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 – 9 in the reply filed on 12 JUNE 2026 is acknowledged. Claims 10 – 13 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12 JUNE 2026.
Specification
The abstract of the disclosure is objected to because of the term “Disclosed is…”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
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 – 4 and 7 – 8 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 (line 2) includes the term “indicating a target disease”. It is unclear if this is intended to be the same or different than the previously-recited target disease. For the purposes of examination, the term “indicating a target disease” is deemed to claim “indicating the target disease”. Claim 3 is similarly rejected due to its dependence on Claim 2.
Claim 4 (line 2) recites the term “include at least one of the number of occurrences”. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited number of occurrences. For the purposes of examination, the term “include at least one of the number of occurrences” is deemed to claim “include at least one of a number of occurrences”.
Claim 7 (line 5) recites the term “the plurality of parameters corresponding to the time component”. There is insufficient antecedent basis for this limitation in the claim. It is unclear if these are intended to be the same or different than the previously-recited plurality of parameters corresponding to at least one abnormal breathing sound. For the purposes of examination, the term “the plurality of parameters corresponding to the time component” is deemed to claim “a plurality of time component-associated abnormal breathing sound parameters”. Similarly for the remaining recitations of “the plurality of parameters corresponding to the time component” in the claim, they are interpreted as “the plurality of time component-associated abnormal breathing sound parameters”.
Claim 7 (lines 8 – 10) recites the term “wherein the reference time length is determined based on equation
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, where
t
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refers to the reference time length,
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refers to a sampling rate of the breathing sound data, and
L
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refers to a hop length of the spectrogram data.” Looking to the units associated with
t
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,
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and
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, the right side of the equation is (
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, which is
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, and the left side of the equation is seconds. The associated units do not match on each side of the equation, such that it is unclear the metes and bounds of
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.
Based on unit analysis, it appears that inverting the right side of the equation such that
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would yield appropriate units of seconds = seconds. For the purposes of examination, the term “wherein the reference time length is determined based on equation
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, where
t
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refers to the reference time length,
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refers to a sampling rate of the breathing sound data, and
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refers to a hop length of the spectrogram data” is deemed to claim “wherein the reference time length is determined based on equation
t
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=
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s
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, where
t
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refers to the reference time length,
s
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t
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refers to a sampling rate of the breathing sound data, and
L
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refers to a hop length of the spectrogram data”.
Claim 7 (lines 11 - 14) recites the term “wherein the plurality of parameters corresponding to the time component are determined based on equation
t
x
=
b
x
x
t
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, where
t
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indicates the reference time length, bx indicates one of the abscissa coordinate values, and tx indicates one of the plurality of parameters corresponding to the time component.” Based on the units mis-match in the previously-recited
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=
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, where the result is either in seconds or
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, there is an additional units mismatch in
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, of seconds = constant x ?. The metes and bounds of the intended equation are unclear, as
t
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is one of the plurality of parameters corresponding to the time component, and it is not known which units that parameter is intended to have. Looking to Applicant’s specification at [0036] “…the plurality of parameters may include at least one of the number of occurrences, intensity of occurrence, start time, end time, duration, lowest frequency, maximum frequency, and average frequency of at least one abnormal breathing sound.” These parameters each have different units. Due to the stacking equation issues, the metes and bounds of the claim cannot be ascertained.
Claim 8 (lines 10 - 11) recites the term “refers to the number of reference samples”. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited number of reference samples. For the purposes of examination, the term “refers to the number of reference samples” is deemed to claim “refers to a number of reference samples”.
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 – 9 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.
Regarding Claim 1, the claim recites "an act or step, or series of acts or steps" and is therefore a process, which is a statutory category of invention (Step 1). The claims are then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Each of Claims 1 – 9 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1 – 9 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG. The claimed invention involves making observations, evaluations, judgments, and opinions, which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 1 – 9 recites an abstract idea.
