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 Objections
Claim 5 and 9 are objected is to because of the following informalities:
Claim 5, line 3: “a autoregressive” should be replaced with –an autoregressive–;
Claim 9, line 15: “are taken” should be replaced with –is taken–.
Appropriate correction is required.
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 (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 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “and A, R, 1, 2 ,3 correspond to five sleep stages respectively” in lines 11-12, which is indefinite. How does A, R, 1, 2, and 3 correspond to five sleep stages, respectively? The claim language suggests that the A, R, 1, 2, and 3 each correspond to all five sleep stages, but it is unclear based on the specification whether they “respectively” correspond to all of the five sleep stages? For the purposes of examination, the recitation will be interpreted to be “and A, R, 1, 2, and 3 correspond to one of five of the sleep stages, respectively. Claim 9 recites a similar limitation, so it is rejected on similar grounds.
Claim 1 recites “taking a plurality of sliding windows, the length of which is L” in line 13. It is unclear whether L corresponds to the length of each sliding window or the number of sliding windows. Applicant’s arguments filed 05/26/2026 indicate that L is the length of the plurality of sliding windows, but it is not clear what a “length of a plurality of sliding windows” means. Although there may be a number which corresponds to the plurality of sliding windows, a length of the plurality is not clear. For the purposes of examination, the recitation will be interpreted to be “taking a plurality of sliding windows, each of the sliding windows having a length L,”. Claim 9 recites a similar limitation, so it is rejected on similar grounds.
Claim 1 recites “taking a plurality of sliding windows, the length of which is L, out of the sequence of sleep stage X(i) as sleep patterns of sleep for each historical data in a set of historical data HX={ X(1, X(2), …, X(n-1)} to form a set of sliding window AL(X), in which a set of all sliding windows in the historical data is HA which satisfies HA = U {AL(h) | h ∈Hx}=AL(X(1)) U AL(X(2)) U … U AL(X(n-1))” in lines 7-11, which is indefinite. It is unclear what it means to take a plurality of sliding windows out of a sequence as sleep patterns of sleep for historical data. Although Fig. 5 depicts a plurality of sliding windows of historical data, it does not clearly depict the above relationship. Claim 9 recites a similar limitation, so it is rejected on similar grounds.
Claim 1 recites “the abnormal score” in line 23. There is insufficient antecedent basis for this limitation because the claim does not previously recite an “abnormal score”. For the purposes of examination, the recitation will be interpreted to be “the anomaly score”. Claim 9 recites “the abnormal score” twice, so it is rejected on similar grounds.
Claim 1 recites “taking calculation of the risk assessment function of the anomaly V(X(i), fr, L) for the sequence of sleep stage X(i) as the abnormal score of the sequence of sleep stage X(i)” in lines 19-21. It is unclear what it means to “take calculation of the risk assessment function… as the abnormal score”. Is the function or the calculation the abnormal score? If the calculation is the score, it is unclear how an action (i.e., the calculation) is a score. What does it mean to “take” something “as the abnormal score”. Claim 9 recites a similar limitation, so it is rejected on similar grounds.
Claims 2-8 are rejected by virtue of their dependence from claim 1. Claim 10 is rejected by virtue of its dependence from claim 9.
Claim 7 recites “calculating the anomaly score of the sequence of sleep stage X(i) with the risk assessment function of the anomaly V(X(i), fr, L)” in the last two lines. Claim 1 recites “taking calculation of the risk assessment function of the anomaly V(X(i), fr, L) for the sequence of sleep stage X(i) as the abnormal score of the sequence of sleep stage X(i)” in lines 19-21. It is unclear whether these calculations are the same as, related to, or different from each other. For example, are there two calculation steps or just one? The Examiner suggests deleting the recitation of “; and calculating the anomaly score of the sequence of sleep stage X(i) with the risk assessment function of the anomaly V(X(i), fr, L)” in claim 7.
Claim 9 recites “assess an anomaly score of the sequence of sleep stage X(i) with an anomaly detection technique for a discrete sequence,” in lines 10-11 followed by a plurality of limitations in lines 12-27. The indentation of the limitations of lines 12-27 suggests that the limitations of lines 12-27 somehow further limit the limitations of lines 10-11, but the claim does not clarify how the limitations are related. Clarification is required. If the limitations of lines 12-27 are a part of the assessment of the anomaly score, the relationship should be made clear. If the limitations of lines 12-27 are not a part of the assessment of the anomaly score, the additional indentations should be deleted and the recitation of “and” in line 24 should be deleted.
