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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“signal collection stage”, “feature extraction stage”, “machine learning model construction stage”, and “glycated hemoglobin/blood glucose estimation stage” in claim 1, and
“signal collection unit”, “feature extraction unit”, “model construction unit”, and “glycated hemoglobin/blood glucose estimation unit” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 1-12 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 9 recite(s) an abstract idea of constructing a model for estimating glycated hemoglobin or blood glucose by learning training data including the plurality of features and generating input data on the basis of the bio-signal extracted from the measurement subject being measured and inputting the input data to the machine learning model, so as to estimate glycated hemoglobin or blood glucose of the measurement subject being measured. Under the broadest reasonable interpretation, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper. These steps amount to an evaluation or judgement that can be performed wholly mentally and/or with pen and paper based on the collected bio-signals and features extracted from the bio-signal (step 2A: Prong One).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the “a signal collection stage/unit of collecting a bio-signal of a measurement subject being measured” and “a feature extraction stage/unit of extracting a plurality of features from the bio-signal” are merely insignificant extra-solution activity, such as mere data gathering, recited at a high level of generality and/or in a well-understood, routine, and conventional way, of the information needed to carry out the claimed algorithm (step 2A: Prong Two).
Moreover, this judicial exception is not integrated into a practical application because the claim does not recite any limitations that amount to an improvement in the functioning of a computer, or an improvement to other technology or technical field, apply or use the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement the judicial exception with, or using a judicial exception in conjunction 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 is more than a drafting effort designed to monopolize the exception (step 2B).
Regarding dependent claims 2-3, the claimed “PPG signals” and “LED module” are not sufficient to amount to significantly more than the judicial exception because such elements are well-understood, routine, and conventional in the art, as evidenced by Huiku (US 2009/0326342 A1) – see [0027]. They represent components and/or activities which would routinely be used in applying the abstract idea. As such, they do not meaningfully limit the claim, taken as a whole, to a particular application of the abstract idea.
Regarding dependent claims 4-8 and 10-12, the limitations of these dependent claim(s) merely add details to the algorithm which forms the abstract idea, but does not contain any further “additional elements”. Thus, the dependent claim(s) are not significantly more than the extended abstract idea.
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.
Claim(s) 1-4, 7, and 9-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ribas et al. (EP 2544124 A1).
Regarding claim 1, Ribas et al. discloses a method for non-invasive estimation of glycated hemoglobin (HbAlc) or blood glucose by using machine learning, the method comprising:
a signal collection stage of collecting a bio-signal of a measurement subject being measured (see [0014] – “System 110 is a PPG pulse system which may provide an SpO2 level, which will be further described below”);
a feature extraction stage of extracting a plurality of features from the bio-signal (see [0032] – “In an embodiment, in accordance with block 230 of method 200, various parameters may be extracted from a PPG signal in order to determine the glucose level of a patient”);
a machine learning model construction stage of constructing a machine learning model for estimating glycated hemoglobin or blood glucose by learning training data including the plurality of features (see [0016] – “Embodiments may use a machine learning algorithm, such as a "random forest", deep belief network trained using restricted Boltzmann machines, or support vector machine”); and
a glycated hemoglobin/blood glucose estimation stage of generating input data on the basis of the bio-signal extracted from the measurement subject being measured and inputting the input data to the machine learning model, so as to estimate glycated hemoglobin or blood glucose of the measurement subject being measured (see [0071] – “Once the fixed dimension output vector V(n) has been generated at block 240 of method 200, the estimation of the variable of interest, such as glucose level, may be determined using a function approximation system 140 of FIG. 1A, in accordance with block 250 of method 200”).
Regarding claim 2, Ribas et al. discloses the signal collection stage comprises measuring PPG signals of the measurement subject being measured and collecting the same as the bio-signal (see [0027] – “Obtaining a PPG curve in accordance with block 220 of method 200 may be possible using non-invasive techniques. A PPG curve may detect volume changes in a tissue's micro-vascular network. To obtain such a curve, one or more light sources may be required to illuminate the tissue where the sample is to be obtained. Additionally, one or more photodetectors may be used to measure variations in light intensity, associated with the changes in tissue perfusion in the detection volume”).
