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 .
Election/Restrictions
Applicant’s election without traverse of Group I (Claims 1-12) in the reply filed on 2nd June 2026 is acknowledged. Claims 13-17 have been canceled. Claims 18-20 are withdrawn from consideration. The subject matter of newly added claims 21-25 falls within the scope of Group I.
Claim Objections
Claim 21 is objected to because of the following informalities:
Claim 21, ‘generating, with a control circuit of the first sensor unit, generating frequency…’ should read ‘generating, with a control circuit of the first sensor unit, frequency…’.
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: ‘generating, with the inertial sensor unit, breathing of the user by performing a classification process based on the sensor data’ & ‘detecting, with the inertial sensor unit, breathing of the user by performing a classification process based on the frequency domain data’ in claim 1, interpreted as an application specific integrated circuit (ASIC) via Pg. 6 of Applicant’s specification which states the sensor unit includes a control circuit, and the control circuit is described as an application specific integrated circuit (ASIC). Further, the limitations: ‘generating, with a control circuit of the first sensor unit, generate frequency domain data based on the first sensor data’, ‘generating, with the control circuit, breathing detection data indicative of breathing of the user based on a spectral energy, a spectral centroid frequency, and a spectral spread of the frequency domain data’ in claim 21, interpreted as an application specific integrated circuit (ASIC) via Pg. 6, lines 17-18 of Applicant’s specification.
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 2-12 & 23-25 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 2, ‘a classification process’, it is unclear if this is referring to or part of ‘a classification process’ as previously recited in claim 1 or if it is referring to a distinct classification process, rendering claim 2 indefinite.
Claim 2, ‘breathing of the user’, it is unclear if this is referring to or part of ‘breathing of the user’ as previously recited in claim 1 or a distinct instance of breathing of the user, rendering claim 2 indefinite. Examiner notes the limitation should likely read ‘the breathing of the user’, as best understood by the disclosure.
Claims 7 & 11, ‘detecting breathing’, it is unclear if this is referring to or part of ‘breathing of the user’ as previously recited in claim 1 or a distinct instance of breathing, rendering claims 7 & 11 indefinite. Examiner notes this limitation should likely read ‘detecting the breathing of the user’ as previously recited.
Claim 7, ‘a group of the windows’, there is insufficient antecedent basis for this limitation in this claim. Examiner notes this limitation should likely read ‘the plurality of windows’.
Claim 7, ‘the group of windows’, it is unclear if this limitation is part of ‘the plurality of windows’ as previously recited or a distinct group of windows separate from the plurality of windows, rendering claim 7 indefinite. Examiner interprets the limitation to read ‘the group of the plurality of windows’.
Claim 7, ‘each window of the group’, there is insufficient antecedent basis for this limitation in this claim. Examiner notes the limitation should likely read ‘the group of the plurality of windows’.
Claim 8, ‘the product’, there is insufficient antecedent basis for this limitation in this claim.
Claim 8, ‘generating a value by dividing the spectral energy by the product of the spectral centroid frequency and spectral spread’, it is unclear how a value is generating by dividing spectral energy by the product of spectral centroid frequency and spectral spread, rendering claim 8 indefinite. Examiner interprets the limitation to read ‘generating a value by dividing a value of the spectral energy by a product of a value of the spectral centroid frequency and a value of the spectral spread’.
Claim 9, ‘the classification algorithm’, there is insufficient antecedent basis for this limitation in this claim. Examiner notes this limitation should likely read ‘the classification process’ as previously recited.
Claim 10, ‘the breathing’, there is insufficient antecedent basis for this limitation in this claim. Examiner notes this limitation should likely read ‘the breathing of the user’ as previously recited.
Claim 12, ‘the wearable electronic device’, there is insufficient antecedent basis for this limitation in this claim. Examiner notes the limitation should likely read ‘the electronic device’ or ‘the electronic device worn by the user’.
Claim 23, ‘detect breathing’, it is unclear if this is referring to or part of ‘breathing of the user’ as previously recited in claim 21 or a distinct instance of breathing, rendering claim 23 indefinite. Examiner notes this limitation should likely read ‘detect the breathing of the user’ as previously recited.