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 1-10 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. Claims 1-3, 5-9, 11-15, and 17-18 do not include additional elements that integrate the exception into a practical application of the exception or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, p. 50, January 7, 2019), and the 2024 Guidance Update on Patent Subject Matter Eligibility (Federal Register, Vol. 89, No. 137 p. 58128, July 17, 2024).
The analysis of claim 9 is as follows:
Step 1: Claim 9 is directed to a device, which is a statutory category.
Step 2A - Prong 1: Claim 9 is directed to an abstract idea in the form of a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. Additionally or alternatively, claim 9 is directed to an abstract idea in the form of mathematical algorithms and/or formulas.
In particular, claim 9 recites the following limitations:
[A1]: divide the EEG signal into sections and determine that which sleep stage each section of the EEG signal is to classify each section of the EEG signal into a plurality of sleep stages so as to get a sequence of sleep stage X(i);
[B1]:assess an anomaly score of the sequence of sleep stage X(i) with an anomaly detection technique for a discrete sequence, wherein the sequence of sleep stage X(i) as X(n) = (X(n)(1), X(n)(2), X(n)(3),… X(n)(m)) is set, in which X(i)(j) belongs to a set of {A, R, 1, 2, 3}, and A, R, 1, 2, 3 correspond to five of the plurality of sleep stages, respectively, a plurality of sliding windows, the length of which is L, are taken out of the sequence of sleep stage X(i) as patterns of sleep for each historical data in a set of historical data Hx= { X(1), X(2), … X(n-1)} to form a set of sliding window AL(X), in which a set of all of the sliding windows in the historical data is HA, HA= U {AL(h) | h ϵ HX} = AL(X(1)) U AL(X(2)) U… U AL(X(n-1)), and the plurality of sliding windows contain a series of continuous data of the sequence of sleep stage X(i), a risk assessment function of the anomaly V(X(i), fr, L) for the sequence of sleep stage X(i) is calculated, in which fr(∙) is a function determining a sleep pattern of the sleep disorder, and calculation of the risk assessment function of the anomaly V(X(i), fr, L) for the sequence of sleep stage X(i) is taken as the abnormal score of the sequence of sleep stage X(i);
[C1]: determine if the sequence of sleep stage X(i) represents the sleep disorder based on a predetermined threshold η, wherein when the abnormal score satisfies V(X(i), Fr, L)>η, the sequence of sleep stage X(i) is determined to represent the sleep disorder.
These elements [A1]-[C1] of claim 9 are directed to an abstract idea because they are processes that, under their broadest reasonable interpretation, are mere steps that are capable of being mentally performed with the aid of pen and paper. For example, a skilled artisan is capable of classifying sections of an EEG signal to arrive at a sequence of sleep stages, determining an anomaly score of the sequence of sleep stages based on a risk assessment function, determining sets of the sequence of sleep stages, calculating a risk assessment function, setting a value of a risk assessment function as the anomaly score, and determining whether the score or function is greater than a predetermined threshold. Additionally or alternatively, the elements [B1]-[C1] are directed to an abstract idea because they are directed to mathematical algorithms and/or formulas. See at least ¶¶ [0022]-[0023] of the specification with regards to the mathematical nature of the elements.
Step 2A - Prong Two: Claim 9 does not recite additional elements that integrate the judicial exception into a practical application. Claim 9 recites the following additional elements:
[A2]: a communication unit receiving an EEG signal from an EEG sensor
[B2]: a programming unit.
[C2]: a feature extraction algorithm;
[D2]: a machine learning algorithm.
The elements [A2]-[D2] do not integrate the exception into a practical application of the exception.
The elements [A2] and [B2] do not integrate the exception into a practical application of the exception because the elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - See MPEP 2106.04(d) and MPEP 2106.05(f).
The elements [C2] and [D2] do not integrate the exception into a practical application of the exception because the elements amount to (A) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - See MPEP 2106.04(d) and MPEP 2106.05(f); (B) generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.04(d) and MPEP 2106.05(h); and/or (C) merely adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.04(d); MPEP 2106.05(g).
