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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 7, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Objections
Claim 16 is objected to because of the following informality:
“each of the differential cell” should read “each of the differential cells” (lines 3-4).
Appropriate correction is required.
Claim 26 is objected to because of the following informality:
“comes from” should read “come from” (line 2).
Appropriate correction is required.
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 is: “differential cell” in claims 1, 10-11, 14, 16, and 28-29.
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 § 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-36 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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “differential cell” in claims 1, 10-11, 14, 16, and 28-29 is used by the claim to be an element capable of calculating predicted blood glucose, while the accepted meaning is a specialized functional unit of an organism. The term is indefinite because the specification does not clearly redefine the term.
Claim 1 recites the limitation "the time of the second round of data collection" in line 11. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites “the blood glucose levels corresponding to each of the two inputs” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. It is unclear if “the blood glucose levels” is referring to the reference blood glucose level and predicted blood glucose level, or if there are different blood glucose levels corresponding to each of the two inputs. Further clarification is required.
Claim 11 recites “the blood glucose levels corresponding to each of the two inputs” in line 4. There is insufficient antecedent basis for this limitation in the claim. It is unclear if “the blood glucose levels” is referring to the reference blood glucose level and predicted blood glucose level, or if there are different blood glucose levels corresponding to each of the two inputs. Further clarification is required.
Claim 14 recites “its” in line 3. The term “its” renders the claim indefinite because it is unclear what the term “its” is referencing. Further clarification is required.
Claim 14 recites “the first historical round of data collection” in lines 6-7, and “the second historical round of data collection” in lines 8-9. There is insufficient antecedent basis for these limitations in the claim.
Claim 16 recites the limitation “the sum of the absolute value” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation “the scattered location” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites “the union”, “the weight” and “the number of points”. There is insufficient antecedent basis for these limitations in the claim.
Claim 23 recites “the repeatedly trained N rounds” and “the maximum and minimum outcomes”. There is insufficient antecedent basis for these limitations in the claim.
Claim 30 recites the limitation “the sum of the absolute values” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 32 recites “the union”, “the weight” and “the number of points”. There is insufficient antecedent basis for these limitations in the claim.
Claim 34 recites “the repeatedly trained N rounds” and “the maximum and minimum outcomes”. There is insufficient antecedent basis for these limitations in the claim.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed model can be interpreted as software, which is an intangible product that does not fall within a statutory category per MPEP §2106.03.
Claims 1-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-36 are directed to a non-invasive blood glucose prediction model using a computational algorithm, which is an abstract idea. Claims 1-36 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process.
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] – “A non-invasive blood glucose prediction model that predicts blood glucose level of a subject based on deduction learning comprising a differential cell, a first input, a second input, a reference blood glucose level input and a predicted blood glucose level output”;
[B1] – “the differential cell is configured to calculate the predicted blood glucose level of the subject at the time of the second round of data collection based upon the first input, the second input, the reference blood glucose level and correlation between differences between the two inputs and differences between the reference blood glucose level input and the predicted blood glucose level”; and
[C2] – “the correlation is learned by the model using deduction learning”.
These elements [A1]-[C1] of claim 1 are drawn to an abstract idea since they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations, and they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] – “wherein the first input is based upon a first cardiovascular data collected from the subject at a first round of data collection”;
[B2] – “the second input is based upon a second cardiovascular data collected from the subject at a second round of data collection”;
[C2] – “the reference blood glucose level input is collected from the subject at the first round of data collection”.
These elements [A2]-[C2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the elements [A2-C2] are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “data collected from the subject” does not qualify as significantly more because this limitation merely describes data collection and does not incorporate any type of sensor or particular machine as part of the claimed invention.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 2-36 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 6: “conventional finger prick method”.
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claim 6 is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the finger prick method is conventional, as evidenced by the claim language “conventional” finger prick method.
Also, this limitation from claim 6 is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions (that is, one of display) that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(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.
Claims 1-8, 13, 17, 22, 25, and 27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20210259640 A1 (Newberry et al.).