Claim 24, ‘the sensor unit’, there is insufficient antecedent basis for this limitation in this claim. Examiner notes this limitation should likely read ‘the first sensor unit’.
Claims 3-12 & 24-25 are rejected for their dependence on a rejected parent claim.
Claim Rejections - 35 USC § 101
Claims 1-12 & 21-25 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 1-12 & 21-25 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-12 & 21-25 recites at least one step or instruction for 1-12 & 21-25, 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 1-12 & 21-25 recites an abstract idea.
Specifically,
Regarding Claim 1, A method, comprising:
generating, with an inertial sensor unit of an electronic device worn by a user, sensor data based on bone conduction of sound;
generating, with the inertial sensor unit, frequency domain data based on the sensor data (Observation, Judgement, Evaluation/Opinion); and
detecting, with the inertial sensor unit, breathing of the user by performing a classification process based on the frequency domain data (Observation, Evaluation/Opinion).
Regarding Claim 21, A method, comprising:
generating, with an inertial sensor of a first sensor unit, first sensor data based on bone conduction of sound;
generating, with a control circuit of the first sensor unit, generate frequency domain data based on the first sensor data (Observation, Judgement, Evaluation/Opinion);
generating, with the control circuit, breathing detection data indicative of breathing of the user based on a spectral energy, a spectral centroid frequency, and a spectral spread of the frequency domain data (Observation, Judgement, Evaluation/Opinion).
(additional elements are bolded, abstract ideas such as observations, judgements, or evaluations which are grouped as a mental process under the 2019 PEG are underlined)
Further, dependent Claims 2-12 and 22-25 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 1 & 21 (and their respective dependent Claims 2-12 & 22-25 ) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claims 2-12 & 22-25), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: inertial sensor unit; a first/second sensor unit; electronic device; a first/second earphone are generically recited computer elements in independent Claims 1 & 21 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 1 & 21 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., inertial sensor unit; first/second sensor unit as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1 & 21 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1 & 21 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-12 & 21-25 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: an inertial sensor unit; a first/second sensor unit; an electronic device; a first/second earphone
The above-identified additional elements 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.
Per Applicant’s specification, inertial sensor unit (Pg. 6, lines 1-11) a first/second sensor unit (Pg. 6, lines 1-11; Pg. 18, lines 24-27); electronic device (Pg. 1, line 25-Pg. 2, line 6); a first/second earphone (Pg. 1, line 25-Pg. 2, line 6)
Accordingly, in light of Applicant’s specification, the claimed terms inertial sensor unit & first/second sensor unit is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the inertial sensor unit & first/second sensor unit. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-12 & 21-25 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the methods of Claims 1-12 & 21-25 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-12 & 21-25 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1 & 21 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-12 & 21-25 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-12 & 21-25 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-12 & 21-25 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 12016662 B2 to Kirszenblat et al. (hereinafter, Kirszenblat).
Regarding Claim 1, Kirszenblat discloses a method (Kirszenblat: Abstract), comprising:
generating, with an inertial sensor unit (Kirszenblat: Col. 1, lines 46-50 ‘an accelerometer’ and ‘a processor’ combined.) of an electronic device worn by a user, sensor data based on bone conduction of sound (Kirszenblat: Col. 1, lines 47-57);
generating, with the inertial sensor unit, frequency domain data based on the sensor data (Kirszenblat: Col. 2, lines 31-46); and
detecting, with the inertial sensor unit, breathing of the user by performing a classification process based on the frequency domain data (Kirszenblat: Col. 1, lines 56-57 “based on the frequency of the oscillation, computes a rate of respiration.”, Col. 5, lines 18-25).
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.
Claim(s) 2-3, 10 & 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kirszenblat in view of US 11737708 B2 to Linnes et al. (hereinafter, Linnes).
Regarding Claim 2, Kirszenblat discloses the method of claim 1, Kirszenblat is silent regarding specific features of frequency domain data.
