
Regarding Claim 20, Kaib in view of Abeyratne, Li, and Nakajima teaches the method of claim 14, wherein the one or more sleep sensors include an accelerometer and the sleep data include patient motion data and/or torso orientation data (Kaib | Paragraphs 0046, 0054).
Claims 4-7, 17, and 18 are rejected under 35 U.S.C 103 as being unpatentable over Kaib in view of Parker, Abeyratne, Li, Nakajima, and Stahmann et al. (US 20110061647 A1).
Regarding claim 4 and 17, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1 and the method of claim 14, respectively. Kaib teaches wherein the potential sleep disorder includes sleep apnea (Kaib | Paragraph 0054, Sensors 110, 135 can detect and monitor physical activity and activity trends, body positions, and sleep conditions, such as sleep apnea), and wherein the sleep factors include:
an indication of a change in heart rate at a threshold rate corresponding with the first subperiod of airflow absence according to the ECG data (Kaib | Paragraph 0068; [Examiner’s note, according to the Cardiac Rhythm Disorders in Obstructive Sleep Apnea (OSA) states that patients diagnosed with OSA have cardiac rhythm disorders like an arrhythmia.]).
Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching an indication of time that the patient is asleep during the sleep period according to the sleep data; an indication that a first subperiod of airflow is absent during the sleep period according to the respiration data.
Stahmann teaches an indication of time that the patient is asleep during the sleep period according to the sleep data (Stahmann | Figure 7; Paragraph 0281); an indication that a first subperiod (Stahmann | non-breathing – element 760; Figure 7; [Examiner’s note, the sleep apnea subperiod is highlighted in Annotated Figure 1.]) of airflow is absent during the sleep period according to the respiration data (Stahmann | Paragraph 0338). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teachings of determining whether a patient is experiencing sleep apnea from Stahmann. Doing so would allow physicians to properly diagnose and treat the patient suffering from sleep apnea (Stahmann | Paragraph 0522).
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Annotated Figure 1 | Total Period of Sleep Disorder Recorder - Subperiod of Sleep Apnea
Regarding claim 5, Kaib in view of Parker, Abeyratne, Li, Nakajima, and Stahmann teaches the system of claim 4, wherein the sleep data includes a torso position of the patient during the sleep period, and wherein the sleep factors include an indication of a flat or angled supine position of the patient according to the sleep data (Kaib | Paragraph 0051).
Regarding claims 6 and 18, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1, and the method of claim 14, respectively. Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching the potential sleep disorder includes hypopnea, wherein the sleep factors include: an indication of a time that the patient is asleep during a sleep period according to the sleep data; the respiration data meeting a hypopnea threshold level of airflow reduction in a second subperiod during the sleep period; and one or more additional sleep factors selected from a group of: oxygenation data meeting a hypopnea threshold level of blood oxygen desaturation in the second subperiod, and an indication of a change in heart rate corresponding with the respiration data according to the ECG data.
Stahmann teaches the potential sleep disorder includes hypopnea (Stahmann | Figures 8A-8B and 9), wherein the sleep factors include: an indication of a time that the patient is asleep during a sleep period according to the sleep data (Stahmann | Paragraph 0281); the respiration data meeting a hypopnea threshold level of airflow reduction in a second subperiod during the sleep period (Stahmann | Figure 8B; [Examiner’s note, the hypopnea subperiod is highlighted in Annotated Figure 2.]; Figure 9; Paragraph 0308); and one or more additional sleep factors selected from a group of: oxygenation data meeting a hypopnea threshold level of blood oxygen desaturation in the second subperiod (Stahmann | Paragraph 0341), and an indication of a change in heart rate corresponding with the respiration data according to the ECG data (Stahmann | Paragraph 0771). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teachings of determining whether a patient is experiencing hypopnea from Stahmann. Doing so would allow physicians to properly diagnose and treat the patient suffering from hypopnea (Stahmann | Paragraph 0522: The health care professionals may use the sleep quality indicator trends alone or in conjunction with other device-gathered or clinical data to diagnose disorders and/or adjust the patient's device or
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medical therapy as needed to improve the patient's quality of sleep).
Annotated Figure 2 | Total Period of Sleep Disorder Recorder - Subperiod of Hypopnea
Regarding claim 7, Kaib in view of Parker, Abeyratne, Li, Nakajima, and Stahmann the system of claim 6. Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching the second subperiod is a sliding window that is restarted upon a detection of patient motion during the sleep period, and wherein the respiration data include an average magnitude of respiratory complexes acquired during the second subperiod.
