DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
2. 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.
3. 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:
“a data acquisition unit configured to” in claims 1 and 2;
“a data analysis unit configured to” in claims 1 and 2;
“a determination unit configured to” in claims 1 and 2;
“a display unit configured to” in claim 12;
“a heart sound reproduction unit configured to” in claim 13;
“a storage unit configured to” in claim 14;
“a data acquisition step in which a data acquisition unit” in claim 16;
“a data analysis step in which” in claim 16;
“a determination step in which a determination unit” in claim 16;
“a data acquisition step in which a data acquisition unit” in claim 17;
“a data analysis step in which” in claim 17; and
“a determination step in which a determination unit” in claim 17.
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 Objections
4. Claim 15 is objected to because of the following informalities:
in line 3, “a determination results are” is a typographical/grammatical error, and should be amended to “a determination results are”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, i.e. abstract idea, without significantly more.
Step 1 of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.03):
Claims 1-14 are directed to a “device”, which describes one of the four statutory categories of patentable subject matter, i.e. a machine.
Claim 15 is directed to a “system”, which describes one of the four statutory categories of patentable subject matter, i.e. a machine.
Claim 16 is directed to a “method”, which describes one of the four statutory categories of patentable subject matter, i.e. a process.
Claim 17 is directed to a “program”, which does not describe one of the four statutory categories of patentable subject matter. MPEP 2106.03(I) states the following:
Non-limiting examples of claims that are not directed to any of the statutory categories include:
• Products that do not have a physical or tangible form, such as information (often referred to as “data per se”) or a computer program per se (often referred to as “software per se”) when claimed as a product without any structural recitations; …
Since claim 17 recites “An auscultation program …”, the claim is directed to a computer program per se, and constitutes non-statutory subject matter.
Step 2A of the Revised Patent Subject Matter Eligibility Guidance (see MPEP 2106.04):
Claim(s) 1-17, recite the following mental process:
… analyze a waveform including a first sound, a second sound, a third sound, and a fourth sound included in one heartbeat of the heart sound acquired by the data acquisition unit; and
… determine whether there is any abnormality in the heart sound on the basis of an analysis result for the waveform including the first sound, the second sound, the third sound, and the fourth sound analyzed by the data analysis unit.
Based on broadest reasonable interpretation, these limitations are directed to receiving data and performing a mathematical operation, which can be done mentally or using pen and paper.
This judicial exception is not integrated into a practical application because the additional limitations of “a data acquisition unit configured to acquire the heart sound data from the stethoscope” in claim 1, “a data measurement unit configured to measure a heart sound data” in claim 2, and “a data acquisition step in which a data acquisition unit of an auscultation device acquires data about the heart sound” in claims 16 and 17, add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the mental process. Furthermore, “a data analysis unit configured to” and “a determination unit configured to” in claims 1 and 2, “a data analysis unit of the auscultation device” and “a determination unit of the auscultation device” in claims 16 and 17, and “a remote terminal to which the heart sound data and a determination results are transmitted from the auscultation device”, are merely parts of a computer to be used as a tool to perform the mental process.
Step 2B of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.05):
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered separately and in combination.
Analyzing the additional claim limitations individually, the additional limitations that are not directed to the mental process are “a data acquisition unit configured to acquire the heart sound data from the stethoscope” in claim 1, “a data measurement unit configured to measure a heart sound data” in claim 2, and “a data acquisition step in which a data acquisition unit of an auscultation device acquires data about the heart sound” in claims 16 and 17. Such features are conventional and routine in the art (for example, see Ghaffarzadegan et al., U.S. Patent Application Publication No. 2019/03965342 A1, published on 05 December 2019, para. [0019]), and add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the abstract idea.
The limitations “a data analysis unit configured to” and “a determination unit configured to” in claims 1 and 2, “a data analysis unit of the auscultation device” and “a determination unit of the auscultation device” in claims 16 and 17, and “a remote terminal to which the heart sound data and a determination results are transmitted from the auscultation device”, are merely parts of a computer to be used as a tool to perform the mental process, and amounts to computer implementation of the abstract idea.