However, Linnes teaches calculating, from the frequency domain data, a spectral energy, a spectral centroid frequency, and a spectral spread based on the sensor data (Linnes: Col. 12, lines 23-45; Col. 12, line 60-Col. 13, line 16; Note: Spectral energy is inherent in the DFT magnitude spectrum ‘X(k) used for the centroid and peak detection.); and
detecting, with the inertial sensor unit, breathing of the user by performing a classification process based on the spectral energy, the spectral centroid frequency, and the spectral spread (Linnes: Col. 12, lines 46-58; Note: Spectral centroid derived from DFT magnitudes that embody spectral energy is used to compute respiratory rate; peak detection on the spectrum leads to classification.).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat to include calculation of spectral energy, spectral centroid frequency, and spectral spread from frequency domain data as taught by Linnes to find small variances in frequency analysis that could be due to small contributions by frequencies, and the centroid value represents the most accurate frequency indicative of respiration (Linnes: Col. 12, line 60-Col. 13, line 16).
Regarding Claim 3, Kirszenblat in view of Linnes disclose the method of claim 2, Kirszenblat further discloses comprising performing axis fusion on the sensor data prior to generating the frequency domain data (Kirszenblat: Col. 1, lines 62-66; Col. 4, lines 61-64).
Regarding Claim 10, Kirszenblat in view of Linnes discloses the method of claim 2, comprising outputting, from the inertial sensor unit, breathing detection data based on detecting the breathing (Kirszenblat: Col. 4, lines 37-41).
Regarding Claim 21, Kirszenblat discloses a method (Kirszenblat: Abstract), comprising:
generating, with an inertial sensor (Kirszenblat: Col. 1, lines 46-50 ‘an accelerometer’) of a first sensor unit (Kirszenblat: Col. 1, lines 46-50 ‘an accelerometer’ and ‘a processor’ combined.), first sensor data based on bone conduction of sound (Kirszenblat: Col. 1, lines 47-57);
generating, with a control circuit of the first sensor unit, generate frequency domain data based on the first sensor data (Kirszenblat: Col. 2, lines 31-46);
Kirszenblat is silent regarding specific features of frequency domain data.
However, Linnes teaches generating, with the control circuit, breathing detection data indicative of breathing of the user based on a spectral energy, a spectral centroid frequency, and a spectral spread of the frequency domain data. (Linnes: Col. 12, lines 23-45; Col. 12, line 60-Col. 13, line 16; Note: Spectral energy is inherent in the DFT magnitude spectrum ‘X(k) used for the centroid and peak detection.).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat to include generation of breathing detection data based on spectral energy, spectral centroid frequency, and spectral spread from frequency domain data as taught by Linnes to find small variances in frequency analysis that could be due to small contributions by frequencies, and the centroid value represents the most accurate frequency indicative of respiration (Linnes: Col. 12, line 60-Col. 13, line 16).
Regarding Claim 22, Kirszenblat in view of Linnes discloses the method of claim 21, Kirszenblat is silent regarding specific features of frequency domain data.
However, Linnes teaches wherein the control circuit is configured to generate, from the frequency domain data, the spectral energy, the spectral centroid frequency, and the spectral spread based on the first sensor data (Linnes: Col. 12, lines 23-45; Col. 12, line 60-Col. 13, line 16; Note: Spectral energy is inherent in the DFT magnitude spectrum ‘X(k) used for the centroid and peak detection.) .
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat to include generation of breathing detection data based on spectral energy, spectral centroid frequency, and spectral spread from frequency domain data as taught by Linnes to find small variances in frequency analysis that could be due to small contributions by frequencies, and the centroid value represents the most accurate frequency indicative of respiration (Linnes: Col. 12, line 60-Col. 13, line 16).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kirszenblat in view of Linnes in further view of US 20100312188 A1 to Robertson et al. (hereinafter, Robertson).
Regarding Claim 4, Kirszenblat in view of Linnes disclose the method of claim 3, Kirszenblat is silent on comprising performing low-pass filtering and decimation after performing axis fusion and prior to generating the frequency domain data.