Stahmann teaches the second subperiod is a sliding window that is restarted upon a detection of patient motion during the sleep period (Stahmann | Figure 95; Paragraph 1607, A muscle activity signal is sensed at a block 9502. Muscle activity may be sensed, for example, using EMG sensors, accelerometers, or other sensors suitable for determining patient movement. A determination block 9504 is used to decide if the patient is sleeping. If determination 9504 concludes that the patient is not sleeping, the method 9500 loops back to the beginning), and wherein the respiration data includes an average magnitude of respiratory complexes acquired during the second subperiod (Stahmann | Paragraph 0302, hypopnea is detected when an average of the patient's respiratory tidal volume taken over a selected time interval falls below the hypopnea tidal volume threshold). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teachings of the second subperiod from Stahmann. Doing so would allow physicians to properly diagnose and treat the patient suffering from a sleep disorder (Stahmann | Paragraph 0522).
Claims 9, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaib in view of Parker, Abeyratne, Li, Nakajima and Ramanan et al (US 20120179061 A1).
Regarding claims 9 and 19, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1 and the method of claim 14, respectively. Kaib in view of Parker, Abeyratne, Li, Nakajima is silent in teaching the potential sleep disorder includes respiratory effort related arousal (RERA), and wherein the sleep factors include: an indication of a sleep period in which the patient is asleep during a first portion of the sleep period and awake during a second portion of the sleep period according to the sleep data; and the respiration data meeting a RERA threshold level of airflow reduction during a third subperiod of time during the sleep period.
Ramanan teaches the potential sleep disorder includes respiratory effort related arousal (RERA) (Ramanan | Paragraph 0383, If the input arousal type represents a RERA (respiratory effort related arousal), then the output weight is -0.1), and wherein the sleep factors include: an indication of a sleep period in which the patient is asleep during a first portion of the sleep period and awake during a second portion of the sleep period according to the sleep data (Ramanan | Figure 11 – Display element 1120; [Examiner’s note, the sleep condition detection device (element 102) contains a display that shows the awake time period and the sleep time period of the patient.]); and the respiration data meeting a RERA threshold level of airflow reduction during a third subperiod of time during the sleep period (Ramanan | Paragraph 0383, the output of the thresholder 2810C may also be a weight in a range of -0.05 and 0.5 based on the sleep stability index…the input arousal type represents a RERA (respiratory effort related arousal), then the output weight is -0.1). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to include the diagnosis of a potential sleep disorder to include RERA from Ramanan because the system can detect another type of sleep disorder and can aid in improving the patient’s quality of sleep (Ramanan | Paragraph 0423).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kaib in view of Parker, Abeyratne, Li, Nakajima, Ramanan, and Stahmann et al. (US20110061647 A1).
Regarding claim 10, Kaib in view of Parker, Abeyratne, Li, Nakajima, and Ramanan teaches the system of claim 9. Kaib in view of Abeyratne, Li, and Nakajima are silent in teaching the third subperiod is a sliding window and the respiration data includes an average magnitude of respiratory complexes acquired during the third subperiod.
Ramanan teaches the third subperiod is a sliding window and the respiration data includes an average magnitude of respiratory complexes acquired during the third subperiod (Ramanan | feature extraction module – element 110; [Examiner’s note, the feature extraction module calculates various respiratory data.]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify Kaib in view of Parker, Abeyratne, Li, and Nakajima to include the diagnosis of a potential sleep disorder to include RERA from Ramanan because the system can detect another type of sleep disorder and can aid in improving the patient’s quality of sleep (Paragraph 0423: Detecting sleep onset accurately can provide a very interesting insight into the patient's sleeping patterns and overall quality of sleep. It can also be important in accurately detecting sleep state).
Additionally, Kaib in view of Parker, Abeyratne, Li, Nakajima, and Ramanan are silent in teaching wherein the third subperiod is restarted upon a detection of patient motion. Stahmann teaches the third subperiod is restarted upon a detection of patient motion (Stahmann | Figure 95; Paragraph 1607, a muscle activity signal is sensed at a block 9502. Muscle activity may be sensed, for example, using EMG sensors, accelerometers, or other sensors suitable for determining patient movement. A determination block 9504 is used to decide if the patient is sleeping. If determination 9504 concludes that the patient is not sleeping, the method 9500 loops back to the beginning). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, Nakajima, and Ramanan to incorporate the teachings of restarting the third subperiod based on the detection of patient motion from Stahmann. Doing so would allow the prevention of noise interfering with the data collection process when the user is experiencing a sleep disorder (Stahmann | Paragraph 1607).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kaib in view of Parker, Abeyratne, Li, Nakajima, and Russell et al. (US 20090281394 A1).
Regarding claim 13, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1, which contains the respiration detector. Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching the respiration detector receives respiratory impedance signals from alternating current (AC) signals at the electrodes or receives direct current (DC) signals at the electrodes. Russell teaches the respiration detector receives respiratory impedance signals from alternating current (AC) signals at the electrodes (Russell | Paragraph 0106) or receives direct current (DC) signals at the electrodes [Examiner’s note, the claim comprises multiple limitations; however, only one of the two alternatives needs to be supported by the prior art.]. One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teachings of respiratory impedance signals from Russell. Doing so would allow the device to capture the patient's respiration rate by detecting the rising and falling of the thorax or diaphragm (Russell | Paragraphs 0026-0028).