The additional limitations of dependent claims 3-14 are merely directed to and further narrow the scope of the mental process or further narrow the scope of the additional limitations that do not integrate the mental process into a practical application or are not significantly more than the mental process.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide computer implementation of the abstract idea using collected data without: improvement to the functioning of a computer or to any other technology or technical field; applying the mental process with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; applying or using the mental process in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment; or adding a specific limitation other than what is well-understood, routine, conventional activity in the field.
Claim Rejections - 35 USC § 102
7. 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.
8. 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.
9. Claims 1-3 and 10-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ghaffarzadegan et al., U.S. Patent Application Publication No. 2019/03965342 A1 (“Ghaffarzadegan”).
As to Claim 1, Ghaffarzadegan teaches the following:
An auscultation device (“abnormal heart sound detecting system”) 10 that acquires heart sound data from a stethoscope (“microphone”) 28 that measures a heart sound of a subject, and determines whether there is any abnormality (see “With reference to FIGS. 1 and 2A-2B, an exemplary embodiment of an abnormal heart sound detecting system 10 for detecting abnormal heart sounds of a person 12 is described. The abnormal heart sound detecting system 10 is configured to monitor acoustic characteristics of a heart sound of the person 12 and inform a user in case of abnormalities in heart.” in para. [0017]), the auscultation device 10 comprising:
a data acquisition unit (“stethoscope”) 20 configured to acquire the heart sound data from the stethoscope (“microphone”) 28 (see “FIG. 2A shows an exemplary embodiment of a stethoscope 20, which is configured to record a phonocardiogram of the heart of the person 12 and provide it to the portable electronic device 30. In the illustrated embodiment, the stethoscope 20 comprises a processor 22 operably connected with a memory 24, a transceiver 26, and a microphone 28. The memory 24 is configured to store program instructions that, when executed by the processor 22, enable the stethoscope 20 to perform various operations described elsewhere herein, including recording a phonocardiogram of the heart of the person 12 and communicating with the portable electronic device 30 to provide the phonocardiogram to the portable electronic device 30.” in para. [0019]);
a data analysis unit (“processor 32” of “portable electronic device 30”) 32 configured to analyze a waveform (“phonocardiogram”, not labeled) including a first sound, a second sound, a third sound, and a fourth sound (“heart sounds”) included in one heartbeat of the heart sound acquired by the data acquisition unit 20 (see “The memory 34 is configured to store program instructions that, when executed by the processor 32, enable the portable electronic device 30 to perform various operations described elsewhere herein, including communicating with the stethoscope 20 to receive a phonocardiogram the heart of the person 12, processing the phonocardiogram to identify abnormal heart sounds, and informing the user in the event that abnormal heart sounds are detected.” in para. [0026]); and
a determination unit (“PCG Classification Model” of “portable electronic device 30”) 42 configured to determine whether there is any abnormality in the heart sound on the basis of an analysis result for the waveform including the first sound, the second sound, the third sound, and the fourth sound analyzed by the data analysis unit 32 (see “The processor 32 is configured to utilize the phonocardiogram classification model 42 to extract features from the phonocardiogram of the heart of the person 12 and to classify the phonocardiogram as being normal or abnormal. As used herein, the term “machine learning model” refers to a system or set of program instructions and/or data configured to implement an algorithm or mathematical model that predicts and provides a desired output based on a given input.” in para. [0026]).