However, Robertson teaches performing low-pass filtering and decimation after performing low-pass filtering and decimation (Robertson: Para. [0156]) after performing axis fusion and prior to generating the frequency domain data (Robertson: Para. [0143] ‘the three axes are combined at block 630 and filtered at block 635.’).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat in view of Linnes to specify performing low-pass filtering axis fusion and decimation after performing axis fusion and prior to generating the frequency domain data as taught by Robertson to construct total acceleration and make the system robust against different orientations of the receiver with respect to the subject (Robertson: Para. [0143])
Claim(s) 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kirszenblat in view of Linnes in further view of Röddiger et al., 2020. Towards Respiration Rate Monitoring Using an In-Ear Headphone Inertial Measurement Unit. In Proceedings of the 1st International Workshop on Earable Computing (EarComp'19). Association for Computing Machinery, New York, NY, USA, 48–53. https://doi.org/10.1145/3345615.3361130, (hereinafter, Röddiger).
Regarding Claim 5, Kirszenblat in view of Linnes disclose the method of claim 2, While Kirszenblat discloses FFT being applied to derive respiratory oscillation frequency and Linnes teaches applying DFT as the first step of respiration signal-processing, Kirszenblat in view of Linnes is silent on generating, from the sensor data, a plurality of windows; and generating the frequency domain data by performing a sliding discrete Fourier transform on each window.
However, Röddiger teaches generating, from the sensor data, a plurality of windows (Röddiger: Pg. 48 ‘(B): we split the data into 20 second windows and interpolate using cubic splines…’; Pg. 49, Para. 3.2 Data Processing Pipeline); and
generating the frequency domain data by performing a sliding discrete Fourier transform on each window (Röddiger: Pg. 50, Para. 3.2 Data Processing Pipeline, ‘We perform a spectral analysis of each principal component using a Fast Fourier Transformation (FFT) with zero-padding and compute the maximum peak and its magnitude for each component. We then report the frequency corresponding to the peak with the highest magnitude as respiration frequency that we can convert to CPM.’).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat in view of Linnes to include generating a plurality of windows and generating domain data by performing a sliding discrete Fourier transform on each window as taught by Röddiger to remove signal shifts, trends, and data where movement is too high based on a threshold (Röddiger: Para. 3.2 Data Processing Pipeline).
Regarding Claim 6, Kirszenblat in view of Linnes in view of Röddiger discloses the method of claim 5, Kirszenblat is silent on wherein calculating the spectral energy, the spectral centroid frequency, and the spectral spread includes calculating the spectral energy, the spectral centroid frequency, and the spectral spread for each window.
While Kirszenblat in view of Linnes teaches DFT computation, spectral centroid calculation from resulting spectrum, spectral energy as the basis of the magnitude spectrum X(k) used for centroid and peak detection and that the process is applied to segmented/physiological signal portions, Linnes is silent on performing the signal processing steps on windows.
However, Röddiger teaches per-window FFT in signal-processing (Röddiger: Pg. 50, Para. 3.2 Data Processing Pipeline, ‘We perform a spectral analysis of each principal component using a Fast Fourier Transformation (FFT) with zero-padding and compute the maximum peak and its magnitude for each component. We then report the frequency corresponding to the peak with the highest magnitude as respiration frequency that we can convert to CPM.’).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat in view of Linnes to include generating a plurality of windows and generating domain data by performing a sliding discrete Fourier transform on each window as taught by Röddiger to remove signal shifts, trends, and data where movement is too high based on a threshold (Röddiger: Para. 3.2 Data Processing Pipeline).
Regarding Claim 7, Kirszenblat in view of Linnes in view of Röddiger discloses the method of claim 6, Kirszenblat is silent on wherein the classification process includes: making a classification for each window from a group of the windows; and detecting breathing for the group of windows based on the classification of each window of the group.
While Kirszenblat in view of Linnes teaches DFT computation, spectral centroid calculation from resulting spectrum, spectral energy as the basis of the magnitude spectrum X(k) used for centroid and peak detection and that the process is applied to segmented/physiological signal portions, Kirszenblat in view of Linnes is silent on making a classification for each window from a group of the windows; and detecting breathing for the group of windows based on the classification of each window of the group.
