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
Claims 1-10 are objected to because of the following informalities:
Claims 1 and 9, line 1: –a– should be inserted before “sleep disorder”;
Claims 1 and 9, line 1: –an– should be inserted before “electroencephalography”;
Claim 1, lines 3, 7, and 9 should be indented;
Claims 1 and 9, line 3: “an EEG” should be replaced with –the EEG–;
Claim 1, lines 9 and 12: –the– should be inserted before “sleep disorder”;
Claim 1, lines 9-12: “determining if the sequence of sleep stage X(i) represents sleep disorder with a predetermined threshold η, so that if a risk assessment function of anomaly V(X(i), Fr, L) satisfies V(X(i), Fr, L)>η, determining the sequence of sleep stage X(i) represents sleep disorder” should be replaced with –determining if the sequence of sleep stage X(i) represents the sleep disorder based on a predetermined threshold η, wherein when a risk assessment function of an anomaly V(X(i), Fr, L) satisfies V(X(i), Fr, L)>η, the sequence of sleep stage X(i) is determined to represent the sleep disorder–;
Claims 2-8 and 10: in the preambles, –the– should be inserted before each of “sleep disorder” and “EEG signal”;
Claim 2, line 2: “a EEG” should be replaced with –the EEG–;
Claim 2, line 3: “a plurality” should be replaced with –the plurality–;
Claim 2, line 4: “a feature” should be replaced with –the feature–;
Claim 2, line 4: “a machine” should be replaced with –the machine–;
Claim 2, line 5: “a sequence” should be replaced with –the sequence–
Claim 2, line 6: the line should be indented;
Claim 4, line 2: –a– should be inserted before “convolutional”;
Claim 4, line 3: –a– should be inserted before “recurrent”;
Claim 5, line 2: –a– should be inserted before each of “Fourier” and “wavelet”;
Claim 5, line 3: –a– should be inserted before each of “short-term” and “autoregressive”;
Claim 6, line 2: both instances of “an anomaly” should be replaced with –the anomaly–;
Claim 6, line 3: “a discrete” should be replaced with –the discrete–;
Claim 6, line 5: both instances of “2 ,3” should be replaced with –2, 3–;
Claim 7, line 2: both instances of “an anomaly” should be replaced with –the anomaly–;
Claim 7, line 3: “a discrete” should be replaced with –the discrete–;
Claim 7, lines 4, 8, 10, 13, 16, and 18 should be indented;
Claim 7, line 4: –a– should be inserted after “sleep pattern”;
Claim 7, line 7: “and” should be deleted;
Claim 7, lines 13 and 14: “a sleep pattern” should be replaced with –the sleep pattern–;
Claim 7, line 13: –the– should be inserted before “sleep disorder”;
Claim 8, lines 2 and 4: –the– should be inserted before “sleep disorder”;
Claim 8, line 3: “an” should be deleted;
Claim 9, line 1: “of” should be replaced with –for–;
Claim 9, lines 5, 9, and 11 should be indented;
Claim 9, line 5: “an EEG” should be replaced with –the EEG–;
Claim 9, lines 11-14: “determine if the sequence of sleep stage X(i) represents sleep disorder with a predetermined threshold η, so that if a risk assessment function of anomaly V(X(i), Fr, L) satisfies V(X(i), Fr, L)>η, determine the sequence of sleep stage X(i) represents sleep disorder” should be replaced with –determine if the sequence of sleep stage X(i) represents the sleep disorder based on a predetermined threshold η, wherein when a risk assessment function of an anomaly V(X(i), Fr, L) satisfies V(X(i), Fr, L)>η, the sequence of sleep stage X(i) is determined to represent the sleep disorder–;
Claim 9, line 14: –the– should be inserted before “sleep disorder”; and
Claim 10, line 1: “of” should be replaced with –for–.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Programming unit in claim 9 because it uses a generic placeholder (“unit”) that is coupled with functional language (“programming”) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
The limitation is being interpreted to correspond to a processor, microprocessor, central processing unit, as indicated in ¶ [0018] of the specification, and equivalents thereof.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 determining that which sleep stage each section of the EEG signal is through a feature extraction algorithm and a machine learning algorithm so as to get a sequence of sleep stage X(i)” in lines 4-6, which includes a step of classifying each section of the EEG signal into sleep stages. Claim 1 also recites “classifying each section of the EEG signal into a plurality of sleep stages” in lines 3-4. It is unclear how these steps for classifying the sections into sleep stages are related to each other. Are they the same as, related to, or different from each other? For the purposes of examination, “and determining that which sleep stage each section of the EEG signal is through a feature extraction algorithm and a machine learning algorithm so as to get a sequence of sleep stage X(i)” will be interpreted to be –using a feature extraction algorithm and a machine learning algorithm, and generating a sequence of sleep stage X(i)– such that the feature extraction and machine learning algorithm are used for classifying the sections into the plurality of sleep stages. Claims 2 and 9 recite similar limitations, so they are rejected on similar grounds.
