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 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 1-3 and 5-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
STEP 1
Regarding claim 12, the claim recites a series of steps or acts, including re-guiding remeasurement based on a correlation. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of re-guiding remeasurement based on a correlation sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion) and by a human using using a pen and paper. Thus, the claim is drawn to a Mathematical Concept, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. The re-measurement does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the remeasurement, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of measuring impedance of a user, obtaining metadata, using an ANN, outputting physiological data, and calibrating the physiological data. Measuring impedance, obtaining metadata, and calibrating the obtained data of a user is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the measuring, obtaining, and calibrating steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited impedance sensor is a generic sensor configured to perform pre-solutional data gathering activity and the computer system is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application. Same rationale applies to claim 18.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data processing. The measuring, obtaining, and calibrating steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 6-7, 12-13, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kendall et al. (US 20250025078- Previously cited), hereinafter Kendall and further in view of Simpson et al. (US 20220384007), hereinafter Simpson, and Umekawa et al. (US 20230071410), hereinafter Umekawa.
Regarding claims 1 and 18, Kendall teaches, a memory storing one or more instructions and a processor configured to execute the one or more instructions(see para. [0458], processing device and memory);
an impedance sensor configured to measure a bio-impedance of a user (see para. [0220-225]), and comprising at least a pair of electrodes (abstract); and
a processor configured to measure, by using a multiple-output artificial neural network (ANN) learning model (see para. [0436]), a concentration of a basic blood component and a concentration of at least one auxiliary blood component (see para. [0361,0406], a plurality of analytes such as amino acids (basic component) and creatinine (auxiliary) can be measured), based on user metadata and the measured bio-impedance (see para. [0556,0560], the monitoring process begins by acquiring subject attributes, which will be used in calculating one or more metrics);
output the concentration of the basic blood component and the concentration of the at least one auxiliary blood component associated with the basic blood component (see para. [0441], “The output could additionally and/or alternatively, include an indication of an indicator, such as a measured value, or information derived from an indicator. Thus, a hydration level or analyte level or concentration could be presented to the user”).
Kendall fails to teach calibrating the concentration of the basic blood component based on the concentration of the at least one auxiliary blood component.
Simpson teaches an analyte monitoring system in the field of diabetes (abstract). The system is configured to calibrate a blood analyte concentration based on the concentration of a different blood analyte concentration (see para. [0063,0569], “ first sensor device may be configured to measure glucose and the second sensor device may be configured to measure an analyte selected from the group consisting of: glucagon, insulin, other hormones involved in metabolic processes, glycogen, starch, free fatty acids, triglycerides, monoglycerides, troponin, cholesterol” and “calibration parameters of an analyte that is relatively easy to calibrate are determined and then used to estimate the calibration parameters of an analyte that is more difficult to calibrate.”) In other words, calibration of a blood analyte concentration based on the calibration of a different analyte concentration is performed to aid in determining the concentration of a “difficult” analyte without direct measurement.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Kendall, to calibrate the concentration of the basic blood component based on the concentration of the at least one auxiliary blood component, as taught by Simpson, to aid in determining the concentration of an analyte without direct measurement.
Kendall-Simpson fails to teach when outputting the concentration of the basic blood component and the concentration of the at least one auxiliary blood component, guide remeasurement based on a correlation, between the basic blood component and the at least one auxiliary blood component, falling outside a correlation range.
Umekawa teaches a biological measurement device configured to measure analyte concentration in blood (abstract and para. [0347]). To determine the analyte concentration, a correlation between two signals that are correlated to blood components, is determined to be high or low (see para. [0273,0296,0347]). When the determination is high, the analyte concentration is considered inappropriate, and remeasurement is recommended (see para. [0296], range is 0.1 to 1.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Kendall-Simpson, such that when outputting the concentration of the basic blood component and the concentration of the at least one auxiliary blood component, guide remeasurement based on a correlation, between the basic blood component and the at least one auxiliary blood component, falling outside a correlation range, as taught by Umekawa, because the measured analyte concentrations are inappropriate and remeasurement leads to improved data reliability (see para. [0347] of Umekawa).