Specifically, Independent Claim 1 recites (underlined are observations, judgements, evaluations, or opinions, which are grouped as a mental process under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim
A method of operating a diagnosis device for diagnosing a disease of a user, the method comprising:
obtaining breathing sound data by measuring a breath of the user;
generating feature parameter information including a plurality of parameters corresponding to at least one abnormal breathing sound based on an analysis of the breathing sound data;
generating diagnosis information indicating a target disease with a highest model output value among a plurality of diseases by applying the feature parameter information to a pre-trained differential diagnosis model;
generating diagnosis basis information that quantifies an importance of the plurality of parameters used to determine the target disease; and
outputting the diagnosis information and the diagnosis basis information through a user interface device of the diagnosis device.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps. For example, in Independent Claim 1, these limitations include:
Observation and judgment to diagnose a disease of a user
Observation and judgment to obtain breathing sound data by measuring a breath of the user;
Observation and judgment to generate feature parameter information including a plurality of parameters corresponding to at least one abnormal breathing sound based on an analysis of the breathing sound data;
Observation and judgment to generate diagnosis information indicating a target disease with a highest model output value among a plurality of diseases by applying the feature parameter information to a pre-trained differential diagnosis model;
Observation and judgment to generate diagnosis basis information that quantifies an importance of the plurality of parameters used to determine the target disease;
Similarly, the Dependent Claims include the following abstract limitations, in addition to the aforementioned limitations in Independent Claim 1 (underlined observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG):
observing a change in an output of the pre-trained differential diagnosis model depending on a change in at least one parameter among the plurality of parameters of the feature parameter information;
Observation and judgment of a change in an output of the pre-trained differential diagnosis model depending on a change in at least one parameter among the plurality of parameters of the feature parameter information;
calculating the importance of the plurality of parameters depending on the change in the output of the pre-trained differential diagnosis model;
Observation and judgment to evaluate the importance of the plurality of parameters depending on the change in the output of the pre-trained differential diagnosis model;
generating the diagnosis information indicating the target disease based on the importance of the plurality of parameters.
Observation and judgment to generate the diagnosis information indicating the target disease based on the importance of the plurality of parameters.
generating spectrogram data that visually represents a time component and a frequency component of the at least one abnormal breathing sound based on the breathing sound data;
Observation and judgment to generate spectrogram data that visually represents a time component and a frequency component of the at least one abnormal breathing sound based on the breathing sound data;
generating heatmap data including pixels each having a temperature value depending on a probability of corresponding to the at least one abnormal breathing sound based on a convolution neural network operation of the spectrogram data;
Observation and judgment to generate heatmap data including pixels each having a temperature value depending on a probability of corresponding to the at least one abnormal breathing sound based on a convolution neural network operation of the spectrogram data;
generating the feature parameter information based on the spectrogram data and the heatmap data.
Observation and judgment to generate the feature parameter information based on the spectrogram data and the heatmap data.
detecting a blob region by filtering the heatmap data based on a threshold temperature value;
Observation and judgment to detect a blob region by filtering the heatmap data based on a threshold temperature value;
extracting coordinates of the blob region from the heatmap data;
Observation and judgment to extract coordinates of the blob region from the heatmap data;
generating the feature parameter information corresponding to the time component and the frequency component of the at least one abnormal breathing sound based on the spectrogram data and the extracted coordinates.
Observation and judgment to generate the feature parameter information corresponding to the time component and the frequency component of the at least one abnormal breathing sound based on the spectrogram data and the extracted coordinates.
calculating the plurality of parameters corresponding to the time component based on abscissa coordinate values of the extracted coordinates and a reference time length,
Observation and judgment to evaluate the plurality of parameters corresponding to the time component based on abscissa coordinate values of the extracted coordinates and a reference time length,
wherein the reference time length is determined based on equation
t
p
=
s
r
a
t
e
L
h
o
p
, where
t
p
refers to the reference time length,
s
r
a
t
e
refers to a sampling rate of the breathing sound data, and
L
refers to a hop length of the spectrogram data,
wherein the reference time length is Observed and judged based on equation
t
p
=
s
r
a
t
e
L
h
o
p
, where
t
p
refers to the reference time length,
s
r
a
t
e
refers to a sampling rate of the breathing sound data, and
L
refers to a hop length of the spectrogram data,
wherein the plurality of parameters corresponding to the time component are determined based on equation
t
x
=
b
x
x
t
p
, where
t
p
indicates the reference time length, bx indicates one of the abscissa coordinate values, and tx indicates one of the plurality of parameters corresponding to the time component.
wherein the plurality of parameters corresponding to the time component are Observed and judged based on equation
t
x
=
b
x
x
t
p
, where
t
p
indicates the reference time length, bx indicates one of the abscissa coordinate values, and tx indicates one of the plurality of parameters corresponding to the time component.