Claim 1 recites “assessing an anomaly score” in line 7 and “if a risk assessment function of anomaly V(X(i), Fr, L) satisfies V(X(i), Fr, L)>η, determining the sequence of sleep stage X(i) represents sleep disorder” in lines 10-12. In light of the specification, it is unclear whether the anomaly score and the risk assessment function of anomaly V(X(i), fr, L) are the same as, related to, or different from each other. Paragraphs [0022]-[0023] of the specification suggests that the anomaly/abnormal score is the same as the risk assessment function of the anomaly V(X(i), fr, L). Specifically, the function V(X(i), fr, L) appears to be a value which is then compared to the threshold η, and the anomaly/abnormal score appears to be the same value. However, the claim suggests that they are different because of the different terminology. If they are the same, consistent terminology should be used. If they are related to or different from each other, the relationship should be made clear. The Examiner suggests clarifying the relationship between the anomaly score, the function V(X(i), fr, L), and the predetermined threshold η. Claim 9 recites a similar limitation, so it is rejected on similar grounds. Claim 7 recites “calculate the abnormal score of the sequence of sleep stage X(i) with the risk assessment function of anomaly V(X(i), fr, L)” in lines 18-19, which causes further confusion regarding whether the anomaly/abnormal score is the same as, related to, or different from the function.
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 2 recites “the step of dividing a EEG signal into sections, classifying each section of the EEG signal into a plurality of sleep stages… further comprises: classifying each section of the EEG signal into a plurality of standard sleep stages” in lines 2-6, which is indefinite. It is unclear how the “classifying each section of the EEG signal into a plurality of sleep stages” of lines 2-3 and “classifying each section of the EEG signal into a plurality of standard sleep stages” of line 6 are related. The specification suggests that they are the same step, but the claim language suggests that there are multiple “classifying” steps. For the purposes of examination, claim 2 will be interpreted to recite “The method of detecting the sleep order based on the EEG signal according to claim 1, wherein the plurality of sleep stages comprise an awake stage, an REM stage, an N1 stage, an N2 stage, and an N3 stage”.
Claim 3 recites “wherein the EEG signal is divided into sections of a fixed length which is between…” in lines 1-2. Claim 1 recites “sections” in line 3. It is unclear if these sections are the same as, related to, or different from each other. The specification does not make a distinction between the sections, which suggests that they are the same. However, the different recitations in the claim suggests that they are different. For the purposes of examination, the recitation in claim 3 will be interpreted to be “wherein the sections each have a fixed length between…”.
Claim 6 recites “and A, R, 1, 2 ,3 correspond to five sleep stages respectively” in lines 5-6, which is indefinite. How does A, R, 1, 2, and 3 respectively correspond to five sleep stages? The claim language suggests that the A, R, 1, 2, and 3 each correspond to all five sleep stages, but do 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 an awake stage, an REM stage, an N1 stage, an N2 stage, and an N3 stage, respectively.
Claim 6 recites “taking a plurality of sliding windows, the length of which is L” in line 7. First, it is unclear whether L corresponds to the length of each sliding window or the plurality of sliding windows. Second, it is unclear how the plurality of sliding windows are related to “a sliding window” in line 13 of claim 1. Are they the same as, related to, or different from each other? For the purposes of examination, (A) “a sliding window” in claim 1 will be interpreted to be “a first sliding window” and (B) the recitation in claim 6 will be interpreted to be “taking a plurality of second sliding windows, each having a length L,”.