However, Röddiger teaches making a classification for each window from a group of the windows and detecting breathing for the group of windows based on the classification of each window of the group (Röddiger: Pg. 49-50, Para. 3.2 Data Processing Pipeline, (7)).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat in view of Linnes to include generating a plurality of windows and generating domain data by performing a sliding discrete Fourier transform on each window as taught by Röddiger to remove signal shifts, trends, and data where movement is too high based on a threshold (Röddiger: Para. 3.2 Data Processing Pipeline).
Claim(s) 8-9, 11 & 23-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kirszenblat in view of Linnes in further view of Monaco et al., Multi-Time-Scale Features for Accurate Respiratory Sound Classification. Appl. Sci. 2020, 10, 8606. https://doi.org/10.3390/app10238606., (Hereinafter, Monaco)
Regarding Claim 8, Kirszenblat in view of Linnes discloses the method of claim 2, Kirszenblat is silent on wherein the classification process includes: generating a value by dividing the spectral energy by the product of the spectral centroid frequency and spectral spread; and comparing the value to a threshold.
Monaco teaches using feature importance thresholds in line with classification algorithms (Monaco: Materials and Methods ‘we present a novel classification framework for respiratory sounds, specifically aimed at detecting the presence of significant sounds during the respiratory cycle (see Figure 1)... The goal is the development of a diagnostic decision support system for the discrimination of healthy controls from patients with respiratory symptoms. The proposed approach consists of three main steps: (i) data standardization, (ii) multi-time-scale feature extraction and (iii) classification. A detailed description of these steps is provided in the following sections. ’, Fig. 1; 4. Discussion ‘Finally, we investigated which features were best at characterizing the presence of significant sounds. We observed that a relatively small amount of features (∼50) was sufficient for an accurate classification. Besides, our findings demonstrated that, in the examined case variations, roughly this number of features results in negligible performance differences (see Figure 6); this is a relevant aspect, considering that using different feature importance thresholds can significantly affect the classification performance. Finally, by grouping these top rank features by type, we observed that the main contribution was given by the chroma vector. The chroma vector is a 12−dimensional representation of the spectral energy [75].’).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat to include using a classification algorithm to generate a value based on features extracted and comparing to a threshold as taught by Monaco because automated classification of respiratory sound has gained increasing attention in recent years and has been the subject of a growing number of international scientific challenges for the development of accurate classification algorithms to support clinical practice (Monaco: Featured Application), threshold comparisons can significantly affect the classification performance (Monaco: 4. Discussion), and further as a matter of routine optimization through experimentation, in this case motivated by the need for improved accuracy of the classification algorithm.
Regarding Claim 9, Kirszenblat in view of Linnes discloses the method of claim 2, Kirszenblat is silent on wherein the classification algorithm includes: comparing the spectral energy to a first threshold value; comparing the spectral centroid frequency to a second threshold value; and comparing the spectral spread to a third threshold value.
Monaco teaches using feature importance thresholds in line with classification algorithms (Monaco: Materials and Methods ‘we present a novel classification framework for respiratory sounds, specifically aimed at detecting the presence of significant sounds during the respiratory cycle (see Figure 1)... The goal is the development of a diagnostic decision support system for the discrimination of healthy controls from patients with respiratory symptoms. The proposed approach consists of three main steps: (i) data standardization, (ii) multi-time-scale feature extraction and (iii) classification. A detailed description of these steps is provided in the following sections. ’, Fig. 1; 4. Discussion ‘Finally, we investigated which features were best at characterizing the presence of significant sounds. We observed that a relatively small amount of features (∼50) was sufficient for an accurate classification. Besides, our findings demonstrated that, in the examined case variations, roughly this number of features results in negligible performance differences (see Figure 6); this is a relevant aspect, considering that using different feature importance thresholds can significantly affect the classification performance. Finally, by grouping these top rank features by type, we observed that the main contribution was given by the chroma vector. The chroma vector is a 12−dimensional representation of the spectral energy [75].’).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat to include using a classification algorithm to compare features extracted to thresholds as taught by Monaco because automated classification of respiratory sound has gained increasing attention in recent years and has been the subject of a growing number of international scientific challenges for the development of accurate classification algorithms to support clinical practice (Monaco: Featured Application) and doing so can significantly affect the classification performance as a matter of routine optimization (Monaco: 4. Discussion).