calculating a plurality of parameters corresponding to the frequency component based on ordinate coordinate values of the extracted coordinates and a reference frequency magnitude
Observation and judgment to evaluate a plurality of parameters corresponding to the frequency component based on ordinate coordinate values of the extracted coordinates and a reference frequency magnitude
wherein the reference frequency magnitude is determined based on equation
f
p
=
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t
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N
f
, where fp refers to the reference frequency magnitude,
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t
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refers to a sampling rate of the breathing sound data, and Nf refers to the number of reference samples
wherein the reference frequency magnitude is component are Observed and judged based on equation
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=
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f
, where fp refers to the reference frequency magnitude,
s
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t
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refers to a sampling rate of the breathing sound data, and Nf refers to the number of reference samples
wherein the plurality of parameters corresponding to the frequency component are determined based on equation
f
y
=
b
y
x
f
p
, where fp indicates the reference frequency magnitude, by indicates one of the ordinate coordinate values, and fy indicates one of the plurality of parameters corresponding to the frequency component.
wherein the plurality of parameters corresponding to the frequency component are component are Observed and judged based on equation
f
y
=
b
y
x
f
p
, where fp indicates the reference frequency magnitude, by indicates one of the ordinate coordinate values, and fy indicates one of the plurality of parameters corresponding to the frequency component.
generating the heatmap data from the spectrogram data based on a top-down method.
Observation and judgment to generate the heatmap data from the spectrogram data based on a top-down method.
all of which are grouped as mental processes or mathematical algorithms under the 2019 PEG.
Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in each of Independent Claim 1 (and their respective Dependent Claims) are not integrated into a practical application under 2019 PEG because the additional elements (identified in Claims 1 – 9), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional elements of:
“user interface device”
“diagnosis device”
Additional elements recited include “user interface device” and “diagnosis device” in Independent Claim 1 (and its dependent claims). These components are recited at a high level of generality, i.e., as a user interface performing a generic function of outputting data (the outputting). These generic hardware component limitations “user interface device” and “diagnosis device” are no more than mere instructions to apply the exception using generic computer and hardware components. As such, these additional elements do not impose any meaningful limits on practicing the abstract idea.
Further additional elements from Claims 1 – 9 includes pre-solution activity limitations, such as:
wherein the importance of the plurality of parameters indicates a priority of each of the plurality of parameters with respect to the target disease.
wherein the plurality of parameters of the feature parameter information include at least one of the number of occurrences, occurrence intensity, start time, end time, duration, lowest frequency, maximum frequency, and average frequency of the at least one abnormal breathing sound.
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps.
The “user interface device” and “diagnosis device” as recited in Independent Claim 1 (and its dependent claims) are generically recited computer and hardware elements 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 ideas identified above in Independent Claim 1 (and its 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 processor 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 Claim 1 (and its dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, Independent Claim 1 (and its dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 1 – 9 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: “user interface device” and “diagnosis device” as recited in Independent Claim 1 (and their dependent claims).
The additional elements of the “user interface device” and “diagnosis device” in Independent Claim 1 (and its dependent claims), as discussed with respect to Step 2A Prong Two, amounts to no more than mere instructions to apply the exception using generic computer and hardware components. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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, the “user interface device” is described generically at [0030] as “the user interface device 150 may be configured to be included in electronic devices such as smart phones and tablet PCs…” The “user interface device” is shown as generic box element “user interface device 150” in Fig. 1.
Per Applicant’s specification, the “diagnosis device” is described generically at [0029] as “ the diagnosis device 100 may be implemented as one of various electronic devices that analyze breathing sounds, such as a smart phone, tablet personal computer (PC), desktop, PC, and laptop.” The “diagnosis device” is shown as generic box element “diagnosis device 100” in Fig. 1.
Accordingly, in light of Applicant’s specification, the claimed terms “user interface device” and “diagnosis device” are reasonably construed as a generic computing and hardware devices. 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 “user interface device” and “diagnosis device.” 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 Independent Claim 1 (and its dependent claims) 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 – 9 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 – 9 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 for Step 2A Prong 2 in Independent Claim 1 (and its 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 – 9 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 – 9 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1 – 9 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.
Claims 1 – 4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Savic. et. al. (US 2009/0156950 A1).