Claim 6 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. First, 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. Second, it is unclear how “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))” should be interpreted because the phrase appears to be incomplete. Finally, how are “all sliding windows” in lines 9-10 and “a plurality of sliding windows” in line 7 are related to each other? If they are the same, consistent terminology should be used. If they are different, the relationship should be made clear.
Claim 6 recites “HA = U {AL(h) | h ∈Hx}” in line 10, which is indefinite. The term appears to be incomplete because the limits for the union symbol “U” are not defined. Clarification is required.
Claim 7 recites “defining a lookahead pair of a sleep pattern of sleep in the set HA is <x,y>I-” in line 4; “the lookahead pair is a subsequence of a and represented by (am, an)” in lines 5-6; and “<x,y>i is a lookahead pair” in lines 10-11. First, the recitation in line 4 is unclear. What is the relationship between the lookahead pair and <x,y>I and the defining of the lookahead pair? The recitation of “is” makes the relationship unclear, and the recitation should be replaced with “as”. Second, the recitation in line 4 suggests that the lookahead pair is <x,y>I-, lines 5-6 indicate that it is (am, an), and lines 10-11 indicate that a lookahead pair is <x,y>i. It is unclear whether the lookahead pair is <x,y>I, (am, an), and <x,y>i. The Examiner suggests using consistent terminology.
Claim 7 recites “the abnormal score” in line 18. There is insufficient antecedent basis for this limitation in the claim 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 8 recites the same limitation, which will also be interpreted to be “the anomaly score”
Claim 9 recites “a communication unit, receiving an EEG signal” in line 3, which is a method step. A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, because it creates confusion as to when direct infringement occurs. (MPEP 2173.05(p) citing In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 97 USPQ2d 1737 (Fed. Cir. 2011)). The limitation will be interpreted to be “a communication unit configured to receive the EEG signal”.
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 1 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 an EEG signal into sections, classify each section of the EEG signal into a plurality of sleep stages, and determine that which sleep stage each section of the EEG signal is 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;
[C1]: determine if the sequence of sleep stage X(i) represents sleep disorder with a predetermined threshold η, so that if a risk assessment function of anomaly V(X(i), Fr, L) satisfies V(X(i), Fr, L)>η, determine the sequence of sleep stage X(i) represents sleep disorder, in which fr(∙) is a function determine a sleep pattern of sleep disorder, and L is a length of a sliding window.
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 and 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, 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) 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) 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) 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) 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) 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) in view of CN 113925459 A (Wang)
With regards to claims 1 and 9, Modarres teaches a method and device of detecting sleep disorder based on 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, receiving an 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 an EEG signal into sections (¶¶ [0005], [0073], [0234] discloses analyzing data from the polysomnography and brain wave patterns in 30-0.1 second intervals), classifying each section of the EEG signal into a plurality of sleep stages 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); and determining if the sequence of sleep stage X(i) represents sleep disorder with a predetermined threshold η, so that if a risk assessment function of anomaly V(X(i), Fr, L) satisfies V(X(i), Fr, L)>η, determining the sequence of sleep stage X(i) represents sleep disorder, in which fr(∙) is a function determining a sleep pattern of sleep disorder, and L is a length of a sliding window (¶¶ [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)).
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.
With regards to claim 2, the above combination teaches or suggests the step of dividing a EEG signal into sections, classifying each section of the EEG signal into a plurality of sleep stages and determining that which sleep stage each section of the EEG signal is through a feature extraction algorithm and a machine learning algorithm so as to get a sequence of sleep stage X(') further comprises: classifying each section of the EEG signal into a 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 abnormal 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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0069236 A1 (Modarres) in view of CN 113925459 A (Wang), as applied to claim 1 above, and further in view of US 2015/0190086 A1 (Chan) and US 2022/0058211 A1 (Wismüller).
With regards to claim 6, 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.
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.
Conclusion
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/S.C.K./Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791