DETAILED ACTION
Claims 1-6, 8-10, 14-20, 22-24, 28-34, 36-38, & 42 are currently pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/30/2025 has been entered.
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 1-6, 8-10, 14-20, 22-24, 28-34, 36-38, & 42 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 1-6, 8-10, 14 are directed to a method (i.e., a process); Claims 15-20, 22-24, & 28 are directed to a system (i.e., a machine); and Claims 29-34, 36-38, & 42 are directed to a CRM (i.e., a manufacture). Accordingly, claims 1-6, 8-10, 14-20, 22-24, 28-34, 36-38, & 42 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1. A computer-implemented method of interpreting images and/or waveforms and determining differences between populations, input sources sensors and/or test subjects, wherein a device is provided, the device having at least one processor and at least one computer-readable non-transitory memory storing at least one program for execution by the at least one processor, the at least one program including instructions, which, when executed by the at least one processor cause the at least one processor to perform operations comprising:
receiving, in the at least one memory a first modulated digital data signal from at least one of the input sources;
automatically digitally encoding the first received modulated digital data signal into a first lower dimensional representation into the at least one memory;
receiving, in the at least one memory, a second modulated digital data signal from the at least one of the input source sensors or from a second input source sensor;
automatically digitally encoding the second received modulated digital data signal into a second lower dimensional representation in the at least one memory, wherein at least one of the first lower dimensional representation and the second lower dimensional representation correspond to a representation of a signal space;
comparing the first low dimensional representation with the second low dimensional representation to generate a reconstruction in the at least one memory;
digitally decoding the representation to reconstruct a data signal into a format similar to that of the received modulated digital data signal, wherein the first modulated digital data signal and/or the second modulated digital data signal comprises an electroencephalogram (EEG), and wherein at least one pathology is mapped to one or more segments of the decoded representation via a function; and
transmitting, over a network, a signal corresponding to the digitally decoded representation, wherein the signal is analyzed with a decision exploration (DE) model to generate a decision, wherein the decision includes one or more of an admission decision, a readmission decision, a risk of mortality, and a diagnosis code, and wherein the diagnosis code comprises an International Classification of Diseases (ICD) code.
The Examiner submits that the foregoing underlined limitations constitute performing mathematical calculations because encoding received data, comparing data, reconstructing data, and decoding the reconstructed data are performed via a trained coding architecture. The Examiner asserts the encoding and decoding steps using a trained coding architecture involves mathematical operations that encode and decode signals. The courts have found mathematical algorithms to be drawn to the judicial exception of an abstract idea (In re Grams, 888 F.2d 823, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)). Furthermore, the steps of mapping pathology with segments of the decoded data representation via a function and analyzing the signal with a model to generate a decision may be practically performed in the human mind using observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Here, the claimed limitations are identified as being directed to the mental process grouping of abstract ideas, and mathematical concepts grouping of abstract ideas. The underlined limitations are considered together as a single abstract idea for further analysis. Thus, the instant claim is drawn to a judicial exception.
Accordingly, independent claim 1 and analogous independent claims 15 & 29 recite at least one abstract idea.
Furthermore, dependent claims 2-6, 8-10, 14, 16-20, 22-24, 28, 30-34, 36-38, & 42 further narrow the abstract idea described in the independent claims. Claims 2-6, 14, 16-20, 28, 30-34, & 42 recites the type of data obtained and determining differences; Claims 8, 22, & 36 recites encoding the data; Claims 9, 23, & 37 recites generating the reconstruction using GAN; Claims 10, 24, & 38 recites to analyzing the reconstructed signal to generate an output. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 1 and analogous independent claims 15 & 29, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
1. A computer-implemented method of interpreting images and/or waveforms and determining differences between populations, input sources sensors and/or test subjects, wherein a device is provided, the device having at least one processor and at least one computer-readable non-transitory memory storing at least one program for execution by the at least one processor, the at least one program including instructions, which, when executed by the at least one processor cause the at least one processor to perform operations comprising:
receiving, in the at least one memory a first modulated digital data signal from at least one of the input sources;
automatically digitally encoding the first received modulated digital data signal into a first lower dimensional representation into the at least one memory;
receiving, in the at least one memory, a second modulated digital data signal from the at least one of the input source sensors or from a second input source sensor;
automatically digitally encoding the second received modulated digital data signal into a second lower dimensional representation in the at least one memory, wherein at least one of the first lower dimensional representation and the second lower dimensional representation correspond to a representation of a signal space;
comparing the first low dimensional representation with the second low dimensional representation to generate a reconstruction in the at least one memory;
digitally decoding the representation to reconstruct a data signal into a format similar to that of the received modulated digital data signal, wherein the first modulated digital data signal and/or the second modulated digital data signal comprises an electroencephalogram (EEG), and wherein at least one pathology is mapped to one or more segments of the decoded representation via a function; and
transmitting, over a network, a signal corresponding to the digitally decoded representation, wherein the signal is analyzed with a decision exploration (DE) model to generate a decision, wherein the decision includes one or more of an admission decision, a readmission decision, a risk of mortality, and a diagnosis code, and wherein the diagnosis code comprises an International Classification of Diseases (ICD) code.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the device, processor, & memory; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of receiving data from various input sources and transmitting a signal; the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)).