Regarding claims 2, 13 and 19, Kendall teaches wherein the multiple-output ANN learning model is pre-trained to output the concentration of the basic blood component and the concentration of the at least one auxiliary blood component (see para. [0435], “In this instance, the computational model could be obtained by applying machine learning to reference metrics derived from subject data measured for one or more reference subjects having known health statuses. In this instance, the health status could be indicative of organ function, tissue function or cell function, could include the presence, absence, degree or severity of a medical condition, or could include one or more measures otherwise associated with a health status, such as measurements of the presence, absence, level or concentration of one or more analytes or measurements of other biomarkers.” (emphasis added) indicating that metrics obtained are applied to the trained model to determine an indicator based on the relationship between the training data, e.g., analyte concentration, etc., and the obtained measurement).
Regarding claims 6 and 12, Kendall teaches wherein the processor is further configured to obtain the user metadata via a user interface (see para. [0444,0458,0529,0534] and fig. 3A); and wherein the user metadata comprises age, gender, height, and weight (see para. [0556]).
Regarding claims 7 and 15, Kendall teach wherein the processor is further configured to, based on the measured concentration of the basic blood component and the measured concentration of the at least one auxiliary blood component, provide the user with health guidance, and wherein the health guidance comprises at least one of a warning (see para. [0441], “Thus, the monitoring device could be configured to generate an output including a notification or an alert” and “The output could additionally and/or alternatively, include an indication of an indicator, such as a measured value, or information derived from an indicator. Thus, a hydration level or analyte level or concentration could be presented to the user”).
Claims 3, 5, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kendall in view of Simpson and Umekawa, as applied to claims 2 and 13, further in view of Giacomello et al. (Relation between serum triglyceride level, serum urate concentration, and fractional urate excretion, Sep 1997- Previously cited), hereinafter Giacomello.
Regarding claims 3, 14 and 20, Kendall fails to explicitly teach wherein the processor further is configured to determine, as the at least one auxiliary blood component, at least one blood component that has a correlation with the basic blood component that is greater than or equal to a threshold value.
It is noted, Kendall teaches that different types of sensor substrates allows for different types of analyte measurements (see para. [0269]).
Giacomello teaches there are positive relationships between triglyceride, uric acid, and creatinine concentrations (see abstract and pg. 1086 ¶2, “serum triglyceride level was positively correlated with serum urate concentration” and “Serum creatinine and triglyceride concentrations had the strongest positive independent association with serum urate levels”; see tables 1 and 3-5).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Kendall-Simpson-Umekawa, such that it determines at least one blood component that has a correlation with the basic blood component that is greater than or equal to a threshold value, as taught by Giacomello, because Kendall teaches that different substrates are required for different analytes and by correlating a single analyte concentration to multiple analytes and streamlined approach to measuring analytes would be achieved. As such, the modification would also be applying a known technique (correlating analytes to each other) to a known device (analyte measurement system) ready for improvement to yield predictable results.
Regarding claim 5, Kendall-Umekawa-Giacomello fail to teach wherein the blood component analytes, correlated to with the basic blood component, measured by the impedance sensor comprises triglyceride, and the at least one auxiliary blood component comprises uric acid. Kendall does teach that the auxiliary blood component can be creatinine (see para. [0361]).
Simpson teaches that the analytes that can be correlated to each other can be triglycerides, uric acid, and creatinine (see para. [0013,0154]).
Therefore, it would have been obvious to one of ordinary skill in the art at the invention was effectively filed to have modified the device of Kendall-Simpson-Umekawa-Giacomello, such that wherein the blood component analytes measured by the impedance sensor comprises triglyceride, and the at least one auxiliary blood component comprises uric acid, as taught by Simpson, as it would merely be combining prior art elements (analyte detection systems) according to known methods (measuring triglycerides and uric acid) to yield predictable results.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kendall in view of Simpson and Umekawa, as applied to claim 1, further in view of Segal et al. (US 20230076246- Previously cited), hereinafter Segal.