Accordingly, each of the additional elements do not integrate the abstract into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Step 2B: Claim 9 does not recite additional elements that amount to significantly more than the judicial exception itself. Claim 9 recites the following additional elements:
[A2]: a communication unit receiving an EEG signal from an EEG sensor
[B2]: a programming unit.
[C2]: a feature extraction algorithm;
[D2]: a machine learning algorithm.
The elements [A2]-[D2] do not amount to significantly more than the judicial exception itself.
Simply reciting the elements [A2]-[D2] not qualify as significantly more because these elements are (A) simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (See MPEP 2106.05(d)(II); Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93); (B) generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h); and/or (C) merely adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g).
Additionally, the elements are well-understood, routine, and conventional. With regards to element [C2], US 2014/0316230 A1 (Denison) (previously cited) discloses that typically, past research includes identifying mental states using Fourier transforms and applying algorithms that recognize EEG waveform features associated with a particular state at ¶ [0007]. With regards to element [D2], US 2020/0367810 A1 (Shouldice) (previously cited) discloses a recurrent neural network (RNN) is a standard neural network structure, wherein LSTM RNN is well known at ¶ [0242].
In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Independent claim 1 recite a mirrored method limitations and are not patent eligible for substantially similar reasons.
Claims 2-8 depend from claim 1, and they recite the same abstract idea as claim 1. Claims 10 depend from claim 9, and they recite the same abstract idea as claim 9. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process or mathematical algorithm) and/or append abstract ideas (that is, the claims only recite limitations that add further mental processes or mathematical algorithms) except for the following limitations.
Claim 4 recites “the machine learning algorithm comprises one of convolution neural network (CNN), recurrent neural network (RNN) and random forests”. However the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amounts to (A) generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.04(d) and MPEP 2106.05(h); and/or (B) merely adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.04(d); MPEP 2106.05(g). Additionally, the element is well-understood, routine, and conventional. US 2020/0367810 A1 (Shouldice) (previously cited) discloses a recurrent neural network (RNN) is a standard neural network structure, wherein LSTM RNN is well known at ¶ [0242].
Claim 5 recites “the feature extraction algorithm comprises one of Fourier transform, waveform transform, short-time Fourier transform and autoregressive model extracting a feature”. However the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amounts to (A) generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.04(d) and MPEP 2106.05(h); and/or (B) merely adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.04(d); MPEP 2106.05(g). Additionally, the element is well-understood, routine, and conventional. US 2014/0316230 A1 (Denison) (previously cited) discloses that typically, past research includes identifying mental states using Fourier transforms and applying algorithms that recognize EEG waveform features associated with a particular state at ¶ [0007].
Claim 10 recites “the device is a mobile phone, the communication unit of which is one of a Bluetooth wireless communication unit and a Wi-Fi wireless communication unit”. However the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amounts to (A) simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry; (B) generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h); and/or (C) merely adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Additionally, the element is well-understood, routine, and conventional. US 2019/0365342 A1 (Ghaffarzadegan) (previously cited) teaches transceivers that are common to smart phones and/or smart watches, such as Wi-Fi transceivers and transceivers configured to communicate via for wireless telephony networks at ¶ [0023].
In view of the above, the additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0069236 A1 (Modarres) (previously cited) in view of CN 113925459 A (Wang), US 2015/0190086 A1 (Chan) (previously cited), and US 2022/0058211 A1 (Wismüller) (previously cited).