As to Claim 2, Ghaffarzadegan teaches the following:
An auscultation device (“abnormal heart sound detecting system”) 10 that measures a heart sound of a subject and determines whether there is any abnormality (see “With reference to FIGS. 1 and 2A-2B, an exemplary embodiment of an abnormal heart sound detecting system 10 for detecting abnormal heart sounds of a person 12 is described. The abnormal heart sound detecting system 10 is configured to monitor acoustic characteristics of a heart sound of the person 12 and inform a user in case of abnormalities in heart.” in para. [0017]), the auscultation device 10 comprising:
a data measurement unit (“stethoscope”) 20 configured to measure a heart sound data (see “FIG. 2A shows an exemplary embodiment of a stethoscope 20, which is configured to record a phonocardiogram of the heart of the person 12 and provide it to the portable electronic device 30. In the illustrated embodiment, the stethoscope 20 comprises a processor 22 operably connected with a memory 24, a transceiver 26, and a microphone 28. The memory 24 is configured to store program instructions that, when executed by the processor 22, enable the stethoscope 20 to perform various operations described elsewhere herein, including recording a phonocardiogram of the heart of the person 12 and communicating with the portable electronic device 30 to provide the phonocardiogram to the portable electronic device 30.” in para. [0019]);
a data analysis unit (“processor 32” of “portable electronic device 30”) 32 configured to analyze a waveform (“phonocardiogram”, not labeled) including a first sound, a second sound, a third sound, and a fourth sound (“heart sounds”) included in one heartbeat of the heart sound measured by the data measurement unit 20 (see “The memory 34 is configured to store program instructions that, when executed by the processor 32, enable the portable electronic device 30 to perform various operations described elsewhere herein, including communicating with the stethoscope 20 to receive a phonocardiogram the heart of the person 12, processing the phonocardiogram to identify abnormal heart sounds, and informing the user in the event that abnormal heart sounds are detected.” in para. [0026]); and
a determination unit (“PCG Classification Model” of “portable electronic device 30”) 42 configured to determine whether there is any abnormality in the heart sound on the basis of an analysis result for the waveform including the first sound, the second sound, the third sound, and the fourth sound analyzed by the data analysis unit 32 (see “The processor 32 is configured to utilize the phonocardiogram classification model 42 to extract features from the phonocardiogram of the heart of the person 12 and to classify the phonocardiogram as being normal or abnormal. As used herein, the term “machine learning model” refers to a system or set of program instructions and/or data configured to implement an algorithm or mathematical model that predicts and provides a desired output based on a given input.” in para. [0026]).
As to Claim 3, Ghaffarzadegan teaches the following:
wherein the determination unit 42 determines whether there is any abnormality in the heart sound by using the analysis result by the data analysis unit 32, a signal strength and duration of at least one of the first sound, the second sound, the third sound, and the fourth sound, an interval between the waveforms, and at least one of the waveform characteristics (see “With continued reference to FIG. 7, the method 200 continues with a step of, for each segment in the plurality of segments, determining a probability that the respective segment contains an abnormal heart sound based on the respective plurality of frequency sub-band segments using at least one neural network (block 240). Particularly, with respect to the embodiments described in detail herein, the processor 32 of the portable electronic device 30 is configured to determine a probability or probabilities that the segmented cardiac cycle(s) 100 contains an abnormal heart sound based on the frequency sub-band segments decomposed from the respective the segmented cardiac cycle 100 using at least one neural network. In the particular embodiments described herein, the processor 32 is configured to use the phonocardiogram classification model 42, which includes the first convolutional layer 120, the first maxpooling layer 130, the second convolutional layer 140, the second maxpooling layer 150, the flattening layer 160, and the multilayer perceptron (MLP) network having the hidden fully connected layer 170 and the output layer 180. However, it will be appreciated that in alternative embodiments, other types of machine learning models may be used to process the frequency sub-band segments to detect an abnormal heart sound in the segmented cardiac cycle(s) 100.” in para. [0058]).
As to Claim 10, Ghaffarzadegan teaches the following:
wherein the first sound is a sound that occurs when a ventricle of heart contracts, and the second sound is the sound that occurs when a ventricle begins to expand (see “The method 200 continues with a step of segmenting the phonocardiogram into a plurality of segments, each segment comprising a time series of acoustic values corresponding to only one cardiac cycle from the phonocardiogram (block 220). Particularly, with respect to the embodiments described in detail herein, the processor 32 of the portable electronic device 30 is configured to segment the phonocardiogram into a plurality of segment cardiac cycles 100. In at least one embodiment, each segmented cardiac cycle 100 comprises a time series of acoustic values corresponding to only one cardiac cycle from the phonocardiogram.” in para. [0053]).
As to Claim 11, Ghaffarzadegan teaches the following:
wherein the third sound is the sound that occurs when a ventricle finishes expanding, and the fourth sound is the sound that occurs when an atrium of the heart contracts (see “The method 200 continues with a step of segmenting the phonocardiogram into a plurality of segments, each segment comprising a time series of acoustic values corresponding to only one cardiac cycle from the phonocardiogram (block 220). Particularly, with respect to the embodiments described in detail herein, the processor 32 of the portable electronic device 30 is configured to segment the phonocardiogram into a plurality of segment cardiac cycles 100. In at least one embodiment, each segmented cardiac cycle 100 comprises a time series of acoustic values corresponding to only one cardiac cycle from the phonocardiogram.” in para. [0053]).