Regarding claim 3, Ribas et al. discloses the signal collection stage comprises: irradiating a body part of the measurement subject being measured with light through an LED module positioned on one side of the body part; detecting transmitted light transmitting the body part or reflected light reflected from the body part through a photo detector positioned corresponding to the LED module; and measuring the PPG signals based on a change in intensity of the transmitted light or the reflected light (see [0027] – “Obtaining a PPG curve in accordance with block 220 of method 200 may be possible using non-invasive techniques. A PPG curve may detect volume changes in a tissue's micro-vascular network. To obtain such a curve, one or more light sources may be required to illuminate the tissue where the sample is to be obtained. Additionally, one or more photodetectors may be used to measure variations in light intensity, associated with the changes in tissue perfusion in the detection volume”).
Regarding claim 4, Ribas et al. discloses the feature extraction stage comprises collecting external features directly measured from the measurement subject being measured along with internal features extracted directly from the PPG signals and determining the same as the plurality of features (see [0015] – “The vector includes information about the pulse shape (measured by autoregression coefficients and moving averages), the average distance between pulses, variance, instant energy information, energy variance, and patient medical information, such as gender, height, weight, age, body mass index, and other measures”).
Regarding claim 7, Ribas et al. discloses the machine learning model comprises a machine learning model trained using Random Forest or XGBoost algorithm (see [0016] – “Embodiments may use a machine learning algorithm, such as a "random forest", deep belief network trained using restricted Boltzmann machines, or support vector machine”; see also [0072]-[0081]).
Regarding claim 9, Ribas et al. discloses an apparatus for non-invasive estimation of glycated hemoglobin (HbAlc ) or blood glucose by using machine learning, the apparatus comprising:
a signal collection unit of collecting a bio-signal of a measurement subject being measured (see [0014] – “System 110 is a PPG pulse system which may provide an SpO2 level, which will be further described below”);
a feature extraction unit of extracting a plurality of features from the bio-signal (see [0032] – “In an embodiment, in accordance with block 230 of method 200, various parameters may be extracted from a PPG signal in order to determine the glucose level of a patient”);
a model construction unit of constructing a machine [earning model for estimating glycated hemoglobin or blood glucose by learning training data including the plurality of features (see [0016] – “Embodiments may use a machine learning algorithm, such as a "random forest", deep belief network trained using restricted Boltzmann machines, or support vector machine”); and
a glycated hemoglobin/blood glucose estimation unit of generating input data on the basis of the bio-signal extracted from the measurement subject being measured and inputting the input data to the machine learning model, so as to estimate glycated hemoglobin or blood glucose of the measurement subject being measured (see [0071] – “Once the fixed dimension output vector V(n) has been generated at block 240 of method 200, the estimation of the variable of interest, such as glucose level, may be determined using a function approximation system 140 of FIG. 1A, in accordance with block 250 of method 200”).
Regarding claim 10, Ribas et al. discloses the feature extraction stage comprises collecting external features directly measured from the measurement subject being measured along with internal features extracted directly from the PPG signals and determining the same as the plurality of features (see [0015] – “The vector includes information about the pulse shape (measured by autoregression coefficients and moving averages), the average distance between pulses, variance, instant energy information, energy variance, and patient medical information, such as gender, height, weight, age, body mass index, and other measures”).
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.
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.
Claim(s) 5-6 and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ribas et al., further in view of Gunturi et al. (US Publication No. 2016/0324477 A1) and Tang et al. (US Publication No. 2023/0355143 A1).