Regarding claim 1, Newberry teaches a non-invasive blood glucose prediction model that predicts blood glucose level of a subject ([0023] “non-invasive health monitoring, and in particular, a system and method for detection of glucose levels in blood flow using an optical sensor.”) based on deduction learning comprising a differential cell ([0191] “machine learning algorithm”), a first input ([0032] “a first PPG signal”), a second input ([0032] “a second PPG signal”), a reference blood glucose level input ([0285] “reference glucose”; [0288]) and a predicted blood glucose level output ([0287] “predicted glucose value”) wherein the first input is based upon a first cardiovascular data collected from the subject at a first round of data collection ([0031] “determining one or more PPG parameters using the plurality of PPG signals and determining a health index using the one or more PPG parameters, wherein the health index indicates a vascular health of the user.”; [0033] “a first PPG signal of the plurality of PPG signals”; [0145] “for one or more wavelengths, the systolic points 402 and diastolic points 404 in the spectral response are determined.”);
the second input is based upon a second cardiovascular data collected from the subject at a second round of data collection (Fig. 3; [0034] “the second PPG signal obtained during the insulin release event.”; [0139] “The first spectral response (or first PPG signal) of the light over the first range of wavelengths including the first predetermined wavelength and the second spectral response (or second PPG signal) of the light over the second range of wavelengths including the second predetermined wavelengths is then generated at 302 and 304.”; [0145] “the spectral response obtained at each wavelength may be aligned based on the systolic 402 and diastolic 404 points in their respective spectral responses.”);
the reference blood glucose level input is collected from the subject at the first round of data collection ([0040] “the first PPG signal is obtained using a wavelength of light in a range of 380 nm-410 nm”; [0285] “a reference glucose was tested at discrete points using a blood test”; [0289] “The reference glucose at one or more discrete points is compared to the R.sub.395nm/940nm values at the same discrete points, and the individual calibration is obtained.”);
the differential cell is configured to calculate the predicted blood glucose level of the subject at the time of the second round of data collection based upon the first input, the second input, the reference blood glucose level and correlation between differences between the two inputs and differences between the reference blood glucose level input and the predicted blood glucose level ([0032] “the one or more processing devices are configured to determine the one or more PPG parameters by determining at least one of the following PPG parameters: a phase delay between a first PPG signal and a second PPG signal of the plurality of PPG signals, a correlation of phase shape between the first PPG signal and the second PPG signal of the plurality of PPG signals or a periodicity of first PPG signal or the second PPG signal of the plurality of PPG signals.”; [0110] “The plurality of parameters are then analyzed to determine a glucose level in blood flow of the use”; [0196] “The input vector is processed by a processing device executing a neural network (aka machine learning algorithm). The processing device executes the machine learning algorithm or neural network techniques using the input vector to determine health data at 1310. The health data includes one or more of heart rate, period of vasodilation, level of vasodilation, respiration rate, blood pressure, oxygen saturation level, NO level, liver enzyme level, Glucose level, Blood alcohol level, blood type, sepsis risk factor, infection risk factor, cancer, virus detection, creatinine level or electrolyte level.”; [0242]; Fig. 19; [0269] “a cross correlation function may determine a phase offset between the PPG signals and/or pulse shape correlations during the insulin release event.”; [0285]; [0287-0289] “the individual calibration directly correlates the R values to the predicted glucose level for patients with vascular dysfunction. The reference glucose at one or more discrete points is compared to the R.sub.395nm/940nm values at the same discrete points, and the individual calibration is obtained.”); and
the correlation is learned by the model using deduction learning ([0261] “a correlation between the PPG waveform at 940 nm and the PPG waveform at 395 nm is illustrated as Phase Delay 2414 and Pulse Shape Correlation 2416. The PPG signals are processed using a cross correlation function or a Hilbert transformation or another algorithm that determines similarities in pulse shape and temporal relationship between PPG signals. For example, the time delay between the two signals can also be calculated at each time instant from the phase shift of their wavelet transforms.”; [0370-0371] “one or more types of artificial intelligence or neural network processing models may be implemented by the processing device 4300 to determine health data from one or more of the parameters. For example, the processing device 4300 may implement a regression model or classifier type model. A regression module neural network may be trained using one or more learning vectors with similar types of input parameters and known outputs as described further hereinabove. A classifier neural network may be applied to the one or more parameters to determine a glucose level or other health data. The glucose level may be expressed as within one or more ranges, such as normal, above normal, below normal, etc. (Region 1, 2, 3, 4 of glucose range—normal, below or above).