As to Claim 12, Ghaffarzadegan teaches the following:
a display unit (“display screen”) 39 configured to display a determination result from the determination unit 42 (see “In at least one embodiment, the output device is the display screen 39 of the portable electronic device 30 and the perceptible output is a notification displayed on the display screen 39 which indicates that the phonocardiogram likely includes an abnormal heart sound.” in para. [0061]).
As to Claim 13, Ghaffarzadegan teaches the following:
a heart sound reproduction unit (“output device may be a speaker or light”, not labeled) configured to reproduce data about the heart sound on the basis of a determination result from the determination unit 42 (see “However, in other embodiments, the output device may be a speaker or light that is operated to generate a perceptible output indicating that the phonocardiogram likely includes an abnormal heart sound.” in para. [0061]).
As to Claim 14, Ghaffarzadegan teaches the following:
a storage unit (“memory”) 34 configured to store the heart sound data and a determination result from the determination unit (see “The memory 34 may be of any type of device capable of storing information accessible by the processor 32, such as a memory card, ROM, RAM, hard drives, discs, flash memory, or other computer-readable medium. The memory 34 is configured to store program instructions that, when executed by the processor 32, enable the portable electronic device 30 to perform various operations described elsewhere herein, including communicating with the stethoscope 20 to receive a phonocardiogram the heart of the person 12, processing the phonocardiogram to identify abnormal heart sounds, and informing the user in the event that abnormal heart sounds are detected.” in para. [0025]).
As to Claim 15, Ghaffarzadegan teaches the following:
An auscultation system (see “A method and system for detecting abnormal heart sounds in a phonocardiogram of a person are disclosed. At least one segmented cardiac cycle of the phonocardiogram is received at a processor.” in Abstract), comprising:
the auscultation device 10 according to claim 1 (see grounds of rejection for claim 1 above); and
a remote terminal (“other electronic devices”, not labeled) to which the heart sound data and a determination results are transmitted from the auscultation device 10 (see “The transceivers 36 at least includes a transceiver, such as a Bluetooth® transceiver, configured to communicate with the stethoscope 20, but may also include any of various other devices configured for communication with other electronic devices, including the ability to send communication signals and receive communication signals.” in para. [0023]).
As to Claim 16, Ghaffarzadegan teaches the following:
An auscultation method for measuring a heart sound of a subject and determining whether there is any abnormality (see “A method and system for detecting abnormal heart sounds in a phonocardiogram of a person are disclosed. At least one segmented cardiac cycle of the phonocardiogram is received at a processor.” in Abstract), the method comprising:
a data acquisition step in which a data acquisition unit (“stethoscope”) 20 of an auscultation device (“abnormal heart sound detecting system”) 10 acquires data about the heart sound (see “FIG. 2A shows an exemplary embodiment of a stethoscope 20, which is configured to record a phonocardiogram of the heart of the person 12 and provide it to the portable electronic device 30. In the illustrated embodiment, the stethoscope 20 comprises a processor 22 operably connected with a memory 24, a transceiver 26, and a microphone 28. The memory 24 is configured to store program instructions that, when executed by the processor 22, enable the stethoscope 20 to perform various operations described elsewhere herein, including recording a phonocardiogram of the heart of the person 12 and communicating with the portable electronic device 30 to provide the phonocardiogram to the portable electronic device 30.” in para. [0019]);
a data analysis step in which a data analysis unit (“stethoscope”) 20 of the auscultation device 10 analyzes a waveform (“phonocardiogram”, not labeled) including a first sound, a second sound, a third sound, and a fourth sound (“heart sounds”) included in the heart sound acquired in the data acquisition step (see “The memory 34 is configured to store program instructions that, when executed by the processor 32, enable the portable electronic device 30 to perform various operations described elsewhere herein, including communicating with the stethoscope 20 to receive a phonocardiogram the heart of the person 12, processing the phonocardiogram to identify abnormal heart sounds, and informing the user in the event that abnormal heart sounds are detected.” in para. [0026]); and
a determination step in which a determination unit (“PCG Classification Model” of “portable electronic device 30”) 42 of the auscultation device 10 determines whether there is any abnormality in the heart sound on the basis of an analysis result for the waveform including the first sound, the second sound, the third sound, and the fourth sound analyzed in the data analysis step (see “The processor 32 is configured to utilize the phonocardiogram classification model 42 to extract features from the phonocardiogram of the heart of the person 12 and to classify the phonocardiogram as being normal or abnormal. As used herein, the term “machine learning model” refers to a system or set of program instructions and/or data configured to implement an algorithm or mathematical model that predicts and provides a desired output based on a given input.” in para. [0026]).
