DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The 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.
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.
Claims 16, 18-23, 27-34 and 36 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 16 recites the limitation " wherein the PPG and ECG signals obtained from the plurality of subjects are paired; wherein the paired signals are obtained from the same subject at the same time”. It is unclear how the paired signals can be from the same subject while also being from a plurality of subjects. Furthermore, the limitation “the paired signals” is lacking antecedent basis.
Claim 20 recites the limitation " an ECG signal of the subject". There is insufficient antecedent basis for this limitation in the claim. It is unclear if this is the same ECG signal of claim 14 or a different ECG signal. The same issue is present in claims 21, and 31-32. The examiner recommends amending the claims to recite “to the generated ECG signal of the subject”.
In claim 23 it is unclear how the attention-based generator (which is part of the deep learning network) focuses on a PQRSTU complex (which is a component of an ECG not a PPG) if the deep learning network is used to generate the ECG signal. The ECG signal would first have to be generated before it can be focused on. It is unclear how the element that is generating the ECG signal (the deep learning network that includes the attention-based generator) can focus on something it hasn’t generated yet. The same issue is present in claim 34.
Claim 27 recites the limitation " the generated ECG signal of the subject" in line 7. There is insufficient antecedent basis for this limitation in the claim. It is recommended line 4 of the same claim be amended to say “generate ECG signal of the subject” to fix this issue.
In regards to claim 30 it is unclear what ECG signals the discriminator is operating on. Claim 27 says it acquires a PPG signal and generates an ECG signal of the subject. It is recommended the claim be amended the same way claim 19 has been amended.
Claims not explicitly rejected above are rejected because they depend from claims rejected above as indefinite.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 14-16, 25-27, and 36 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Biswas (US 20200196897 A1).
In regards to claim 1 Biswas teaches a method for generating an electrocardiogram (ECG) signal from a photoplethysmogram (PPG) signal ([0065]), comprising:
using a processor to receive a PPG signal of a subject ([0065]);
using the processor to generate an ECG corresponding to the PPG of the subject signal using an algorithm based on a deep learning network trained with PPG and ECG signals obtained from a plurality of subjects ([0036-0037] According to an embodiment, said training data further comprises abnormality data corresponding to abnormalities of heart functioning acquired from other subjects, said abnormality data comprising a PPG signal and an associated ECG signal. [0128] [0172]);
and outputting the generated ECG signal ([0047] [0173]).
In regards to claim 14 Biswas teaches an electronic device, comprising:
a processor adapted to receive an input PPG signal of a subject as an input ([0065]);
non-transitory computer readable media compatible with the processor having stored instructions that direct the processor to generate an ECG signal of the subject corresponding to the input PPG signal of the subject using an algorithm based on a deep learning network trained with PPG and ECG signals obtained from a plurality of subjects ([0036-0037] [0063] [0065] [0128] [0172]);
and an output device connected to the processor that outputs the generated ECG signal ([0047] [0173]).
In regards to claim 15 Biswas teaches the electronic device of claim 14, comprising a PPG sensor that obtains the PPG signal of the subject ([0172]).
In regards to claim 16 Biswas teaches the electronic device of claim 14, wherein the PPG and ECG signals obtained from the plurality of subjects are paired ([0036] [0098-0099], training signals come from multiple subjects and are paired).
In regards to claim 25 Biswas teaches the electronic device of claim 14, wherein the processor is configured to extract cardiac information and/or estimate heart rate (HR) using the generated ECG signal and the PPG signal of the subject ([0013]).
In regards to claim 26 Biswas teaches the electronic device of claim 15, wherein the electronic device is adapted to be worn by the subject ([0051]).
In regards to claim 27 Biswas teaches non-transitory computer readable media for use with a processor, the computer readable media having stored thereon instructions that direct the processor to ([0063]):
receive a PPG signal of a subject ([0065]);
generate an ECG signal of the subject corresponding to the input PPG signal of the subject using an algorithm based on a deep learning network trained with PPG and ECG signals obtained from a plurality of subjects ([0036-0037] [0063] [0065] [0128] [0172]);
and output the generated ECG signal ([0047] [0173]).
In regards to claim 36 Biswas teaches non-transitory computer readable media of claim 27, wherein the instructions direct the processor to extract cardiac information and/or estimate heart rate using the generated ECG and the PPG signal ([0013]).