Regarding claim 2, Newberry teaches the model of claim 1, wherein the first cardiovascular data comprises a first photoplethysmography (PPG) signal collected from the subject at the first round of data collection and the second cardiovascular data comprises a second PPG signal collected from the subject at the second round of data collection ([0034] “the one or more processing devices are further configured to determine the one or more PPG parameters using the first PPG signal and the second PPG signal obtained during the insulin release event.”).
Regarding claim 3, Newberry teaches the model of claim 1, wherein the first cardiovascular data, second cardiovascular data and reference blood glucose level are collected from the subject in a fasting state ([0082] “FIG. 36 illustrates a schematic diagram of graphs of PPG signals during periods of ingestion and fasting.”; [0283]; [0315]).
Regarding claim 4, Newberry teaches the model of claim 1, wherein the first input comprises a first set of extracted features extracted from the first cardiovascular signal and the second input comprises a second set of extracted features extracted from the second cardiovascular signal ([0194] “Other parameters may be extracted by representing the PPG signal as a stochastic auto-regressive moving average (ARMA). Parameters also may be extracted by modeling the energy of the PPG signal using the Teager-Kaiser operator, calculating the heart rate and cardiac synchrony of the PPG signal, and determining the zero crossings of the PPG signal. These and other parameters may be obtained using a PPG signal. The PPG input data may include the PPG signals, and/or one or more parameters derived from the PPG signals”).
Regarding claim 5, Newberry teaches the model of claim 4, wherein the extracted features comprises heart rate, area under the curve of the waveform, full width, width at 25% of maximum peak amplitude, width at 50% of maximum peak amplitude and/or width at 75% of maximum peak amplitude ([0176] “A heart rate may be determined from the spectral response.”; [0281] “an area under the R value curve is determined during the insulin release event.”).
Regarding claim 6, Newberry teaches the model of claim 1, wherein the reference blood glucose level is obtained using conventional finger prick method ([0396] “For example, a person may input a glucose measurement obtained using a blood test with a strip meter.”; [0393] “glucose levels may be obtained in a clinical setting using a known standard blood test”).
Regarding claim 7, Newberry teaches the model of claim 1, wherein the first input comprises one or more signal windows wherein each signal window comprises a digitized segment of the first cardiovascular signal ([0119] “The A/D circuits convert the spectral responses to digital spectral data for processing by a DSP or other processing circuit.”; [0148]); and
wherein the second input comprises one or more signal windows wherein each signal window comprises a digitized segment of the second cardiovascular signal ([0119]; [0148] “The spectral responses may be measured over a predetermined period (such as 300 usec.) or at least over 2-3 cardiac cycles. This measurement process is repeated continuously, e.g., pulsing the light at 10-100 Hz and obtaining spectral responses over a desired measurement period, e.g. from 1-2 seconds to 1-2 minutes or from 2-3 hours to continuously over days or weeks. The spectral data obtained by the PPG circuit 110, such as the digital or analog spectral responses, may be processed locally by the biosensor 100 or transmitted to a central control module for processing”).
Regarding claim 8, Newberry teaches the model of claim 7, wherein each segment comprises PPG signal lasting for about 10 second to about 5 minutes such as about 10 seconds, about 30 seconds, about 1 minute, about 1.5 minutes, about 2 minutes, about 2.5 minutes, about 3 minutes, about 3.5 minutes about 4 minutes about 4.5 minutes or about 5 minutes ([0148] “The spectral responses may be measured over a predetermined period (such as 300 usec.) or at least over 2-3 cardiac cycles. This measurement process is repeated continuously, e.g., pulsing the light at 10-100 Hz and obtaining spectral responses over a desired measurement period, e.g. from 1-2 seconds to 1-2 minutes or from 2-3 hours to continuously over days or weeks.”).
