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 Status
Claims 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2022-0171868, filed on 12/09/2022. Therefore, the effective filing date of claims 1-20 is 12/09/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/02/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show the feature vector (axes and labels are missing) as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification submitted on 05/02/2023 is accepted.
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.
Claim 20 is non-statutory as they recite "a computer readable medium". The claims as instantly
recited read on carrier waves, which are transitory propagating signals and therefore are not proper
patentable subject matter because they do not fit within any of the four statutory categories of
invention (In re Nuijten, Federal Circuit, 2007). It is noted that the recitation of a "non-transitory
computer readable medium" would overcome the rejection with respect to claim 20 reading on signals.
However, the amendment to only "non-transitory computer readable medium" would not overcome
the rejection under 35 U.S.C. 101 since the claims would still be directed to a judicial exception without
significantly more (see below).
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea and/or a natural phenomenon without significantly more.
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1).
Claims 1 and 18 recites calculating the analyte concentration from an in-vivo spectrum obtained non- invasively by using a calibration model; subtracting a spectrum specific to an analyte from the in-vivo spectrum; generating a test calibration model based on feature vectors of the subtracted in-vivo spectrum obtained non-invasively; calculating a test concentration for the analyte from the in-vivo spectrum by using the test calibration model; and analyzing the test concentration for the analyte using the test calibration model to determine accuracy of the analyte concentration non-invasively.
Claim 2 recites scaling a net spectrum of the analyte to the analyte concentration.
Claim 3 recites scaling a net spectrum of the analyte to the analyte concentration and an optical path length.
Claim 4 recites calculating a net analyte signal (NAS) from feature vectors of the subtracted in-vivo spectrum.
Claim 5 recites performing feature extraction on the subtracted in-vivo spectrum.
Claim 6 recites performing principal component analysis (PCA) on the subtracted in-vivo spectrum.
Claim 7 recites determining the accuracy of the analyte concentration by comparing the test concentration for the analyte with a reference value.
Claim 8 recites comparing, with a reference value, a standard deviation of the test concentration for the analyte.
Claim 9 recites comparing, with a reference value, a standard deviation of the test concentration calculated for a predetermined interval.
Claim 10 recites determining that the accuracy of the analyte concentration is high when a standard deviation of the test concentration calculated for a 10-minute interval is less than 50 mg/dl.
Claim 11 recites comparing the test concentration for the analyte with the analyte concentration.
Claim 12 recites that the calibration model is at least one of a partial least squares (PLS) calibration model, a net analyte signal (NAS) calibration model, and a calibration model based on deep learning.
Claim 13 recites the test calibration model is a NAS calibration model.
Claim 14 recites the analyte is at least one of glucose, urea, lactic acid, triglyceride, protein, cholesterol, and ethanol.
Claim 15 recites the in-vivo spectrum is an in- vivo spectrum of a test subject generated based on absorption spectroscopy.
Claim 16 recites the in-vivo spectrum is an in- vivo spectrum of a test subject for near-infrared rays.
Claim 17 recites that the accuracy of the analyte concentration calculated using the calibration model.
Claim 19 recites the method of claim 18, wherein the determining of the accuracy of the analyte concentration comprises comparing the test concentration of the analyte with a reference value to determine the accuracy of the analyte concentration.
Claim 20 recites a non-transitory (see note regarding non-statutory category of invention above) computer-readable recording medium having recorded thereon a program for executing the method of claim 19 on a computer.
The limitations reciting calculating the analyte concentration from an in-vivo spectrum obtained non- invasively by using a calibration model; subtracting a spectrum specific to an analyte from the in-vivo spectrum; generating a test calibration model based on feature vectors of the subtracted in-vivo spectrum obtained non-invasively; and calculating a test concentration for the analyte from the in-vivo spectrum by using the test calibration model are verbal equivalents of mathematical calculations; scaling a net spectrum of the analyte to the analyte concentration; scaling a net spectrum of the analyte to the analyte concentration and an optical path length; calculating a net analyte signal (NAS) from feature vectors of the subtracted in-vivo spectrum; performing principal component analysis (PCA) on the subtracted in-vivo spectrum; comparing, with a reference value, a standard deviation of the test concentration calculated for a predetermined interval; and therefore fall under the “mathematical concept” grouping of ideas.