Claim Rejections - 35 USC § 103
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.
Claim(s) 18-19, 28-30, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Biswas (US 20200196897 A1) as applied to claims 14, 16 and 27, in view of Chen (EmotionalGAN: Generating ECG to Enhance Emotion State Classification).
In regards to claim 18 Biswas teaches the electronic device of claim 16. Biswas fails to teach a device wherein the deep learning network comprises at least one generator and at least one discriminator. Chen teaches training and using a Generative Adversarial Network (GAN) to generate an ECG signal (Pg 311, 3.2 ECG generation using EmotionalGAN). It would have been prima facie obvious to a person of ordinary skill in the art to make the deep learning network of Biswas a GAN and train it using the PPG and ECG signals from a plurality of subjects to take in data (a PPG signal) and output an ECG signal like the GAN of Chen. Doing so would merely be a simple substitution of one known deep learning network (GAN) for another (DNN) to obtain predictable results.
In regards to claim 19 modified Biswas teaches the electronic device of claim 18, wherein the at least one discriminator operates on the ECG signals obtained from the plurality of subjects in the time domain (Biswas [0024-0027]).
In regards to claim 28 Biswas teaches the non-transitory computer readable media of claim 27. Biswas fails to teach a non-transitory computer readable media wherein the deep learning network comprises a generative adversarial network (GAN). Chen teaches training and using a Generative Adversarial Network (GAN) to generate an ECG signal (Pg 311, 3.2 ECG generation using EmotionalGAN). It would have been prima facie obvious to a person of ordinary skill in the art to make the deep learning network of Biswas a GAN and train it using the PPG and ECG signals from a plurality of subjects to take in data (a PPG signal) and output an ECG signal like the GAN of Chen. Doing so would merely be a simple substitution of one known deep learning network (GAN) for another (DNN) to obtain predictable results.
In regards to claim 29 Biswas teaches non-transitory computer readable media of claim 27. Biswas fails to teach a non-transitory computer readable media wherein the deep learning network comprises at least one generator and at least one discriminator. Chen teaches training and using a Generative Adversarial Network (GAN) to generate an ECG signal (Pg 311, 3.2 ECG generation using EmotionalGAN). It would have been prima facie obvious to a person of ordinary skill in the art to make the deep learning network of Biswas a GAN and train it using the PPG and ECG signals from a plurality of subjects to take in data (a PPG signal) and output an ECG signal like the GAN of Chen. Doing so would merely be a simple substitution of one known deep learning network (GAN) for another (DNN) to obtain predictable results.
In regards to claim 30 modified Biswas teaches the non-transitory computer readable media of claim 29, wherein the at least one discriminator operates on ECG signals in the time domain (Biswas [0024-0027]).
In regards to claim 37 Biswas teaches the electronic device of claim 14. Biswas fails to teach a device wherein the deep learning network comprises a generative adversarial network (GAN). Chen teaches training and using a Generative Adversarial Network (GAN) to generate an ECG signal (Pg 311, 3.2 ECG generation using EmotionalGAN). It would have been prima facie obvious to a person of ordinary skill in the art to make the deep learning network of Biswas a GAN and train it using the PPG and ECG signals from a plurality of subjects to take in data (a PPG signal) and output an ECG signal like the GAN of Chen. Doing so would merely be a simple substitution of one known deep learning network (GAN) for another (DNN) to obtain predictable results.
Claim(s) 22-23 and 33-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Biswas (US 20200196897 A1) in view of Chen (EmotionalGAN: Generating ECG to Enhance Emotion State Classification) as applied to claims 18 and 29, further in view of Oktay (Attention U-Net: Learning Where to Look for the Pancreas – cited by applicant).
In regards to claim 22 Biswas in view of Chen teaches the electronic device of claim 19. Biswas in view of Chen fails to teach a device wherein the at least one generator is an attention-based generator. Oktay teaches an attention-based model that that automatically learns to focus on target structures (Abstract, Introduction). It would have been prima facie obvious to a person of ordinary skill in the art to modify the generator of Biswas in view of Chen to be an attention-based generator like the model of Oktay. Doing so would merely be a simple substitution of one known model for another to obtain the predictable result of having a generator that focuses on specific sections of data.