As to Claim 17, Ghaffarzadegan teaches the following:
An auscultation program that measures a heart sound of a subject and determines whether there is any abnormality, the auscultation program causing a computer to execute an auscultation method (see “The memory 24 may be of any type of device capable of storing information accessible by the processor 22, such as write-capable memories, read-only memories, or other computer-readable mediums. Additionally, it will be recognized by those of ordinary skill in the art that a “processor” includes any hardware system, hardware mechanism or hardware component that processes data, signals or other information.” in para. [0019]; and see “A method and system for detecting abnormal heart sounds in a phonocardiogram of a person are disclosed. At least one segmented cardiac cycle of the phonocardiogram is received at a processor.” in Abstract) comprising:
a data acquisition step in which a data acquisition unit (“stethoscope”) 20 of an auscultation device (“abnormal heart sound detecting system”) 10 acquires data about the heart sound (see “FIG. 2A shows an exemplary embodiment of a stethoscope 20, which is configured to record a phonocardiogram of the heart of the person 12 and provide it to the portable electronic device 30. In the illustrated embodiment, the stethoscope 20 comprises a processor 22 operably connected with a memory 24, a transceiver 26, and a microphone 28. The memory 24 is configured to store program instructions that, when executed by the processor 22, enable the stethoscope 20 to perform various operations described elsewhere herein, including recording a phonocardiogram of the heart of the person 12 and communicating with the portable electronic device 30 to provide the phonocardiogram to the portable electronic device 30.” in para. [0019]);
a data analysis step in which a data analysis unit (“stethoscope”) 20 of the auscultation device 10 analyzes a waveform (“phonocardiogram”, not labeled) including a first sound, a second sound, a third sound, and a fourth sound (“heart sounds”) included in the heart sound acquired in the data acquisition step (see “The memory 34 is configured to store program instructions that, when executed by the processor 32, enable the portable electronic device 30 to perform various operations described elsewhere herein, including communicating with the stethoscope 20 to receive a phonocardiogram the heart of the person 12, processing the phonocardiogram to identify abnormal heart sounds, and informing the user in the event that abnormal heart sounds are detected.” in para. [0026]); and
a determination step in which a determination unit (“PCG Classification Model” of “portable electronic device 30”) 42 of the auscultation device 10 determines whether there is any abnormality in the heart sound on the basis of an analysis result for the waveform including the first sound, the second sound, the third sound, and the fourth sound analyzed in the data analysis step (see “The processor 32 is configured to utilize the phonocardiogram classification model 42 to extract features from the phonocardiogram of the heart of the person 12 and to classify the phonocardiogram as being normal or abnormal. As used herein, the term “machine learning model” refers to a system or set of program instructions and/or data configured to implement an algorithm or mathematical model that predicts and provides a desired output based on a given input.” in para. [0026]).
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. Claims 4-9 are rejected under 35 U.S.C. 103 as being unpatentable over Ghaffarzadegan, as applied to claim 1 above, and further in view of Zheng, W.O. Patent No. 2017/210955 A1 (“Zheng”).
As to Claim 4, Ghaffarzadegan teaches the subject matter of claim 1 above. Ghaffarzadegan does not teach the following:
wherein the determination unit determines whether there is any abnormality in the heart sound by comparing the waveform analyzed by the data analysis unit to a waveform of a past heart sound of the subject.