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kaib in view of Parker, Abeyratne, Li, Nakajima, and Selim et al. (“The Association between Nocturnal Cardiac Arrhythmias and Sleep-Disordered Breathing: The DREAM Study,” 2016).
Regarding claim 21, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1. Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching determining the potential sleep disorder is further based on: comparing a second trend of a portion of the patient data with a health disorder index of a different health disorder of the patient; correlating the first trend with the second trend; and determining that the correlation indicates the potential sleep disorder.
Selim teaches determining the potential sleep disorder is further based on: comparing a second trend of a portion of the patient data with a health disorder index of a different health disorder of the patient (Selim | Page 829 – Methods Paragraph); correlating the first trend with the second trend (Selim | Page 829 – Methods and Conclusions Paragraphs; [Examiner’s note, the first trend is regarding sleep disordered breathing and the second trend is regarding cardiac arrhythmias.]); and determining that the correlation indicates the potential sleep disorder (Selim | Figure 3; Page 829 – Conclusions Paragraph; Page 835 – Left Column First Paragraph). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teachings of determining a potential sleep disorder based on two or more sleep disorder indexes from Selim. Doing so would allow physicians to provide more accurate treatment and diagnosis by understanding the high association between cardiac conditions and sleep disorders (Selim | Page 836 – Left Column Last Paragraph to Right Column First Paragraph).
Regarding claim 22, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1. Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching determining the potential sleep disorder is further based on: comparing a second trend of a portion of the patient data with a health disorder index of a different health disorder of the patient; correlating the first trend with the second trend; and determining that the correlation indicates the potential sleep disorder.
Selim teaches determining the potential sleep disorder is further based on: comparing a second trend of a portion of the patient data with a health disorder index of a different health disorder of the patient (Selim | Page 829 – Methods Paragraph); correlating the first trend with the second trend (Selim | Page 829 – Methods and Conclusions Paragraphs; [Examiner’s note, the first trend is regarding sleep disordered breathing and the second trend is regarding cardiac arrhythmias.]); and determining that the correlation indicates the potential sleep disorder (Selim | Figure 3; Page 829 – Conclusions Paragraph; Page 835 – Left Column First Paragraph). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teachings of determining a potential sleep disorder based on two or more sleep disorder indexes from Selim. Doing so would allow physicians to provide more accurate treatment and diagnosis by understanding the high association between cardiac conditions and sleep disorders (Selim | Page 836 – Left Column Last Paragraph to Right Column First Paragraph).
Claims 23 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Kaib in view of Parker, Abeyratne, Li, Nakajima, and Cairns et al. (Sex differences in sleep apnea predictors and outcomes from home sleep apnea testing, 2016, reference V on PTO-892).
Regarding Claim 23, Kaib in view of Parker, Abeyratne, Li, and Nakajima teaches the system of claim 1. Kaib in view of Parker, Abeyratne and Nakajima is silent in teaching two sleep disorder indexes. Li teaches two or more sleep disorder indexes (Li | Table 2; Figures 4-5; Page 599, Statistical Analysis through ROC Analysis; [Examiner’s note, the sleeps factors, EEG and respiration data, are used to determine two or more sleep disorder indexes which are AHI, apnea index (AI), hypopnea index (HI), and RERA index (RERAI).]) One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, and Nakajima to incorporate the teachings of determining a potential sleep disorder based on two or more sleep disorder indexes from Li. Doing so would allow the physician to receive comprehensive patient data to produce an accurate sleep disorder diagnosis and tailored treatment (Li | Page 597 under Conclusions).
Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching determining the sleep disorder indexes further includes accessing one or more patient characteristics that may impact the sleep factors, wherein defining the at least one sleep indicator includes assigning associated weights to the sleep factors based on the impact of one or more patient characteristics, and wherein determining the sleep disorder indexes includes applying the sleep factors with the associated weights.
Cairns teaches wherein determining the sleep disorder indexes further includes accessing one or more patient characteristics that may impact the sleep factors (Cairns | Tables 1 and 2; [Examiner’s note, the patient characteristics being evaluated are age, sex, BMI, and patient medical history.]), wherein defining the at least one sleep indicator includes assigning associated weights to the sleep factors based on the impact of one or more patient characteristics (Cairns | Pages 200- 202 – Section regarding Domains 1-4; [Examiner’s note, this study examines how patient characteristics, such as age, sex, BMI, and medical history, correlate with sleep indexes, particularly those measuring sleep apnea and hypopnea. The results show sex-based differences: snoring frequency correlates equally with sleep apnea/hypopnea in both sexes, whereas hypertension shows a stronger correlation in females. Overall, age, sex, BMI, and medical history carry varying weights in predicting a patient's likelihood of a sleep disorder.]), and wherein determining the sleep disorder indexes includes applying the sleep factors with the associated weights (Cairns | Pages 200- 202 – Section regarding Domains 1-4). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teaching of patient characteristics with sleep indexes from Cairns because females with obstructive sleep apnea/hypopnea often show different symptoms than males. Because of this, they are less likely to visit a sleep specialist or get a referral for a sleep test (Cairns | Page 198, Left Column, 2nd Paragraph).