With regards to claims 1 and 9, Modarres teaches a method and device of detecting a sleep disorder based on an electroencephalography (EEG) signal (¶ [0005] teaches a method of detecting post-traumatic stress disorder using a brain wave pattern. ¶ [0212] which indicates that disturbed sleep is a core feature of PTSD; ¶ [0057] discloses applying the methods of analysis to insomnia and REM behavioral disorder); ¶¶ [0008], [0009] depict a system comprising an EEG device, a storage device, and a processor), comprising: a communication unit, for receiving the EEG signal from an EEG sensor (¶ [0008] discloses the processor receiving a brain wave pattern from the EEG device, which requires a communication unit; also see ); and a programming unit (¶ [0008] discloses the processor is configured to detect PTSD) configured to perform the steps of: dividing the EEG signal into sections (¶¶ [0005], [0073], [0234] discloses analyzing data from the polysomnography and brain wave patterns in 30-0.1 second intervals), and determining that which sleep stage each section of the EEG signal is (¶¶ [0005], [0071]-[0072], [0234] discloses segmenting the brain wave pattern into sleep stages) so as to get a sequence of sleep stage X(i) (¶¶ [0005], [0234] disclose arriving at a sequence of occurrence of a particular sleep stage and fluctuation patterns across sleep stages); assessing an anomaly score of the sequence of sleep stage X(i) with an anomaly detection technique for a discrete sequence (¶ [0241] discloses the neuromarkers of coherence and phase delays were computed on a micro-level using a short duration of less than 5 seconds guided by the underlying macro structure of the brain state belonging to one of the 5 sleep stages; ¶ [0243] discloses determining a value of one or more of the neuromarkers); calculating a risk assessment function of an anomaly V(X(i), Fr, L) for the sequence of sleep stage X(i), in which fr(∙) is a function determining a sleep pattern of the sleep disorder (¶¶ [0005], [0243] discloses detecting PTSD in a subject by determining if the value of the one or more neuromarkers is above a designated threshold; ¶ [0007] discloses measurements of inter-hemispheric and intra-hemispheric coherences and phase delay comprise measurement of transition between sleep stages (i.e., the neuromarker is a function based upon the sequence of sleep stages); ¶ [0299] discloses coherence values are computed using 5-second sliding windows (i.e., the neuromarker is dependent upon the sliding window); ¶ [0059] discloses coherence value is a the magnitude of normalized cross-power spectrum (i.e., a function for use in the determination of PTSD), and calculation of the risk assessment function of the anomaly V(X(i), fr, L) for the sequence of sleep stage X(i) is taken as the abnormal score of the sequence of sleep stage X(i); determining if the sequence of sleep stage X(i) represents the sleep disorder based on a predetermined threshold η, wherein when the anomaly score satisfies V(X(i), Fr, L)>η, the sequence of sleep stage X(i) is determined to represent the sleep disorder, (¶¶ [0005], [0243] discloses detecting PTSD in a subject by determining if the value of the one or more neuromarkers is above a designated threshold; ¶ [0007] discloses measurements of inter-hemispheric and intra-hemispheric coherences and phase delay comprise measurement of transition between sleep stages (i.e., the neuromarker is a function based upon the sequence of sleep stages)).
Modarres is silent regarding whether determining that which sleep stage each section of the EEG signal is through a feature extraction algorithm and a machine learning algorithm.
In a system relevant to the problem of detecting sleep stages, Wang teaches determining that which sleep stage each section of the EEG signal is through a feature extraction algorithm and a machine learning algorithm (Page 3 of the attached machine translation of Wang teaches a sleep staging method based on wavelet transform and a 1D-CNN and VGG network). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the sleep stage of Modarres such that it uses a feature extraction algorithm and a machine learning algorithm as taught by Wang. The motivation would have been to provide a more accurate and automatic sleep staging method.
The above combination is silent regarding whether the step of assessing an anomaly score of the sequence of sleep stage X(i) with an anomaly detection technique for a discrete sequence further comprises: setting the sequence of sleep stage X(i) as X(n) = (X(n)(1), X(n)(2), X(n)(3),… X(n)(m)), in which X(i)(j), belongs to a set of {A, R, 1, 2, 3} and A, R, 1, 2, 3 correspond to five sleep stages respectively; and taking a plurality of sliding windows, the length of which is L, out of the sequence of sleep stage X(i) as sleep patterns of sleep for each historical data in a set of historical data HX={ X(1, X(2), …, X(n-1)} to form a set of sliding window AL(X), in which a set of all sliding windows in the historical data is HA which satisfies HA = U {AL(h) | h ∈Hx}=AL(X(1)) U AL(X(2)) U … U AL(X(n-1))