Regarding claim 13, Newberry teaches the model of claim 1, wherein the model is configured to learn the correlation between differences in PPG signal between the first and the second rounds of data collection and differences in blood glucose levels between the first and the second rounds of data collection using deduction learning ([0207] “The changes in PPG signals due to the changing propagation properties is reflected in a transfer function generated from the PPG signals, e.g. time differences and wave shape differences between PPG signals”; [0220]; [0229]; [0394]).
Regarding claim 17, Newberry teaches the model of claim 1, further comprises a screening module configured to improve accuracy of the model ([0348] “The health screening may include a test affected by an insulin release event, such as one or more of an ECG, EKG, EMG, stethoscopic exam, blood pressure measurement, heart rate measurement, MRI, CATSCAN, echocardiogram, etc. During all or part of the health screening, the heart and/or other vascular tissue is monitored for an insulin release event at 4302 … The indication may include an audible and/or visual alert. The health provider performing the health screening is then aware that the testing may not be accurate due to the insulin release event. The health provider may choose to reperform the health screening or ignore the test results for the period during the insulin release event.”).
Regarding claim 22, Newberry teaches the model of claim 17 wherein the screening module further comprises an outlier screening module configured to identify predicted blood glucose level outliers ([0348] “An insulin release event is identified at 4306, and an indication of the insulin release event is generated at 4308. The indication may include an audible and/or visual alert. The health provider performing the health screening is then aware that the testing may not be accurate due to the insulin release event. The health provider may choose to reperform the health screening or ignore the test results for the period during the insulin release event.”).
Regarding claim 25, Newberry teaches the model of claim 17, wherein the outlier screening module is applied only after the model quality screen module has been applied ([0349] “the indication of the insulin release event may alternatively or additionally be generated as part of the testing results, e.g. an indication that the results may not be accurate due to the insulin release event or an indication of the period of the insulin release event on the test results.”).
Regarding claim 27, Newberry teaches a method of generating a predicted blood glucose for a subject using the model of claim 1 comprising the steps of:
collecting the first and the second cardiovascular data as well as the reference blood glucose level from the subject ([0034] “determine the one or more PPG parameters using the first PPG signal and the second PPG signal obtained during the insulin release event.”; [0285] “The R value approximately tracked the trend in the reference glucose”);
creating the first and second input based upon the first and second cardiovascular data ([0388] “determining a concentration of glucose in blood flow using a plurality of parameters in more detail. At 4802, a first PPG signal is obtained at a first wavelength with a high absorption coefficient for NO in a range of 380-410 nm, preferably 390-395 nm. A second PPG signal is obtained at a second wavelength with a low absorption coefficient for NO, such as equal to or above 660 nm and preferably 940 nm.”);
inputting the first and second inputs into the model ([0370]; [0392] “The AI processing device executes a machine learning algorithm with the parameters as inputs and determines the concentration level of glucose.”; [0393-0394]);
inputting the reference blood glucose level into the model ([0393] “The training set preferably includes the same input parameters and known values of the glucose levels. For example, glucose levels may be obtained in a clinical setting using a known standard blood test. The PPG signals and parameters are also obtained. This training set is provided to a neural network training algorithm to generate the learning vectors.”); and
generating the predicted blood glucose level using the model based on the correlation between differences between the two inputs and differences between the reference blood glucose level input and the predicted blood glucose level ([0110] “The PPG parameters include R values, L values, phase delay between two or more of the plurality of PPG signals, a correlation of phase shape between two or more of the plurality of PPG signals or a periodicity of one or more the plurality of PPG signals. Other types of parameters, such as skin temperature, may also be obtained by the biosensor. The plurality of parameters are then analyzed to determine a glucose level in blood flow of the user.”; [0288] “A difference or other correlation between the interim glucose value and the reference glucose is determined at one or more points of time. The difference or other correlation is used as an individual calibration to adjust the interim glucose value to the predicted glucose levels shown in Graphs 3106 and 3108. Thus, for patients with vascular dysfunction, an individual calibration is used to obtain the predicted glucose levels from the R values”).