Regarding the additional limitation of input source sensors providing data, the Examiner submits that these additional limitations do no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the steps of the at least one abstract idea are performed (see MPEP § 2106.05(h)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claim 15 & 29 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recite at least one abstract idea.
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 10 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, the additional limitations of the device, processor, & memory; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of receiving data and transmitting a signal; the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)). The Examiner has reevaluated such limitations and determined it to not be unconventional as it merely consist of receiving and transmitting data over a network. See MPEP 2106.05(d)(II). Regarding the additional limitation of input source sensors providing data, the Examiner submits that these additional limitations do no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the steps of the at least one abstract idea are performed (see MPEP § 2106.05(h)).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-6, 8-10, 14-20, 22-24, 28-34, 36-38, & 42 are ineligible under 35 USC §101.
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.
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.
Claims 1-4, 8, 10, 14-18, 22, 24, 28-32, 36, 38, & 42 are rejected under 35 U.S.C. 103 as being unpatentable over Ansari (US20210304855) in view of Lyman (US20200160980).
As per claim 1, Ansari discloses a computer-implemented method of interpreting images and/or waveforms and determining differences between populations, input sources sensors and/or test subjects, wherein a device is provided, the device having at least one processor and at least one computer-readable non-transitory memory storing at least one program for execution by the at least one processor, the at least one program including instructions, which, when executed by the at least one processor cause the at least one processor to perform operations (para. 139-140: computing device with processor executing process) comprising:
receiving, in the at least one memory a first modulated digital data signal from at least one of the input sources (para. 13,43: raw input data received from sensors, input data can also be a digital or analog signal, depending on the embodiment and scenario. For example, the input signal may be transmitted by a monitor that emits analog waveforms, or a digitally-encoded signal; input signal may be continuous, discrete, sinusoidal, periodic, aperiodic, etc.);
automatically digitally encoding the first received modulated digital data signal into a first lower dimensional representation into the at least one memory (para. 15, 46: input data encoded to reduced dimensionality via computer system);
receiving, in the at least one memory, a second modulated digital data signal from the at least one of the input source sensors or from a second input source sensor (para. 13,43: raw input data received from sensors, input data can also be a digital or analog signal, depending on the embodiment and scenario. For example, the input signal may be transmitted by a monitor that emits analog waveforms, or a digitally-encoded signal; input signal may be continuous, discrete, sinusoidal, periodic, aperiodic, etc.);
automatically digitally encoding the second received modulated digital data signal into a second lower dimensional representation in the at least one memory, wherein at least one of the first lower dimensional representation and the second lower dimensional representation correspond to a representation of a signal space (para. 15, 46: raw input signals input into encoder to generate reduced dimensionality representations; the embedded features are reduced-dimensionality representations of the input signal);
comparing the first low dimensional representation with the second low dimensional representation to generate a reconstruction in the at least one memory (para. 72: decoder maps and analyses embedded features and reconstructs an output signal);
digitally decoding the representation to reconstruct a data signal into a format similar to that of the received modulated digital data signal (para. 72, 77: decoder maps and analyses embedded features and reconstructs an output signal; output signal can be in the form of the input signal i.e. waveform), wherein the first modulated digital data signal and/or the second modulated digital data signal comprises an electroencephalogram (EEG), and wherein at least one pathology is mapped to one or more segments of the decoded representation via a function (para. 32, 85, 134: EEG waveform obtained; output is then analyzed to identify a patient condition via a classifier); and
transmitting, over a network, a signal corresponding to the digitally decoded representation (para. 72, 77, 116, 123: signal is outputted, client device communicates with server over network), wherein the signal is analyzed with a decision exploration (DE) model to generate a decision (para. 134: classifier identifies various conditions for subject), wherein the decision includes one or more of an admission decision, a readmission decision, a risk of mortality, and a diagnosis code (para. 103: system can output risk stratification for patient populations).
Ansari does not expressly teach wherein the diagnosis code comprises an International Classification of Diseases (ICD) code.