Regarding claim 8¸ Kendall-Simpson-Umekawa teach wherein the multiple-output ANN learning model comprises an input layer, a plurality of hidden layers, and an output layer (see para. [0433-436] of Kendall, one of ordinary skill in the art understands that ANN models are comprised of an input layer, plurality of hidden layers, and an output layer), but fails to teach wherein the plurality of hidden layers comprise at least one of a linear function, a batch normalization function, a rectified linear unit function, and a dropout function.
Segal teaches a system for monitoring analyte by using an ANN model that comprises and input layer, rectified linear unit/hidden layers, and output layer (see abstract and para. [0058-59]). As such, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Kendall-Simpson-Umekawa, such that the plurality of hidden layers comprise a rectified linear unit function, as taught by Segal, because Kendall requires an ANN model, but fails to provide details, and Segal teaches that a rectified linear unit function can be used in an ANN model.
Claims 9 and 16 rejected under 35 U.S.C. 103 as being unpatentable over Kendall in view of Simpson and Umekawa, as applied to claims 1 and 12, further in view of Burwinkle et al. (US 20220248970- Previously cited), hereinafter Burwinkle.
Regarding claims 9 and 16, Kendall-Simpson-Umekawa fail to teach wherein the processor is further configured to perform preprocessing on the measured bio-impedance and the user metadata by standard scaling.
Burwinkle teaches a monitoring systems that is configured to measure impedance and receive user metadata, e.g., age, sex, etc., and compare the data via statistical metrics, such as normalized Z-score (standard scaling) to aid in determining an overall condition state of the subject over the monitoring period (see para. [0033,0035,0037,0042] and abstract).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Kendall-Simpson-Umekawa, such that the processor is further configured to perform preprocessing on the measured bio-impedance and the user metadata by standard scaling, as taught by Burwinkle, to aid in determining an overall condition state of the subject over the monitoring period.
Claims 10-11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kendall in view of Simpson and Umekawa, as applied to claims 1 and 12, further in view of Lee et al. (US 20190167208- Previously cited), hereinafter Lee.
Regarding claims 10 and 17, Kendall-Simpson-Umekawa fail to teach wherein the processor is further configured to obtain an impedance index based on the measured bio-impedance and the user metadata, and input the impedance index into the multiple-output ANN learning model.
Lee teaches a bio-information processing device and method, that requires computing user height and dividing it by the impedance measurement (impedance index) to obtain a height value that aids in estimating a physiological parameter of the user, e.g., body water (see para. [0012-15,0056] and abstract), when input into the model. Lee further teaches that the estimation is then used to generate at least one of a prediction index of diseases, which include liver cirrhosis, intercapillary glomerulosclerosis, and edema, and prognosis evaluation information of a surgical operation field (see para. [0014]).
It would have been obvious to one ordinary skill in the art at the time the invention was effectively filed to have modified the device of Kendall-Simpson-Umekawa, such an impedance index is obtained based on the measured bio-impedance and the user metadata, and input the impedance index into a model, as taught by Lee, to aid in generating at least one of a prediction index of diseases.
Regarding claim 11, Lee teaches wherein impedance index comprises a value obtained by dividing the user metadata by the measured bio-impedance (see para. [0056]).
Response to Arguments
Applicant's arguments filed 10/31/2025 have been fully considered but they are not persuasive.
Applicant arguments with respect to 35 U.S.C. 112(a)-(b) are persuasive and the rejections have been withdrawn.
Applicant’s arguments with respect to 35 U.S.C. 102(a), 103 rejections of claims have been considered but in certain respects are moot because amendments require new grounds of rejection.