Regarding Claim 24, Kaib in view of parker, Abeyratne, Li, and Nakajima teaches the method of claim 14. Kaib in view of Parker, Abeyratne, and Nakajima is silent in teaching two sleep disorder indexes. Li teaches two or more sleep disorder indexes (Li | Table 2; Figures 4-5; Page 599, Statistical Analysis through ROC Analysis; [Examiner’s note, the sleeps factors, EEG and respiration data, are used to determine two or more sleep disorder indexes which are AHI, apnea index (AI), hypopnea index (HI), and RERA index (RERAI).]) One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, and Nakajima to incorporate the teachings of determining a potential sleep disorder based on two or more sleep disorder indexes from Li. Doing so would allow the physician to receive comprehensive patient data to produce an accurate sleep disorder diagnosis and tailored treatment (Li | Page 597 under Conclusions).
Kaib in view of Parker, Abeyratne, Li, and Nakajima is silent in teaching wherein determining the two or more sleep disorder indexes further includes accessing one or more patient characteristics that may impact the sleep factors, wherein defining the at least one sleep indicator includes assigning associated weights to the sleep factors based on the impact of one or more patient characteristics, and wherein determining the two or more sleep disorder indexes includes applying the sleep factors with the associated weights.
Cairns teaches wherein determining the sleep disorder indexes further includes accessing one or more patient characteristics that may impact the sleep factors (Cairns | Tables 1 and 2; [Examiner’s note, the patient characteristics being evaluated are age, sex, BMI, and patient medical history.]), wherein defining the at least one sleep indicator includes assigning associated weights to the sleep factors based on the impact of one or more patient characteristics (Cairns | Pages 200- 202 – Section regarding Domains 1-4; [Examiner’s note, this study examines how patient characteristics, such as age, sex, BMI, and medical history, correlate with sleep indexes, particularly those measuring sleep apnea and hypopnea. The results show sex-based differences: snoring frequency correlates equally with sleep apnea/hypopnea in both sexes, whereas hypertension shows a stronger correlation in females. Overall, age, sex, BMI, and medical history carry varying weights in predicting a patient's likelihood of a sleep disorder.]), and wherein determining the sleep disorder indexes includes applying the sleep factors with the associated weights (Cairns | Pages 200- 202 – Section regarding Domains 1-4). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Kaib in view of Parker, Abeyratne, Li, and Nakajima to incorporate the teaching of patient characteristics with sleep indexes from Cairns because females with obstructive sleep apnea/hypopnea often show different symptoms than males. Because of this, they are less likely to visit a sleep specialist or get a referral for a sleep test (Cairns | Page 198, Left Column, 2nd Paragraph).
Response to Arguments
The applicant’s arguments and amendments filed 01/14/2026 have been fully considered.
Regarding the 35 U.S.C 101 Rejection, the amendments to the claims do not overcome the rejection because the claims meet Step 2A Prong 1, Step 2B Prong 2, and Step 2B. The amended claims meet Step 2A Prong 1 because the claims discuss determining the user’s sleep disorder; the claims are read as a mental process. The amended claims meet Step 2A Prong 2 because the abstract idea in each of independent claims are not integrated into a practical application, referring to Step 2A, Prong 2 for further clarification. Lastly, the amended claims meet Step 2B because the additional elements of a wearable device, electrodes, and accelerometer are well-known, routine, and conventional as taught in US Reference 9372533 B1; the additional element of a processor is a generically claimed computer component.
Regarding the 35 U.S.C. 103 Rejection, the examiner agrees that current prior art does not teach the claim limitations found in the amended claims. Due to the scope changes from the amended claims, further search and consideration was required. New references, Parker et al. (US 20170161462 A1), Abeyratne et al (US-20120004749-A1), Nakajima et al. (US-20190159723-A1), and Cairns et al. (Sex differences in sleep apnea predictors and outcomes from home sleep apnea testing, 2016, reference V on PTO-892), are used to modify the wearable device of Kaib. Please refer to the 35 U.S.C 103 Rejection for further clarification.
Conclusion
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/SRISTI DIVINA GOMES/Examiner, Art Unit 3791
/DANIEL L CERIONI/Primary Examiner, Art Unit 3791
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