Regarding claims 5 and 11, Ribas et al. teaches the feature extraction stage comprises: extracting, based on the PPG signals, Zero-Crossing Rate (ZCR) (see [0032]), Auto Correlation (see [0032]), Kaiser-Teager energy (KTE) (see [0032]), Spectral Analysis (SA) (see [0029]), Autoregressive Coefficients (ARC) (see [0015]), Heart Rate (HR) (see [0032]), and Breathing Rate (BR) (see [0048]) as the internal features; and collecting Body Mass Index (BMI) (see [0015]), Finger Width (FW) (see [0046]), and Saturation Pulse Oxygen (Sp02) (see [0014]) as the external features.
It is noted Ribas et al. does not specifically teach extracting Power Spectral Density (PSD) or Wavelet Analysis (WA). However, Gunturi et al. teaches extracting Power Spectral Density (see [0051] – “At step 508, FFT is performed on the coarse heart rate. In one example, an FFT output is squared to generate the power spectral density. The power spectral density of the coarse heart rate provides the range of peaks”). Tang et al. teaches extracting wavelet analysis (see [0062] – “In an embodiment of the present invention, the processor module 50 eliminates low-frequency respiratory disturbances, and obtains multifractal spectrum features of the photoplethysmography signal through analysis of the wavelet). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Ribas et al. to include extracting Power Spectral Density (PSD) and Wavelet Analysis (WA), as disclosed in Gunturi et al. and Tang et al., so as to eliminate low-frequency respiratory disturbances and obtain multifractal spectrum features of the PPG signal (see Tang et al.: [0062]).
Regarding claims 6 and 12, Ribas teaches the feature extraction stage comprises: determining at least one representative feature among the internal features according to importance; and determining the plurality of features by adding at least one of the external features to the representative features (see [0093] – “Training data may include PPG readings, clinical parameters, and glucose values of a set of patients. The derivative of the error is calculated with respect to the output unit's weights, and minimized using the conjugate gradient method. Weights of hidden layers are then adjusted in the same manner, but using the product of error and outgoing weights as an estimate for their error vector. The procedure is repeated, starting from the output layer and progressing to the input layer, until error estimates are obtained for all hidden layers. Weights may then be iteratively optimized for each batch of training data. In this iterative step, the network's output may slowly converge towards the target output, minimizing any error. Weights may be optimized until a predefined criteria of convergence is reached”).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ribas et al., further in view of Frank et al. (US Publication No. 2021/0401330 A1).
Regarding claim 8, it is noted Ribas et al. does not specifically teach the glycated hemoglobin/blood glucose estimation stage comprises analyzing the glycated hemoglobin or blood glucose to determine a diabetes grade of the measurement subject being measured. However, Frank et al. teaches the glycated hemoglobin/blood glucose estimation stage comprises analyzing the glycated hemoglobin or blood glucose to determine a diabetes grade of the measurement subject being measured (see [0027] – “Once the machine learning model is trained, it is used to predict a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device worn by the user during an observation period spanning multiple days. This “diabetes classification” may in some implementations indicate whether a user has diabetes or is at risk for developing diabetes and/or indicate adverse effects that the user is predicted to experience. By way of example, a user may have his or her glucose monitored to predict whether he or she has diabetes (e.g., Type 1 diabetes, Type 2 diabetes, gestational diabetes mellitus (GDM), cystic fibrosis diabetes, and so on) or is at risk for developing diabetes (e.g., prediabetes), and/or whether she is predicted to experience adverse effects associated with diabetes (e.g., retinopathy, neuropathy, comorbidity, dysglycemia, macrosomia requiring a cesarean section, and neonatal hypoglycemia, to name just a few)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Ribas et al. to include the glycated hemoglobin/blood glucose estimation stage comprises analyzing the glycated hemoglobin or blood glucose to determine a diabetes grade of the measurement subject being measured, as disclosed in Frank et al., so as to predict whether a user has diabetes or is at risk for developing diabetes and/or indicate adverse effects that the user is predicted to experience (see Frank et al.: [0027]).
Conclusion
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/DEVIN B HENSON/ Primary Examiner, Art Unit 3791