The limitations reciting analyzing the test concentration for the analyte using the test calibration model to determine accuracy of the analyte concentration non-invasively wherein the determining of the accuracy of the analyte concentration comprises comparing the test concentration of the analyte with a reference value to determine the accuracy of the analyte concentration; determining the accuracy of the analyte concentration by comparing the test concentration for the analyte with a reference value; comparing, with a reference value, a standard deviation of the test concentration for the analyte; determining that the accuracy of the analyte concentration is high when a standard deviation of the test concentration calculated for a 10-minute interval is less than 50 mg/dl; comparing the test concentration for the analyte with the analyte concentration; and that the accuracy of the analyte concentration calculated using the calibration model falls under the “mental process” grouping of ideas. Comparison between a given concentration of analyte with a reference value can be practically performed in the human mind and therefore is a mental process.
The limitations of the calibration model is at least one of a partial least squares (PLS) calibration model, a net analyte signal (NAS) calibration model, and a calibration model based on deep learning; the test calibration model is a NAS calibration model; the analyte is at least one of glucose, urea, lactic acid, triglyceride, protein, cholesterol, and ethanol; the in-vivo spectrum is an in- vivo spectrum of a test subject generated based on absorption spectroscopy; and the in-vivo spectrum is an in- vivo spectrum of a test subject for near-infrared rays merely serve to further limit the recited abstract idea. As such, claims 1-20 recite abstract ideas.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 1 recites an electronic device.
Claim 20 recites a non-transitory (see note above on non-statutory subject matter) computer readable medium.
There are no limitations that indicate that the claimed computer, processor, input device
or computer-readable medium require anything other than generic computing systems. As such, these
limitations equate to mere instructions to implement the abstract idea on a generic computer that the
courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d
at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claim 1 recites an electronic device.
Claim 20 recites a non-transitory (see note above on non-statutory subject matter) computer readable medium.
As aforementioned, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Of note, the court considered the additional elements individually, noting that all the computer functions were “‘well-understood, routine, conventional activit[ies]’ previously known to the industry," each step “does no more than require a generic computer to perform generic computer functions”, and the recited hardware was “purely functional and generic” (573 U.S. at 225-26, 110 USPQ2d at 1984-85).
There are no additional elements that comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
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.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claims 1-7 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Arnold et al. (US7460895B2) in view of Lorenz (US6697654B2).
Regarding claim 1, Arnold teaches:
An electronic device (computer readable storage medium explicitly recited in Col 11, Lines 10-17) for determining accuracy of an analyte concentration non-invasively (see Abstract; invention directed to measuring the concentration of an analyte in a test subject and evaluating the analytical significance using a calibration model; method is also in-vivo meaning non-invasive), the electronic device comprising:
a memory configured to store one or more instructions (see Col 11, Lines 10-33);
and one or more processors, wherein the one or more processors are configured to execute the one or more instructions to (see Col 11, Lines 10-33):
calculate an analyte concentration of an analyte from an in-vivo spectrum obtained non-invasively by using a calibration model (see the prediction calculation for glucose using a calibration model in Fig. 4 and 5; referenced as Examples 2-3 in text);
generate a test calibration model based on feature vectors of the subtracted in- vivo spectrum obtained non-invasively (see Example 1; where the Net Analyte Signal Calibration Model for glucose incorporates principal component analysis in Col 13 Lines 15-23);
calculate a test concentration for the analyte from the in-vivo spectrum by using the test calibration model (Example 2 describes the subsequent step from the net analyte signal of glucose generated in Example 1 to calculate predicted concentrations of glucose);
and determine the accuracy of the analyte concentration calculated using the calibration model by analyzing the test concentration for the analyte using the test calibration model (comparing the NAS calibration vector against a PLS multivariate calibration vectors to assess similarity recited in Col. 15 Lines 39-42). The Examiner notes that the prior art offers multiple models (e.g. NAS, PLS, etc.) and one of ordinary skill in the art can do an accuracy assessment by selecting one of the models to be the test calibration model and one to be the calibration model.