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 9-12 and 14 are rejected under 35 U.S.C. 102 as being unpatentable over US 20210259640 A1 (Newberry et al.) in view of US 20200375549 A1 (Wexler et al.).
Regarding claim 9, Newberry teaches the model of claim 1.
Newberry does not teach wherein the first round of data collection and the second round of data collection are separated in time by about 1 day, about 2 days, about 5 days about 10 days, about 20 days, about 30 days, about 1.5 months, about 2 months, about 3 months, about 4, months, about 5 months or about 6 months.
However,
Wexler teaches wherein the first round of data collection and the second round of data collection are separated in time by about 1 day, about 2 days, about 5 days about 10 days, about 20 days, about 30 days, about 1.5 months, about 2 months, about 3 months, about 4, months, about 5 months or about 6 months ([0054] “the blood glucose data may be CGM data including a plurality of blood glucose measurements taken at a relatively particular time interval (e.g., once every 5-10 minutes). The blood glucose measurements can be taken over any suitable time period, e.g., over 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 12 hours, 24 hours, 36 hours, or 48 hours, or longer.”; [0063]; [0087] “the patient-specific model 410 is updated at a higher frequency (e.g., once per day), the population model 420 is updated at a high or intermediate frequency (e.g., once per day, once per week), and the aggregate model 430 is updated at a lower frequency (e.g., once per month, once per quarter)”; [0088-0089] “the first prediction 414 can be a first time series of blood glucose values (e.g., every 5 minutes for the next 1-2 hours) and the second prediction 424 can be a second time series of blood glucose values (e.g., every 5 minutes for the next 1-2 hours)”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the model taught by Newberry to include a time difference between the first and second rounds of data collection. One would have been motivated to make this modification because predicting blood glucose at a specific time can take into account average glucose and standard deviation at that time of day from previous blood glucose values, which can accurately predict glucose concentration in the blood of an individual at a present time and/or in the future, as suggested by Wexler [0014, 0063].
Regarding claim 10, Newberry teaches the model of claim 1.
Newberry does not explicitly teach wherein the differential cell comprises a deep neural network configured to learn the correlation between differences between the first input and the second input and differences in the blood glucose levels corresponding to each of the two inputs.
However,
Wexler teaches wherein the differential cell comprises a deep neural network configured to learn the correlation between differences between the first input and the second input and differences in the blood glucose levels corresponding to each of the two inputs ([0014] “The blood glucose data can be correlated with at least one event (e.g., insulin intake, meal intake, physical activity, etc.). The method can include generating at least one initial prediction of the blood glucose state by inputting the blood glucose data into a first set of machine learning models. The method can also include determining a plurality of features from the at least one initial prediction, and optionally from other patient data (e.g., the blood glucose data, previous blood glucose data, personal data, etc.). The method can further include generating a final prediction of the blood glucose state by inputting the plurality of features into a second set of machine learning models”; [0049] “the system 102 is configured to forecast the patient's blood glucose state using one or more machine learning models … deep learning algorithms (e.g., convolutional neural networks, recurrent neural networks, long short-term memory networks, stacked auto-encoders, deep Boltzmann machines, deep belief networks)”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the model taught by Newberry to include a deep neural network. One would have been motivated to make this modification because deep learning algorithms are suitable for use in forecasting techniques, as suggested by Wexler [0049].
Regarding claim 11, Newberry teaches the model of claim 1.
Newberry does not explicitly teach wherein the differential cell comprises a convolution neural network comprising one or more convolution layers configured to learn the correlation between differences in the first input and the second input and differences in the blood glucose levels corresponding to each of the two inputs.