However, Zheng teaches the following:
a determination unit (“APP”, not labeled) determines whether there is any abnormality in the heart sound by comparing the waveform analyzed by the data analysis unit to a waveform of a past heart sound of the subject (see “Specifically, the APP compares the collected heart sound signal with the stored normal heart sound signal, and is not abnormal in the preset value range; if an abnormality is found, the APP will actively alarm in the sound and light manner.” in para. [0073]).
Thus, it would have been obvious for one of ordinary skill in the art at the time the present application was effectively filed to modify Ghaffarzadegan’s determination unit (“PCG Classification Model” of “portable electronic device 30”) 42 to determines whether there is any abnormality in the heart sound by comparing the waveform analyzed by the data analysis unit to a waveform of a past heart sound of the subject, as taught by Zheng, because it is mere substitution of one known feature, i.e. “utilize the phonocardiogram classification model 42 to extract features from the phonocardiogram of the heart of the person 12 and to classify the phonocardiogram as being normal or abnormal” (see Ghaffarzadegan, para. [0066]), for another, i.e. “Specifically, the APP compares the collected heart sound signal with the stored normal heart sound signal, and is not abnormal in the preset value range; if an abnormality is found, the APP will actively alarm in the sound and light manner.” (see Zheng, para. [0073]), to yield the same predictable result, i.e. determine whether a heart sound is abnormal.
As to Claim 5, Ghaffarzadegan in view of Zheng teaches the subject matter of claim 4 above. Zheng teaches the following:
wherein the determination unit determines whether there is any abnormality in the heart sound by comparing the waveform analyzed by the data analysis unit to the waveform of the heart sound of a typical healthy person (see “Specifically, the APP compares the collected heart sound signal with the stored normal heart sound signal, and is not abnormal in the preset value range; if an abnormality is found, the APP will actively alarm in the sound and light manner.” in para. [0073]).
As to Claim 6, Ghaffarzadegan in view of Zheng teaches the subject matter of claim 4 above. Zheng teaches the following:
wherein the determination unit compares the waveform of the heart sound analyzed by the data analysis unit to a past heart sound waveform, calculates a difference value of a specific parameter, and determines whether there is any abnormality in the heart sound depending on whether the difference value is at or over a specific threshold (see “Specifically, the APP compares the collected heart sound signal with the stored normal heart sound signal, and is not abnormal in the preset value range; if an abnormality is found, the APP will actively alarm in the sound and light manner.” in para. [0073]).
As to Claim 7, Ghaffarzadegan in view of Zheng teaches the subject matter of claim 5 above. Zheng teaches the following:
wherein the determination unit determines that a condition of the subject requires follow up observation in an event that a difference value of a specific parameter calculated by comparing the waveform of the heart sound analyzed by the data analysis unit to a past heart sound waveform is at or over a specific threshold, and the difference value of a specific parameter calculated by comparing to the waveform of the heart sound of a typical healthy person is below a specific threshold (see “Specifically, the APP compares the collected heart sound signal with the stored normal heart sound signal, and is not abnormal in the preset value range; if an abnormality is found, the APP will actively alarm in the sound and light manner.” in para. [0073]).
As to Claim 8, Ghaffarzadegan in view of Zheng teaches the subject matter of claim 5 above. Zheng teaches the following:
wherein the determination unit determines that a condition of the subject requires follow-up observation in an event that a difference value of a specific parameter calculated by comparing the waveform of the heart sound analyzed by the data analysis unit to a past heart sound waveform is below a specific threshold, and the difference value of a specific parameter calculated by comparing to the waveform of the heart sound of a typical healthy person is at or over a specific threshold (see “Specifically, the APP compares the collected heart sound signal with the stored normal heart sound signal, and is not abnormal in the preset value range; if an abnormality is found, the APP will actively alarm in the sound and light manner.” in para. [0073]).
As to Claim 9, Ghaffarzadegan in view of Zheng teaches the subject matter of claim 1 above. Zheng teaches the following:
wherein the determination unit determines whether there is any abnormality in the heart sound by comparing the waveform analyzed by the data analysis unit to the waveform of the heart sound of a typical healthy person (see “Specifically, the APP compares the collected heart sound signal with the stored normal heart sound signal, and is not abnormal in the preset value range; if an abnormality is found, the APP will actively alarm in the sound and light manner.” in para. [0073]).
Conclusion
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 06/05/2026
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