Regarding Claim 1, Savic discloses A method of operating a diagnosis device for diagnosing a disease of a user ([Abstract]; Fig. 1), the method comprising:
obtaining breathing sound data by measuring a breath of the user (Fig 1; [0019] “a stethoscope for acquiring the lung sounds from the subject”);
generating feature parameter information including a plurality of parameters corresponding to at least one abnormal breathing sound based on an analysis of the breathing sound data (Fig 6, “wheezes”, ”coarse crackles”, diminished breath sounds”; Figs. 6 and 7)
generating diagnosis information indicating a target disease with a highest model output value among a plurality of diseases ([0113] “bronchitis” and ”fibrosis”) by applying the feature parameter information to a pre-trained differential diagnosis model ([0109] “…preliminary step is training of the Neural Network, so that the trained network can be used for the classification of signals…”; [0109] “…feature…disease…”; [0112]; [0122] – [0127] “6. …output of the Neural Net will indicate…the disease if the lungs are diseased…”; Table 7: “Fibrosis”)
generating diagnosis basis information that quantifies an importance of the plurality of parameters used to determine the target disease ([0052] “Some features are more suitable to identify particular diseases than other features.”, “Good features” or “dominant features” for a particular disease are features that require the least amount of computation to accurately identify a particular disease…Features must be selected…using some kind of “Discriminant Analysis”…); and
outputting the diagnosis information and the diagnosis basis information through a user interface device of the diagnosis device ([0127] “…The output of the Neural Net will indicate if the lungs are healthy, and identify the disease if the lungs are diseased...”; Fig. 1, “computer 16”; [0063] “…classifier is a device or program running in the computer that makes the decision based on the extracted features.”)
Regarding Claim 2, Savic discloses as described above, The method of claim1. For the remainder of Claim 2, Savic discloses wherein the generating of the diagnosis information indicating a target disease with the highest model output value among the plurality of diseases by applying the feature parameter information to the pre-trained differential diagnosis model includes (See citation above in Claim 1);
observing a change in an output of the pre-trained differential diagnosis model depending on a change in at least one parameter among the plurality of parameters of the feature parameter information ([0087] and [0088] “using PARCOR 3 and PARCOR 4 as in FIG. 7, the clusters for Wheezes and Fibrosis are well separated.”; Fig. 6 and 7; Table 7 vs. Table 8, “Fibrosis” and “Non Fibrosis”);
calculating the importance of the plurality of parameters depending on the change in the output of the pre-trained differential diagnosis model ([0052] “Some features are more suitable to identify particular diseases than other features…Features must be selected…using some kind of “Discriminant Analysis”…; Fig. 6 and 7); and
generating the diagnosis information indicating the target disease based on the importance of the plurality of parameters ([0052] “Some features are more suitable to identify particular diseases than other features.”, “Good features” or “dominant features” for a particular disease are features that require the least amount of computation to accurately identify a particular disease…Features must be selected…using some kind of “Discriminant Analysis”…);
Regarding Claim 3, Savic discloses as described above, The method of claim 2. For the remainder of Claim 3, Savic discloses wherein the importance of the plurality of parameters indicates a priority of each of the plurality of parameters with respect to the target disease (Fig. 2 – 5; [0086] “…location of data points for particular diseases…”; Fig. 6, “…use of LPC and PARCOR coefficients…; Fig. 7; [0093] “…Graphical Classifier (GC)…select the best features to identify a particular lung disease…”).
Regarding Claim 4, Savic discloses as described above, The method of claim 1. For the remainder of Claim 4, Savic discloses wherein the plurality of parameters of the feature parameter information include at least one of the number of occurrences ([0071] “…total of the feature vectors form the vector space…”), occurrence intensity ([0071] “…pattern vectors are obtained after measurements. In the present case, the measurement of the amplitude of the breathing sounds versus time. “), start time, end time, duration, lowest frequency, maximum frequency, and average frequency of the at least one abnormal breathing sound ([0008] “…sounds like crackles, wheezes…”; Fig 6 and 7; [0087] “…Wheezes and Fibrosis…”).
Claim 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Saldanha et. al., (“Data augmentation using Variational Autoencoders for improvement of respiratory disease classification”, Ref U on PTO-892).