Applicant’s contends that the claims represent a patentable improvement to the accuracy of measuring basic blood components (blood analyte concentrations), on page 12 of the Remarks. The examiner disagrees. An improved mathematical calculation is still a mathematical calculation even if such a calculation results in more accurate results.1,2 Also, having the claims focus on determining the health
state of the human body is not itself limiting the claims to improving the technology because cases that involve practical, technological improvements
extend beyond simply improving the accuracy of a prediction.3 See, e.g., McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016). (“The claimed process uses a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results: a sequence of synchronized, animated characters.”); Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1304 (Fed. Cir. 2018) (finding patent eligible a
claim drawn to a behavior-based virus scan that protects against viruses that have been “cosmetically modified to avoid detection by code-matching virus scans”);
“[T]he improvement in computational accuracy alleged here does not qualify as an improvement to a technological
process; rather, it is merely an enhancement to the abstract mathematical calculation of haplotype phase itself...The
different use of a mathematical calculation, even one that yields different or better results, does not render patent
eligible subject matter.” In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245 (Fed. Cir.
2021).
2 “[A] claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp, 839 F.3d 1138
(Fec. Cir. 2016).
3 See In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245 (Fed. Cir. 2021).
Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1330, 1333 (Fed. Cir. 2016)
(discussing patent eligible claims directed to “an innovative logical model for a computer database” that included a self-referential table allowing for greater flexibility in configuring databases, faster searching, and more effective storage); CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed. Cir. 2020) (explaining that the claims at issue focus on a specific means for improving cardiac monitoring technology; they are not “directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery” (quoting McRO, 837 F.3d at 1314)).
Therefore, the improvement cannot integrate the abstract ideas into a practical application because the improvement is directed towards the abstract ideas.
Applicant contends that a mathematical concept is not recited in reference to USPTO example 38, on page 14 of the Remarks. However, in contrast to the present case, the example is distinct in the specificity of the details in the claims and clear application to a particular problem. In this case, the recited “basic blood component” and “auxiliary blood component” are highly general and are not applied to a specific field (treating a specific disease, e.g., diabetes,).
Applicant contends that the measurements cannot be performed with the mind, pen and pencil because it is not possible, on page 14 of the Remarks. The examiner disagrees. The argument appears to be directed towards highly general elements, e.g. basic blood components, auxiliary components, that merely represent values. However, these steps merely recite data input into a multioutput artificial neural network and provide an output. It is known by one of ordinary skill in the art at that these types of models can be performed with the mind, pen, and paper.1 The argument is therefore unpersuasive.
Applicant contends that Kendall does not disclose or suggest a “multiple-output artificial neural network,” on page 16 of the Remarks. The examiner disagrees. The deep learning model is configured to output multiple indicators from the response signals, which includes the measurement of a plurality of analytes, among others (para. [0232,0436,0523,0553,0561], “signal values and using these to calculate an indicator indicative of a health status, including the presence, absence, degree or prognosis of one or more medical conditions, a
prognosis associated with a medical condition, a presence, absence, level or
concentration of a biomarker, a presence, absence, level or concentration of an
1 Crisp KM, Sutter EN, Westerberg JA. Pencil-and-Paper Neural Networks: An Undergraduate Laboratory Exercise in Computational Neuroscience. J Undergrad Neurosci Educ. 2015 Oct 15
analyte, a presence, absence or grade of cancer, fluid levels in the subject, blood oxygenation, a tissue inflammation state, bioelectric activity, such as nerve, brain, muscle or heart activity or a range of other health states,” “ electronic processing devices could apply the metric to at least one computational model,” “The nature of the model and the training performed can be of . . . association rule
learning, artificial neural networks deep learning,” “analyse the subject data to generate one or more health status indicators,” “Additionally, and/or alternatively, the subject data could be analysed using a machine learning model or similar. One or more indicators are generated at step 1255, with the nature of the indicators”). Therefore, Kendall teaches and/or suggest that the artificial/machine learning model (ANN, etc.) can output a plurality of indicators (multiple-outputs), the indicators being “measurements of the presence, absence, level or concentration of one or more analytes or measurements of other biomarkers” (see para. [0435]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chan teaches that most patients' blood glucose data may have a high level of correlation with A1c based on any one of the numerous mathematical formulas or models that have been previously discovered or identified by others. US 20200121257
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARTIN NATHAN ORTEGA whose telephone number is (571)270-7801. The examiner can normally be reached M-F 7:10 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert (Tse) Chen can be reached at (571) 272-3672. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARTIN NATHAN ORTEGA/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791