In a system relevant to the problem of detecting sleep stages, Chan teaches setting the sequence of sleep stage X(i) as X(n) = (X(n)(1), X(n)(2), X(n)(3),… X(n)(m)), in which X(i)(j), belongs to a set of {A, R, 1, 2, 3} and A, R, 1, 2, 3 correspond to five sleep stages respectively (Fig. 3 sequence of sleep stage in graph form, wherein the sequence belongs to a set of W, N1, N2, N3, and REM). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate that the sequence of sleep stage X(i) is set as X(n) = (X(n)(1), X(n)(2), X(n)(3),… X(n)(m)), in which X(i)(j), belongs to a set of {A, R, 1, 2, 3} and A, R, 1, 2, 3 correspond to five sleep stages respectively, as taught by Chan. Because both sets of sleep stages are capable of being used for depicting a patient’s sleep progression (¶ [0002] of Chan; ¶ [0072] of Modarres), it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
In a system relevant to the problem of providing training data for machine learning algorithms, Wismüller teaches taking a plurality of sliding windows, the length of which is L, out of the sequence for each historical data in a set of historical data HX={ X(1, X(2), …, X(n-1)} to form a set of sliding window AL(X), in which a set of all sliding windows in the historical data is HA which satisfies HA = U {AL(h) | h ∈Hx}=AL(X(1)) U AL(X(2)) U … U AL(X(n-1)) (¶ [0111] teaches extracting sliding windows U(t) of size qxe from time-series Q for each time t, which amounts to extracting past data and forming a set of sliding windows which satisfies the above condition). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate, based on the teachings of Wismüller, taking a plurality of sliding windows, the length of which is L, out of the sequence of sleep stage X(i) as sleep patterns of sleep for each historical data in a set of historical data HX={ X(1, X(2), …, X(n-1)} to form a set of sliding window AL(X), in which a set of all sliding windows in the historical data is HA which satisfies HA = U {AL(h) | h ∈Hx}=AL(X(1)) U AL(X(2)) U … U AL(X(n-1)). The motivation would have been to provide historical data that allows for a more complete diagnostic analysis of the patient.
With regards to claim 2, the above combination teaches or suggests the plurality of standard sleep stages which comprise awake stage, REM stage, N1 stage, N2 stage and N3 stage (¶ [0072] of Modarres teaches that the sleep stages include an awake period, stage I sleep, stage II sleep, stable III sleep, and REM sleep; also see ¶ [0286] and Fig. 1 of Modarres).
With regards to claim 3, the above combination teaches or suggests the EEG signal is divided into sections of a fixed length which is between 10 seconds and 1 minute (¶ [0072] of Modarres teaches the defined period of less than 30 seconds; ¶ [0234] of Modarres discloses classifying the macro-structure (30-second epoch) of EEG).
With regards to claim 4, the above combination teaches or suggests the machine learning algorithm comprises one of convolutional neural network (CNN), recurrent neural network (RNN) and random forests (Page 3 of the attached machine translation of Wang teaches a sleep staging method based on wavelet transform and a 1D-CNN and VGG network).
With regards to claim 5, the above combination teaches or suggests the feature extraction algorithm comprises one of Fourier transform, wavelet transform,short-time Fourier transform and autoregressive model extracting a feature (Page 3 of the attached machine translation of Wang teaches a sleep staging method based on wavelet transform and a 1D-CNN and VGG network).
With regards to claim 8, the above combination teaches or suggests the abnormal score of the sequence of sleep stage X(i) represents an extent of sleep disorder, and the higher the anomally score of the sequence of sleep stage X(i) is, the greater the an extent of sleep disorder is (¶ [0128] of Modarres teaches that the one or more neuromarkers correlates with a severity of PTSD, wherein the higher value signifies greater severity of the PTSD symptom).
With regards to claim 10, the above combination is silent regarding whether the device is a mobile phone, the communication unit of which is one of a Bluetooth wireless communication unit and a Wi-Fi wireless communication unit.
In related embodiment, Modarres teaches implementing the methods and processing functions can be implemented using computer hardware (¶¶ [0257]-[0258]), the device is a mobile phone (¶ [0260] discloses a computing machine 200 being a mobile phone or smartphone), the communication unit of which is one of a Bluetooth wireless communication unit and a Wi-Fi wireless communication unit (¶ [0267] discloses the network interface 2070 of the computing machine 2000 using connections through wide area networks (WAN), local area networks (LAN), intranets, the Internet, wireless access networks, wired networks, mobile networks, telephone networks, optical networks, or combinations thereof). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Modarres to incorporate the device is a mobile phone, the communication unit of which is one of a Bluetooth wireless communication unit and a Wi-Fi wireless communication unit, as taught by the related embodiment of Modarres. The motivation would have been to provide the hardware necessary for implementing the data processing method.