Although Arnold does not explicitly teach the step of subtracting a spectrum specific to the analyte from the in-vivo spectrum, he does disclose that factors chosen for the subsequent PCA step are selected from inspection of the residuals (Col. 13 Lines 15-20) and there is explicit recitation that significant background factors are removed by conventional mathematical models known to one of ordinary skill in the art to obtain the net analyte signal (see Col. 8 Lines 18-23). Lorenz explicitly teaches the mathematical computation of subtracting an analyte-specific/interference spectrum (see Equations 5 and 7). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lorenz’s mathematical computation with Arnold’s existing analyte concentration determination pipeline in order to substitute a known alternative to obtain the predictable result of generating a net analyte signal (see Col. 8 Lines 18-23). This substitution would have been accomplished with reasonable expectation of success as Arnold already includes some type of normalization within his existing pipeline.
Regarding claim 2, Arnold as modified teaches:
The electronic device of claim 1, wherein the one or more processors are configured to obtain the spectrum specific to the analyte by scaling a net spectrum of the analyte to the analyte concentration (see Col.9 Lines 55-65; Equation explicitly recited as Ck = Bk * (A/λ) + C̄b; where Ck is the predicted analyte concentration, Bk is the net calibration vector as it is a normalized version of the analyte signal (see Equation from Col. 9 Lines 23-35), A is absorbance, λ is the path length, and C̄b is the mean baseline concentration; Bk is scaled within the equation)
Regarding claim 3, Arnold as modified teaches:
The electronic device of claim 1, wherein the one or more processors are configured to obtain the spectrum specific to the analyte by scaling a net spectrum of the analyte to the analyte concentration and an optical path length (see Col.9 Lines 55-65; Equation explicitly recited as Ck = Bk * (A/λ) + C̄b; where Ck is the predicted analyte concentration, Bk is the net calibration vector as it is a normalized version of the analyte signal, A is absorbance, λ is the path length, and C̄b is the mean baseline concentration; Bk is scaled within the equation with λ).
Regarding claim 4, Arnold as modified teaches:
The electronic device of claim 1, wherein the one or more processors are configured to generate the test calibration model by calculating a net analyte signal (NAS) from feature vectors of the subtracted in-vivo spectrum (see Example 1; wherein a Generation of an in-vivo Net Analyte Signal Calibration model is described in Col. 13 Lines 15-49).
Regarding claim 5, Arnold as modified teaches:
The electronic device of claim 4, wherein the one or more processors are configured to obtain the feature vectors by performing feature extraction on the subtracted in-vivo spectrum (see Example 1; where the Net Analyte Signal Calibration Model for glucose incorporates principal component analysis which extracts baseline factors from in-vivo spectra in Col 13 Lines 15-23).
Regarding claim 6, Arnold as modified teaches:
The electronic device of claim 5, wherein the feature vectors of the subtracted in-vivo spectrum comprise main components obtained by performing principal component analysis (PCA) on the subtracted in-vivo spectrum (see Example 1; where the Net Analyte Signal Calibration Model for glucose incorporates principal component analysis in Col 13 Lines 15-23).
Regarding claim 7, Arnold as modified teaches:
The electronic device of claim 1, wherein the one or more processors are configured to determine the accuracy of the analyte concentration by comparing the test concentration for the analyte with a reference value (predicted glucose concentration is compared with arterial blood measurements in Col. 14 Lines 13-20).
Regarding claim 11, Arnold as modified teaches:
The electronic device of claim 1, wherein the one or more processors are configured to determine the accuracy of the analyte concentration by comparing the test concentration for the analyte with the analyte concentration (predicted glucose concentration is compared with arterial blood measurements in Col. 14 Lines 13-20).
Regarding claim 12, Arnold as modified teaches:
The electronic device of claim 1, wherein the calibration model is at least one of a partial least squares (PLS) calibration model, a net analyte signal (NAS) calibration model, and a calibration model based on deep learning (Example 1 is an NAS calibration model in Col. 13 and Examples 3-4 are PLS calibration models in Col. 14).
Regarding claim 13, Arnold as modified teaches:
The electronic device of claim 1, wherein the test calibration model is a NAS calibration model (Example 1 is an NAS calibration model in Col. 13).
Regarding claim 14, Arnold as modified teaches:
The electronic device of claim 1, wherein the analyte is at least one of glucose, urea, lactic acid, triglyceride, protein, cholesterol, and ethanol (all analytes are explicitly listed in a comprehensive list in Col. 8 Lines 49-61).
Regarding claim 15, Arnold as modified teaches:
The electronic device of claim 1, wherein the in-vivo spectrum is an in- vivo spectrum of a test subject generated based on absorption spectroscopy (explicitly recited in the pipeline throughout; see Col. 7 Lines 18-24 for example).