However,
Wexler teaches wherein the differential cell comprises a convolution neural network comprising one or more convolution layers configured to learn the correlation between differences in the first input and the second input and differences in the blood glucose levels corresponding to each of the two inputs ([0014] “The blood glucose data can be correlated with at least one event (e.g., insulin intake, meal intake, physical activity, etc.). The method can include generating at least one initial prediction of the blood glucose state by inputting the blood glucose data into a first set of machine learning models. The method can also include determining a plurality of features from the at least one initial prediction, and optionally from other patient data (e.g., the blood glucose data, previous blood glucose data, personal data, etc.). The method can further include generating a final prediction of the blood glucose state by inputting the plurality of features into a second set of machine learning models”; [0049] “convolutional neural networks”; Convolutional neural networks are made of at least one convolution layer.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the model taught by Newberry to include a convolutional neural network. One would have been motivated to make this modification because convolutional neural networks are suitable for use in forecasting techniques, as suggested by Wexler [0049].
Regarding claim 12, Newberry teaches the model of claim 11.
Newberry does not explicitly teach wherein the convolution layer comprises one or more 1D convolution layer, batch normalization module, activation module and/or maxpooling module.
However,
Wexler teaches wherein the convolution layer comprises one or more 1D convolution layer, batch normalization module, activation module and/or maxpooling module ([0049] “convolutional neural networks”; The blood glucose data is one-dimensional, so the CNN disclosed by Wexler for processing this data would use a 1D convolution layer as opposed to 2D convolution layers that are used for image data.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the model taught by Newberry to include a convolutional neural network. One would have been motivated to make this modification because convolutional neural networks are suitable for use in forecasting techniques, as suggested by Wexler [0049].
Regarding claim 14, Newberry teaches the model of claim 1.
Newberry does not explicitly teach wherein the model is configured to learn the correlation by deduction learning using a plurality of parallel differential cells, each differential cell with its pair of first historical input and second historical input as well as a pair of first historical reference blood glucose level and a second historical reference blood glucose level wherein the first historical input and the first historical reference blood glucose level are collected from the subject at the first historical round of data collection and the second historical input and the second historical reference blood glucose level are collected from the subject at the second historical round of data collection.
However,
Wexler teaches wherein the model is configured to learn the correlation by deduction learning using a plurality of parallel differential cells, each differential cell with its pair of first historical input and second historical input as well as a pair of first historical reference blood glucose level and a second historical reference blood glucose level wherein the first historical input and the first historical reference blood glucose level are collected from the subject at the first historical round of data collection and the second historical input and the second historical reference blood glucose level are collected from the subject at the second historical round of data collection ([0021]; [0064] “at least one initial prediction is generated using a first set of machine learning models. Specifically, the input data (e.g., an augmented episode) is input into the first set of machine learning models, and the first set of machine learning models use the input data to generate the initial prediction(s)”; [0065] “In embodiments where the first set of machine learning models includes multiple machine learning models, some or all of the models can be trained on the same training data, or some or all of the models can be trained on different training data. The training data can include, for example, previous data from the same patient, such as previous blood glucose data (e.g., episodes prior to the current episode), previous insulin intake data, previous food intake data, previous physical activity data, personal data, physiological data, and/or any other type of data described herein.”; [0070] “the features determined at step 330 are input into the second set of machine learning models, which generates the final prediction. In some embodiments, the features from step 330 are the only input into the second set of machine learning models. In other embodiments, the second set of machine learning models can also receive other inputs, such as the input data of step 310 (e.g., one or more augmented episodes), the initial prediction(s) generated in step 320, and/or other data of the patient (e.g., personal data, previous blood glucose data, meal data, medical history data, exercise data, personal data, medication data, physiological data, etc.).”; [0141] “Each prediction was based not only on past observations from the user being predicted, but also on all observations in the training set data collected prior to the point in historical time from which the forecast was calculated.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the model taught by Newberry to include parallel algorithms using pairs of historical data. One would have been motivated to make this modification because using a first and second set of machine learning models can rapidly and accurately predict a patient’s future blood glucose using historical blood glucose data even where data is limited, irregular, and/or incomplete, as suggested by Wexler [0014, 0021].
Conclusion
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/EVELYN GRACE PARK/Examiner, Art Unit 3791
/TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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