Regarding Claim 11, Kirszenblat in view of Linnes discloses the method of claim 2, Kirszenblat is silent on wherein the classification algorithm includes: passing the spectral energy, the spectral centroid frequency, and the spectral spread to an analysis model trained with a machine learning process; and detecting breathing based on a classification of the analysis model.
Monaco teaches wherein the classification algorithm includes: passing the spectral energy, the spectral centroid frequency, and the spectral spread to an analysis model trained with a machine learning process (Monaco: 2.2.1 Short-Term Features; 2.3.2. Cross-Validation, Balancing and Performance Metrics); and
detecting breathing based on a classification of the analysis model (Monaco: 2. Materials and Methods).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat to include using a classification algorithm for breathe detection as taught by Monaco because automated classification of respiratory sound has gained increasing attention in recent years and has been the subject of a growing number of international scientific challenges for the development of accurate classification algorithms to support clinical practice (Monaco: Featured Application).
Regarding Claim 23, Kirszenblat in view of Linnes discloses the method of claim 21, Kirszenblat is silent on further generating the breathing detection data with an analysis model of the control circuit trained with a machine learning process to detect breathing based on the spectral energy, the spectral centroid frequency, and the spectral spread.
However, Monaco teaches generating the breathing detection data with an analysis model of the control circuit trained with a machine learning process to detect breathing based on the spectral energy, the spectral centroid frequency, and the spectral spread (Monaco: 2.2.1 Short-Term Features; 2.3.2. Cross-Validation, Balancing and Performance Metrics).
One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the signal processing of Kirszenblat to include using a classification algorithm for breathe detection as taught by Monaco because automated classification of respiratory sound has gained increasing attention in recent years and has been the subject of a growing number of international scientific challenges for the development of accurate classification algorithms to support clinical practice (Monaco: Featured Application).
Regarding Claim 24, Kirszenblat in view of Linnes in view of Monaco discloses the method of claim 23, Kirszenblat further discloses the sensor unit is housed within a first earphone (Kirszenblat: Col. 1, lines 46-50, Col. 1, lines 58-60).
Regarding Claim 25, Kirszenblat in view of Linnes in view of Monaco discloses the method of claim 24, Kirszenblat further discloses comprising providing second sensor data to the first sensor unit with a second earphone including a second sensor unit (Kirszenblat: Col. 1, line 66-Col. 2, line 15),
wherein the control circuit is configured to generate the breathing detection data based on the first sensor data and the second sensor data (Kirszenblat: Col. 1, line 66-Col. 2, line 15).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kirszenblat in view of Linnes in view of Monaco in further view of US 20190357850 A1 to Li et al. (hereinafter, Li).
Regarding Claim 12, Kirszenblat in view of Linnes in view of Monaco discloses the method of claim 11, while Kirszenblat discloses breathing detection data detected on the wearable device (Kirszenblat: Col. 1, lines 46-60), Kirszenblat is silent on outputting the breathing detection data from the wearable electronic device to a remote electronic device . However, Li teaches comprising outputting breathing detection data from the wearable electronic device to a remote electronic device (Li: Para. [0150-0151]). One of ordinary skill in the art at the time the invention was filed would have found it obvious to modify the method of Kirszenblat to include outputting the breathing detection data from the wearable electronic device to a remote electronic device as taught by Li to provide real-time visual feedback of detection results (Li: Para. [0150-0151]) and Li teaches it as a design choice to provide that display on a remote device (Li: Para. [0151]) which would yield predictable results to one of ordinary skill in the art.
Conclusion
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/SHAWN CURTIS BROUGHTON/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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