Regarding Claim 1, Saldanha discloses A method of operating a diagnosis device for diagnosing a disease of a user ([Abstract] “…computerized auscultation of lung sounds…”; [Page 3, Top] “…Graphical User Interface (GUI) based applications such as LungSounds@UA....”; [Page 11, Top, “6.1. Audio acquisition” section]);, the method comprising:
obtaining breathing sound data by measuring a breath of the user ([Page 10, Bottom, - Page 11, Top, “6.1. Audio acquisition” section] “…dataset consists of audio samples which were obtained by two research teams over a different span and geographical location…recordings”);
generating feature parameter information including a plurality of parameters corresponding to at least one abnormal breathing sound based on an analysis of the breathing sound data ([Page 11, Top, “6.1. Audio acquisition” section] “Sound annotation for the audio samples was done by….visual-auditory wheezes/crackles identification and with the help of Respiratory Sound Annotation Software.”; [Page 25, 1st Full Paragraph] “…the features of the wheezes and crackles..”)
generating diagnosis information indicating a target disease ([Page 19, Paragraph 3] “The fully connected layers were used for predicting the class labels…”; Fig 33: COPD) with a highest model output value among a plurality of diseases (Fig 33: COPD with 97%+ model output) by applying the feature parameter information to a pre-trained ([Page 6, Paragraph 1] “Fig. 6…70% of the audio segments in each class were used to train the VAEs and classification models.”; Fig. 6, “Class wise split of audio segments into train and test sets…”) differential diagnosis model ([Page 30, 2nd Full Paragraph] “….trained each classifier on the four training sets (imbalanced + 3 augmented)…“the classification models can classify …COPD....”; Fig. 33, “ANN classifier with imbalanced and augmented training sets”, COPD 87.2% on Fig. 33(a); Fig. 6, Class wise split of audio segments into train and test sets…” Fig 1; Fig. 7; [Page 11, Top] “…result of the annotation stage was the generation of a text file for each audio recording which was then used in the pre-processing step.”;
generating diagnosis basis information that quantifies an importance of the plurality of parameters used to determine the target disease ([Page 5, 1st Full Paragraph] “Fig 2 shows the distribution of crackles and wheezes in respiratory cycles of various diseases.”; Fig 2)(Examiner notes that the importance can broadly be a distribution or weighting of how much the feature is characteristically present with the disease of interest, such as the distribution of crackles and wheezes of COPD in Fig 2.); and
outputting the diagnosis information and the diagnosis basis information through a user interface device of the diagnosis device ([Page 3, Top] “…development of Graphical User Interface (GUI) based applications such as LungSounds@UA has made it easier for users to interact with multimedia databases that store respiratory sounds and associated clinical parameters.”; ([Page 5, Paragraph 1] “Fig 2 shows the distribution of crackles and wheezes in respiratory cycles of various diseases.”; Fig 2; Fig. 1 using software)(Examiner notes that the software used for the classifiers would output the results and the elements of this report on a computer. The diagnosis device with a user interface is described by Applicant’s specification at [0029] with “ the diagnosis device 100 may be implemented as one of various electronic devices that analyze breathing sounds, such as a smart phone, tablet personal computer (PC), desktop, PC, and laptop.”)
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 5 – 6 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Saldanha et. al., (“Data augmentation using Variational Autoencoders for improvement of respiratory disease classification”, Ref U on PTO-892) in view of Seo et. al., (KR 102883210 B1).
Regarding Claim 5, Saldanha discloses as described above, The method of claim 1. For the remainder of Claim 5, Saldanha discloses wherein the generating of the feature parameter information including the plurality of parameters corresponding to the at least one abnormal breathing sound based on the analysis of the breathing sound data (See citation above in Claim 1) includes:
generating spectrogram data ([Page 12, 2nd Full Paragraph] “…we use Mel Spectrogram as a feature extraction step before the data augmentation process takes place…”) that visually represents a time component and a frequency component of the at least one abnormal breathing sound based on the breathing sound data (Fig 11);
generating heatmap data including pixels each having a temperature value depending on a probability of corresponding to the at least one abnormal breathing sound based on a convolution neural network operation of the spectrogram data (Fig 34, “Confusion matrices for CNN classifier with imbalanced and augmented training sets”; [Page 5, 1st Full Paragraph] “Fig 2 shows the distribution of crackles and wheezes in respiratory cycles of various diseases.”; Fig 2)(Examiner notes that classifying the disease with a certain probability, and the knowledge that particular diseases correspond to certain distribution of crackles and wheezes would mean that the probability of properly classifying the disease is also broadly a corresponding probability of a particular distribution of crackles and wheezes as in Fig. 2.);
Saldanha does not disclose based on a convolution neural network operation of the spectrogram data and generating the feature parameter information based on the spectrogram data and the heatmap data.