Lyman, however, teaches to reconstructing ECG or EEG data and outputting diagnosis ICD decision (para. 59, 74, 254).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Lyman with Ansari based on the motivation of aid medical professionals or other users in diagnosing, triaging, classifying, ranking, and/or otherwise reviewing medical scans (Lyman – para. 38.
As per claim 2, Ansari and Lyman teach the method of claim 1. Ansari further teaches wherein the first modulated data signal and/or the second modulated data signal comprises one or more of an electronic health record (EHR), an electrocardiogram (ECG), a speech waveform, and a spectrogram (para. 32: ECG data obtained).
As per claim 3, Ansari and Lyman teach the method of claim 2. Ansari further teaches wherein the first modulated data signal and/or the second modulated data signal further comprises the ECG, and wherein heart beats and fiducial markers are identified in the decoded representation (para. 32, 136: ECG data and markers identified).
As per claim 4, Ansari and Lyman teach the method of claim 2. Ansari further teaches wherein the first modulated data signal and/or the second modulated data signal further comprises the ECG, and wherein an arrhythmia is identified from the decoded representation (para. 32, 136: ECG data obtained and arrhythmia condition identified).
As per claim 8, Ansari and Lyman teach the method of claim 1. Ansari further teaches wherein the first lower dimensional representation and/or the second lower dimensional representation is encoded with one or more of a perturbation, a compactless loss, and a cross-entropy for classification (para. 85: loss less encoding performed).
As per claim 10, Ansari and Lyman teach the method of claim 1. Ansari further teaches wherein the signal is analyzed to highlight the differences between the populations, the input sources or the test subjects (para. 135, 136; Fig. 7A: system obtains and outputs visualizations allowing for highlighting of differences in the input data).
As per claim 14, Ansari and Lyman teach the method of claim 1. Ansari further teaches wherein the representation is a blobby representation, and the decoded representation is a decoded blobby representation (para. 72, 77: decoder maps and analyses embedded features and reconstructs an output signal; output signal can be in the form of the input signal i.e. waveform; the Examiner interprets the blobby representation as analogous to a waveform format).
Claims 15-18, 22, 24, & 28 recite substantially similar limitations as those already addressed in claims 1-4, 7-8, 10, & 14, and, as such, are rejected for similar reasons as given above.
Claims 29-32, 36, 38, & 42 recite substantially similar limitations as those already addressed in claims 1-4, 7-8, 10, & 14, and, as such, are rejected for similar reasons as given above.
Claims 5-6, 19-20, & 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Ansari (US20210304855) and Lyman as applied to claims 2, 16, & 30, and in further view of Korzekwa (“Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech”).
As per claim 5, Ansari and Lyman teach the method of claim 2, but do not expressly teach wherein the first modulated data signal and/or the second modulated data signal comprises the speech waveform, and wherein differences relative to a standard pronunciation are identified in the decoded representation.
Korzekwa, however, teaches to obtaining speech waveform data, decoding the data, and identifying differences from normal speech using a training data set (pg. 2).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Korzekwa with Ansari and Lyman based on the motivation of lead to more accurate diagnoses and aid in reconstructing healthy speech for afflicted patients (Korzekwa – pg. 1).
As per claim 6, Ansari and Lyman teach the method of claim 2, but do not expressly teach wherein the first modulated data signal and/or the second modulated data signal comprises the speech waveform, and wherein at least one anatomical structure is associated with at least one segment of the decoded representation.
Korzekwa, however, teaches to obtaining speech waveform data, decoding the data, and identifying differences from normal speech using a training data set (pg. 2). Korzekwa also teaches to identifying segments of the waveform that are associated with a defect in the patient’s body such as weakness in the muscles controlled by the brain (pg. 1, 4).
The motivations to combine the above mentioned references are discussed in the rejection of claim 5, and incorporated herein.
Claims 19-20 & 33-34 recite substantially similar limitations as those already addressed in claims 5-6, and, as such, are rejected for similar reasons as given above.
Claims 9, 23, & 37 are rejected under 35 U.S.C. 103 as being unpatentable over Ansari (US20210304855) and Lyman as applied to claims 1, 15, and 29 above, and in further view of Hu (US20190156200).
As per claim 9, Ansari and Lyman teach the method of claim 1, but do not expressly teach wherein the reconstruction is generated with generative adversarial reconstruction (GAN).
Hu, however, teaches to reconstructing input data using generative adversarial network modelling (para. 25, 28).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Hu with Ansari and Lyman teach based on the motivation of prediction models need only be trained on a training set corresponding only to a subset of the classifications/concepts, the overall amount of training needed may be reduced (Hu – para. 50.
Claims 23 & 37 recite substantially similar limitations as those already addressed in claim 9, and, as such, are rejected for similar reasons as given above.