In regards to claim 23 modified Biswas teaches the electronic device of claim 22, wherein the at least one attention-based generator focuses on specific sections of data. t (Oktay Abstract, introduction “AGs automatically learn to focus on target structures without additional supervision). Modified Biswas fails to teach that the at least one attention-based generator focuses on at least one region selected from a P,Q,R,S,T,U complex to generate the ECG signal of the subject. It would have been obvious to a person of ordinary skill in the art make the target structure to be a selected section of the PPG and the generated ECG of modified Biswas in order to improve the PPG to ECG model sensitivity (Oktay Introduction “the proposed AGs improve model sensitivity”). Any structure of a generated ECG would inherently include a region of a P,Q,R,S,T,U complex as those are all included within a generated ECG.
In regards to claim 33 Biswas in view of Chen teaches the non-transitory computer readable media of claim 29. Biswas in view of Chen fails to teach a non-transitory computer readable media wherein at least one generator is an attention-based generator. Oktay teaches an attention-based model that that automatically learns to focus on target structures (Abstract, introduction). It would have been prima facie obvious to a person of ordinary skill in the art to modify the generator of Biswas in view of Chen to be an attention-based generator like the model of Oktay. Doing so would merely be a simple substitution of one known model for another to obtain the predictable result of having a generator that focuses on specific sections of data in order to improve model sensitivity (Oktay Introduction “the proposed AGs improve model sensitivity”).
In regards to claim 34 modified Biswas teaches the non-transitory computer readable media of claim 33, wherein the at least one attention-based generator focuses on specific sections of data (Oktay Abstract, introduction “AGs automatically learn to focus on target structures without additional supervision). Modified Biswas fails to teach that the at least one attention-based generator focuses on at least one region selected from a P,Q,R,S,T,U complex to generate the ECG signal of the subject. It would have been obvious to a person of ordinary skill in the art make the target structure to be a selected section of the PPG and the generated ECG of modified Biswas in order to improve the PPG to ECG model sensitivity (Oktay Introduction “the proposed AGs improve model sensitivity”). Any structure of a generated ECG would inherently include a region of a P,Q,R,S,T,U complex as those are all included within a generated ECG.
Examiner’s Note
Regarding claims 20 and 31, none of the prior art teaches or suggests, either alone or in combination, a device comprising a deep learning network with one discriminator that operates on ECG signals in the frequency domain, and a second discriminator that operates on ECG signals in the time domain, in combination with the other claimed elements.
Regarding claims 21 and 32, none of the prior art teaches or suggests, either alone or in combination, a device comprising a deep learning network with a first generator that translates a PPG signal to an ECG signal, a second generator that translates an ECG signal to PPG data; a first and second discriminator that operates on ECG signals in the frequency and time domain respectively, and a third and fourth discriminator that operates on ECG signals in the frequency and time domains, respectively, in combination with the other claimed elements.
Claims 20-21 and 31-32 contain no rejections however they are not in condition for allowance due to their rejections under 35 U.S.C. 112(b).
Response to Arguments
Applicant’s arguments, see remarks, filed 01/28/2026, with respect to the 35 U.S.C. 112(b) rejections of claims 1, 14-16, 18-26, 30-32, and 34-35, with the exception of the rejection of claim 30 (regarding the claim being unclear what ECG signals the discriminator is operating on), have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejections of claims 1, 14-16, 18-26, 30-32, and 34-35 have been withdrawn. However, upon consideration of the current amended, a new grounds of rejection have been made.
Applicant's arguments filed 01/28/2026 regarding the 35 U.S.C. 112(b) rejections of claim 30 (regarding the claim being unclear what ECG signals the discriminator is operating on) have been fully considered but they are not persuasive. The claim has not been amended to fix the issue.
Applicant’s arguments, see remarks, filed 01/28/2026, with respect to the 35 U.S.C. 102 rejection(s) of claim(s) 11, 14-16, 25-27, and 36, under Biswas, have been fully considered and are not persuasive. Biswas teaches training data from a plurality of subjects in paragraphs [0036-0037]. For this reason the applicant’s arguments regarding the dependent claims are also not purgative as they depend on Biswas not teaching data from a plurality of subjects.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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/LUCY EPPERT/ Examiner, Art Unit 3791
/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791