No Prior Art Rejection of Claim 7
With regards to claim 7, the prior art does not teach or suggest defining C(<x,y>i, HA) = |{ala ϵ HA and <x,y>i ϵ Blo(a)}| in which <x,y>i is a lookahead pair, C(<x,y>i, HA) represents a number of <x,y>i in the set HA, and |∙| represents an element number of a set; defiing the function determining a sleep pattern of sleep disorder fr(∙), an input of which is a sleep pattern a, as fr(a)=1 if |{z|zϵBlo(a) and C(z, HA)/|HA|<θ}|>0, and fr(a)=0 if |{z|zϵBlo(a) and C(z, HA)/|HA|<θ}|=0, in which θ is another predetermined threshold; defiing the risk assessment function of anomaly V(X(i), fr, L) as V(X(i), fr, L) = (sum{fr(a)|aϵAl(X(i))})/(|X(i)|+L-1), 0≤V(X(i), fr, L)≤1” along with the other features of claim 7.
Response to Arguments
Claim Objections
In view of the amendments filed 05/26/2026, the previous grounds of claim objections were withdrawn. There are new grounds of claim objections necessitated by the claim amendments filed 05/26/2026.
Rejections under 35 U.S.C. §112(b)
There are new grounds of rejections under 35 U.S.C. §112(b) necessitated by the claim amendments filed 05/26/2026.
Applicant’s arguments filed 05/26/2026, with respect to “U” not needing a limit, have been fully considered and are persuasive. The corresponding rejection under 35 U.S.C. §112(b) has been withdrawn.
Applicant's remaining arguments filed 05/26/2026 have been fully considered but they are not persuasive.
On page 10 of the response filed 05/26/2026, the Applicant asserts:
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This argument is not persuasive because it is not clear what a “length of the plurality of sliding windows” means. Although there may be a number of sliding windows, a length of the plurality is not clear. For the purposes of examination, the recitation will be interpreted to be “taking a plurality of sliding windows, each of the sliding windows having a length L,”. Claim 9 recites a similar limitation, so it is rejected on similar grounds.
On page 10 of the response filed 05/26/2026, the Applicant asserts:
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This argument is not persuasive because “each of the A, R, 1, 2, 3 corresponds to one of five sleep stages in the given order” has a different meaning from “A, R, 1, 2, 3 correspond to five of the plurality of sleep stages, respectively”. The Examiner suggests amending the claims to recite “each of the A, R, 1, 2, 3 corresponds to one of five sleep stages, respectively”.
On page 10 of the response filed 05/26/2026, the Applicant asserts:
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This argument is not persuasive because it amounts to a general allegation that “taking calculation of the risk assessment function of the anomaly V(X(i), fr, L) for the sequence of sleep stage X(i) as the abnormal score of the sequence of sleep stage X(i)” in lines 19-21 is clear without particularly pointing out how the claim language is clear based on the disclosures of Figs. 4 and 5.
Rejections under 35 U.S.C. §101
Applicant's arguments filed 05/26/2026 have been fully considered but they are not persuasive.
On page 11 of the remarks filed 05/26/2026, the Applicant asserts:
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This argument is not persuasive. First, MPEP 2106.05(a)(II) discloses that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. MPEP 2106.05(a) further discloses that the judicial exception alone cannot provide the improvement; the improvement can be provided by one or more additional elements. The Examiner notes that the determination of the anomaly score is a mental process and/or mathematical algorithm for the reasons, Therefore, the determination of the anomaly score alone, does not amount to an improvement to technology. Second, the assertion that the determination of the anomaly score cannot be mentally performed with the aid of pen and paper amounts to a general allegation that it is not a mental process without providing sufficient evidence. The Examiner maintains that the recited steps are capable of being performed in the mind for the reasons listed in the above rejection.
Rejections under 35 U.S.C. §103
Applicant's arguments filed 05/26/2026 have been fully considered but they are not persuasive.
Applicant asserts that nothing in Modarres, Wang, Chan, and Wismuller, or the proposed combination teaches or suggests the added limitations of claims 1 and 9. The Examiner notes that the added limitations are substantially similar to those of previous claim 6, which were rejected under Modarres in view of Wang, Chan, and Wismuller. However, Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references used in the previous rejection of claim 6. Therefore, the Applicant’s arguments are not 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.
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/S.C.K./Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
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