Response to Arguments
Applicant’s arguments on pages 1-7 regarding claims 1-6, 8-10, 14-20, 22-24, 28-34, 36-38, & 42 being rejected under 35 USC § 101 have been fully considered but they are not persuasive. Applicant claims that:
The claims are similar to Example 41 and provide an improvement to technology.
Regarding A, the Examiner asserts that Example 41 clearly identified a technical problem where prior art cryptographic encoding and decoding methods required expensive encoding and decoding hardware and a secure way of sharing the private key used to encrypt and decrypt the message. The Example 41 claims provided a technical solution allowing for information to be easily shared between users who do not know each other and have not shared the key used to encrypt and decrypt the information. However, the Examiner asserts that the limitations identified in the 101 rejections above provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of encoding data, decoding the data to reconstruct the data into a similar format, and analyzing the data is performed using the decision exploration model.” The decision exploration model is used to generally apply the abstract idea without placing any limits on how the decision exploration model functions. Rather, these limitations only recite the outcome of decoding and analyzing the decoded representation and do not include any details about how the “decoding” and “analyzing” are accomplished. See MPEP 2106.05(f).
The claims qualify for the streamline eligibility analysis because they do not clearly seek to tie up any judicial exception.
As per B, The Examiner asserts that it is not clear how the claims improve a technology or computer functionality and therefore the streamlined eligibility analysis is not appropriate.
Under the Full Eligibility Analysis the claims are eligible
As per C, the Examiner refers to the updated 101 rejection above in view of the amended claims.
The pending claims address a technical problem associated with processing certain types of data.
The Examiner, however, asserts that that there is no indication here that the claimed invention addresses a problem specifically arising in the realm of a technology; the Applicant does not provide adequate evidence or technical reasoning how the process improves the efficiency of the computer or technology, as opposed to the efficiency of the process, or of any other technological aspect. Further, the problems the invention is attempting to solve are “interpreting images or waveforms to determine differences between populations”. (citing Spec. ¶ 11). The solution provided here has not been described or claimed as anything more than a generic use of existing technology performing based, purely conventional functions of a computer. Therefore, the Examiner asserts that the claims as a whole are not directed significantly more than an abstract idea.
Applicant’s arguments on pages 8-9 regarding claims 1-6, 8-10, 14-20, 22-24, 28-34, 36-38, & 42 being rejected under 35 USC § 103(a) have been fully considered but they are not persuasive.
Applicant argues that Ansari fails to teach identifying pathology associated with a segment of a decoded representation of the data comprises the EEG.
The Examiner, however asserts that the Applicant has made conclusory statements regarding Ansari and fails to describe specifically why Ansari does not teach identifying pathology associated with a segment of a decoded representation of the data comprises the EEG. Ansari teaches to automatic interpretation of physiologic signals, such as from an EEG waveform, to facilitate identification, prediction and/or diagnosis of acute and chronic conditions and overall health (para. 32). Ansari further teaches to extracting embedded features from the input signal such as an EEG via a decoder then processed by the distributor to generate an output (para. 72-73). The output is then analyzed to identify a patient condition via a classifier (para. 134). The Examiner asserts that under the broadest reasonable interpretation a pathology can be considered analogous to a condition (see definition of pathology “any deviation from a healthy, normal, or efficient condition” https://www.dictionary.com/browse/pathology). Therefore, the Examiner argues that Ansari clearly teaches identifying pathology associated with a segment of a decoded representation of the data comprises the EEG.
Applicant argues that Ansari fails to teach the encoding step that automatically encodes modulated data signals into lower dimensional representations that correspond to a representation of a signal space.
The Examiner, however, asserts that Applicant has only provided conclusory statements regarding how the Ansari reference fails to teach the encoding step and fails to explain the differences between Ansari and the specific claimed limitations. Ansari teaches to where raw input signals are input into encoder to generate reduced dimensionality representations; the embedded features are reduced-dimensionality representations of the input signal. The Examiner asserts that these embedded features are analogous to the claimed lower dimensional representations that correspond to a representation of a signal space. The embedded features taught by Ansari contain layers that represent one aspect of the representation – such as morphological features of an ECG and location of each beat of the ECG (para. 46). Therefore, Ansari clearly teaches to the aforementioned limitations above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wu (US20210315470) teaches reconstructing electrocardiogram (ECG) waveforms from photoplethysmogram (PPG) signals using dictionary learning and deep learning for continuous monitoring and analytics.
Kalidas (US20220015711) teaches to determining cardiac arrhythmias from electrocardiogram (ECG) waveforms.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached on 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/Primary Examiner, Art Unit 3619