Regarding claim 16, Arnold as modified teaches:
The electronic device of claim 1, wherein the in-vivo spectrum is an in- vivo spectrum of a test subject for near-infrared rays (explicitly disclosed as NIR including 4000-5000 and 5500-6500 cm-1; Col. 5 Lines 8-33).
Regarding claim 17, Arnold as modified teaches:
The electronic device of claim 1, wherein the one or more processors are configured to determine, in a non-invasive manner, the accuracy of the analyte concentration calculated using the calibration model (see Abstract; invention directed to measuring the concentration of an analyte in a test subject and evaluating the analytical significance using a calibration model; method is also in-vivo meaning non-invasive).
Regarding claim 18, Arnold and Lorenz teach:
A method of determining accuracy of an analyte concentration non- invasively, the method comprising:
calculating the analyte concentration from an in-vivo spectrum obtained non- invasively by using a calibration model;
subtracting a spectrum specific to an analyte from the in-vivo spectrum;
generating a test calibration model based on feature vectors of the subtracted in-vivo spectrum obtained non-invasively;
calculating a test concentration for the analyte from the in-vivo spectrum by using the test calibration model; and
analyzing the test concentration for the analyte using the test calibration model to determine accuracy of the analyte concentration non-invasively (see rejection on claim 1).
Regarding claim 19, Arnold as modified teaches:
The method of claim 18, wherein the determining of the accuracy of the analyte concentration comprises comparing the test concentration of the analyte with a reference value to determine the accuracy of the analyte concentration (predicted glucose concentration is compared with arterial blood measurements in Col. 14 Lines 13-20).
Regarding claim 20, Arnold as modified teaches:
A computer-readable recording medium having recorded thereon a program for executing the method of claim 19 on a computer (computer readable storage medium explicitly recited in Col 11, Lines 10-17).
Claim(s) 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Arnold et al. (US7460895B2) in view of Lorenz (US6697654B2), as applied to claim 1, further in view of Wesley et al. (US20140206970)
Regarding claim 8,
Although Arnold does teach a method for developing the calibration model by incorporating the standard error, he does not explicitly teach the electronic device of claim 1, wherein the one or more processors are configured to determine the accuracy of the analyte concentration by comparing, with a reference value, a standard deviation of the test concentration for the analyte. However, Wesley teaches statistical methods for the quantification of glucose measurements including the standard deviation (see [0056]-[0057] for standard deviation formula with a given reference range of 10-25mg/mL). Therefore, it would have been obvious for one of ordinary skill in the art to substitute Arnold’s standard error with Wesley’s standard deviation computation in order to provide substitute one known element for another to obtain the predictable result of glucose concentration quantification (see “Abstract” for the evaluation of glucose data; [0056] explicitly states glucose concentration). This substitution would have been accomplished with reasonable expectation of success as Arnold already includes the incorporation of various statistical measures to evaluate the glucose prediction model such as R, SEP, and MARD as disclosed by Arnold (see Example 3 for discussion of the standard error of prediction used in the PLS Multivariate Calibration Model in Col. 14 Lines 32-44).
Regarding claim 9, Arnold as modified by Wesley teaches:
The electronic device of claim 1, wherein the one or more processors are configured to determine the accuracy of the analyte concentration by comparing, with a reference value, a standard deviation of the test concentration calculated for a predetermined interval (Wesley: see [0050] where glucose statistics are calculated based on a given period of time such as 24-hours with a given reference range of 10-25 mg/mL).
Regarding claim 10, Arnold as modified by Wesley teaches:
The electronic device of claim 1, wherein the one or more processors are configured to determine that the accuracy of the analyte concentration is high when a standard deviation of the test concentration calculated for a 10-minute interval is less than 50 mg/dl (Wesley: explicitly mentioned that measurement intervals can also include a 10-minute interval in [0062]). Although Wesley does not explicitly teach that the threshold of the standard deviation is 50mg/mL, he does teach the use of numerical thresholds to classify glucose data including a 50mg/mL threshold (see [0060]) and separately teaches the calculation of standard deviation and evaluation over defined measurement intervals. Applying this established thresholding technique to the standard deviation accuracy metric computed would have been a routine optimization to one of ordinary skill in the art as Wesley already teaches that the standard deviation is compared against a reference range (see [0057]).
Conclusion
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/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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