Seo teaches a system and method for analyzing sound data by inputting a spectrogram into a neural network to obtain a heatmap to highlight the pixels of the sounds that contribute to classification results of the sound ([Lines 47 – 53]; [Lines 233 – 237]; [Lines 472 - 473]; Fig. 2 – 4). Specifically for Claim 5, Seo teaches generating heatmap data including pixels each having a temperature value depending on a probability of corresponding to the at least one sound event ([Lines 233 – 237] “generating a heat map image…the contribution of each pixel of the image input to the neural network model as a gradient for the prediction result class….”) based on a convolution neural network operation of the spectrogram data ([Lines 47 – 53] “heatmap image corresponding to the audio spectrogram…obtained by inputting the audio spectrogram to a neural network model to obtain an analysis result, and determining the color of each pixel according to the contribution of each pixel to the analysis result….”; [Lines 224 – 225] “…neural network model may be implemented by…Convolutional Neural Network (CNN)”); and
generating the feature parameter information based on the spectrogram data and the heatmap data ([Lines 472 - 473] “the heat map auditory apparatus may generate the heat map mask 214 emphasizing pixels contributing to the analysis result for a specific class output from the neural network model 212…”)
Saldanha and Seo both disclose and teach identifying particular sounds of interest in sound data using spectrogram: Saldanha with using Mel spectrogram for feature extraction [Page 12, 2nd Full Paragraph] of features of interest from breathing sound data including crackles and wheezes that are beneficial to predict disease, and Seo with identifying “acoustic events” in audio data from a heatmap of spectrogram data input into a CNN ([Seo: [Lines 47 – 53]; [Lines 233 – 237]; [Lines 472 - 473]; Fig. 2 – 4). Seo provides a motivation to combine at [Lines 19 – 25 and 42 – 44] including “it is difficult to trust and use the neural network model in fields that require high reliability…” and “the present invention provides a method and apparatus for determining the reliability of a neural network model because it is possible to know whether the analysis result of the neural network model is accurate by extracting and listening to audio data contributed to the analysis result of the neural network model.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that a heatmap of spectrogram data input into a CNN for audio data that finds acoustic events would be useful for determining the sounds of breathing abnormalities (“acoustic events”), in breathing sound data that are used to make classifications.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the identifying particular sounds of interest in breathing sound data using spectrogram including coughs and wheezes to classify disease with a neural network disclosed in Saldanha with the heatmap from spectrogram input of audio data to a CNN taught by Seo, creating a single breathing sound classification method that can visually identify portions of the audio data that are important for disease classification results using a heatmap from spectrogram input to a CNN, increasing the accuracy confidence in the classification results.
Regarding Claim 6, Saldanha in view of Seo discloses as described above, The method of claim 5, wherein the generating of the feature parameter information including the plurality of parameters corresponding to the at least one abnormal breathing sound based on the analysis of the breathing sound data (See citation and discussion above in Claims 1 and 5) includes: For the remainder of Claim 6, Saldanha discloses generating the feature parameter information corresponding to the time component and the frequency component of the at least one abnormal breathing sound ([Page 12, 2nd Full Paragraph] “use Mel Spectrogram as a feature extraction step before the data augmentation process takes place…”; Fig. 11; [Page 11, Top, “6.1. Audio acquisition” section] “Sound annotation for the audio samples was done by….visual-auditory wheezes/crackles identification and with the help of Respiratory Sound Annotation Software.”; [Page 25, 1st Full Paragraph] “…the features of the wheezes and crackles..”)
Saldanha does not disclose detecting a blob region by filtering the heatmap data based on a threshold temperature value; extracting coordinates of the blob region from the heatmap data; and generating the feature parameter information corresponding to the time component and the frequency component of the at least one abnormal breathing sound based on the spectrogram data and the extracted coordinates.
Seo teaches wherein the generating of the feature parameter information based on the spectrogram data and the heatmap data (See citation above) includes:
detecting a blob region by filtering the heatmap data based on a threshold temperature value (Fig. 2, ‘Heatmap mask 214”, “region 215”; [Lines 50 - 56] “…masking the pixel region with the low contribution from the time-frequency data, masked time-frequency data is generated… binarizing the pixel with the high contribution level and the pixel with a low contribution based on a threshold value “);
extracting coordinates of the blob region from the heatmap data ([Lines 515 – 516] “…pixel corresponding to the pixel, the region 223 corresponding to the region having a higher contribution than the threshold value may be extracted from the time-frequency data 221 …”; Fig. 2 and 4) and
generating the feature parameter information corresponding to the time component and the frequency component of the at least one sound event based on the spectrogram data and the extracted coordinates ([Lines 608 - 611] “…generate masked time-frequency data including only a region having a contribution higher than the threshold…”; [Lines 613 - 615] “…obtains audio data capable of audible sound contributed to the analysis result of the neural network model by performing an inverse Fourier transform on the masked time-frequency data…”).
Seo provides a motivation to combine at [Lines 609 – 611] with “generate masked time-frequency data including only a region having a contribution higher than the threshold by masking the time-frequency data of a region having a contribution lower than the threshold value.“ A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that identifying a blob region, or a grouped region of interest in a heatmap, such as Seo’s identification of the “region 215” in the time-frequency data would be useful for determining which particular portions of the audio file are beneficial to influence classification of the sound, such as classifying breathing sound relative to a disease in Saldanha.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the identifying particular sounds of interest in breathing sound data using spectrogram and a heatmap (including coughs and wheezes) to classify disease with a neural network disclosed in Saldanha in view of Seo with the sorting of the regions of interest in a heatmap (“blob regions”) of the spectrogram taught by Seo, creating a single breathing sound classification method that can visually identify portions of the audio data that are important for disease classification results using region identification from a heatmap of spectrogram input to a CNN.
Regarding Claim 9, Saldanha in view of Seo discloses as described above, The method of claim 5. wherein the generating of the heatmap data including pixels each having the temperature value depending on the probability of corresponding to the at least one abnormal breathing sound based on a convolution neural network operation of the spectrogram data (See citation and description above in Claim 5) includes:
Saldanha does not disclose generating the heatmap data from the spectrogram data based on a top-down method.
Seo teaches generating the heatmap data from the spectrogram data based on a top-down method ([Lines 62 - 65] “generating a heat map image… a gradient obtained by backpropagation ( gradient) or a method of weighted summing feature maps of a specific layer of the neural network model…”; [Lines 237 - 238] “…backpropagation method can obtain the contribution of each pixel of the image input to the neural network model as a gradient for the prediction result class.”)
Seo provides a motivation to combine at [Lines 237 – 238] with “the backpropagation method can obtain the contribution of each pixel of the image input to the neural network model as a gradient for the prediction result class.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that using a top-down backpropagation method in the CNN would be useful for identifying the contribution of each pixel of the image to the predicted classification, such as identifying parts of the breathing sound contribution relative to a disease classification in Saldanha.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the identifying particular sounds of interest in breathing sound data using spectrogram and a heatmap (including coughs and wheezes) to classify disease with a neural network disclosed in Saldanha in view of Seo with the specifically backpropagating method of the CNN to create the heatmap of the spectrogram taught by Seo, creating a single breathing sound classification method that can visually identify portions of the audio data that are important for disease classification results using region identification from a heatmap of spectrogram input to a CNN.
Conclusion
In light of the current 112(b) rejections, no prior art rejection is currently able to be applied to Claim 7.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Relative to Claims 7 and 8, Müller teaches ([Page 5, “2 STFT “ Section and EQs (2 - 3)), an audio processing method of music data using STFT (“short-time Fourier Transform”) with is “often visualized by means of a spectrogram” with the “horizontal axis represents time, the vertical axis is frequency”. This includes a Fourier coefficient with
PNG
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46
157
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Greyscale
that is “associated with the physical time position” and the Fourier coefficient with
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media_image2.png
33
125
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Greyscale
with is “the physical frequency”, where Fs is a frequency, N is window length, H is hop size, and m and k are constants.
For Claim 8, Müller does not particularly teach that wherein the plurality of parameters corresponding to the frequency component are determined based on equation
f
y
=
b
y
x
f
p
, where fp indicates the reference frequency magnitude, by indicates one of the ordinate coordinate values, and fy indicates one of the plurality of parameters corresponding to the frequency component.
Wichern et. al., (US 2023/0086355 A1) teaches a method and system for detecting anomalous sound, including receiving a spectrogram of an audio signal with coordinates in identified target regions, with identification including particular frame length and hop length samples in the spectrogram.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELISSA J MONTGOMERY whose telephone number is (571)272-2305. The examiner can normally be reached Monday - Friday 7:30 - 5:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Valvis can be reached at (571) 272 - 4233. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/JUSTIN XU/Primary Examiner, Art Unit 3791
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