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 guiding remeasurement based on a correlation sets forth a judicial exception. This step describes a mathematical concept that is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number. 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 a multi-output ANN learning model comprising multiple layers that output values based on learning a correlation between multiple variables, 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, 6-8, 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-previously cited), hereinafter Simpson, Umekawa et al. (US 20230071410- previously cited), hereinafter Umekawa, and Gutierrez-Osuna et al. (US 20200352481), hereinafter Gutierrez.
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(¶ [0458], processing device and memory);
an impedance sensor configured to measure a bio-impedance of a user (¶ [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 (¶ [0436]), a concentration of a basic blood component and a concentration of at least one auxiliary blood component (¶ [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 (¶ [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 (¶ [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 (¶ [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 ¶ [0347]). To determine the analyte concentration, a correlation between two signals that are correlated to blood components, is determined to be high or low (¶ [0273,0296,0347]). When the determination is high, the analyte concentration is considered inappropriate, and remeasurement is recommended (¶ [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 (¶ [0347] of Umekawa).
Kendall-Simpson-Umekawa fail to teach wherein the multiple-output ANN learning model comprises a shared layer and a task specific layer, wherein the multiple-output ANN learning model represents a simultaneous learning of the correlation between the basic blood component and the at least one auxiliary blood component, and wherein the multiple-output ANN learning model is configured to pass input values from the shared layer to ones of different functions of the task specific layer and to output, from the ones of the different functions of the task specific layer, respective ones of a plurality of output values according to the input values.
Gutierrez teaches a method for learning the shared impact of blood components, e.g., carbohydrates, proteins, fats, and estimating each concentration found in blood (¶[0035] and abstract). The method is based on a multi-output ANN learning model comprising a shared layer and a task layer that learns a correlation between the blood components and represents a simultaneous learning of the correlation between two blood components (¶[0035], “ shared layer 102 that learns information common to the three macronutrients (i.e., carbohydrates, proteins, fats) and a task-specific layer 104 that predicts the amount of each individual macronutrient”). That is, the model learns the correlation between the information common to the blood components to respective concentrations of the individual blood components (¶[0035]). The model is configured to pass input values (106) from the shared layer (102) to ones of different function of the task specific layer (104) and to output (108-112), from the ones of the different function of the task specific layer, respective ones of a plurality of output values according to the input values (¶[0035] and fig. 2, “Input 106 is communicated to shared layer,” “the task-specific layer to span the full range of macronutrient levels,” and “predicts the amount of each individual macronutrient”).
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 model of Kendall-Simpson-Umekawa, such that the model comprises a shared layer and a task specific layer, wherein the multiple-output ANN learning model represents a simultaneous learning of the correlation between the basic blood component and the at least one auxiliary blood component, and wherein the multiple-output ANN learning model is configured to pass input values from the shared layer to ones of different functions of the task specific layer and to output, from the ones of the different functions of the task specific layer, respective ones of a plurality of output values according to the input values, as taught by Gutierrez, because Kendall requires a multiple-output ANN learning model, but fails to provide details, and Gutierrez teaches the specific details of the structure of a model that measures blood components. Moreover, to aid in developing personalized behavioral interventions for obesity and other diseases (¶[0042] of Gutierrez).
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 (¶ [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 (¶ [0444,0458,0529,0534] and fig. 3A); and wherein the user metadata comprises age, gender, height, and weight (¶ [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 (¶ [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”).
Regarding claim 8¸ Kendall-Simpson-Umekawa-Gutierrez teach wherein the multiple-output ANN learning model comprises an input layer, a plurality of hidden layers, and an output layer (¶ [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. ¶[0035] and fig. 2 of Gutierrez), and wherein the plurality of hidden layers comprise at least a rectified linear unit function (¶[0035] of Gutierrez, the shared layer uses a ReLU function).
Claims 3, 5, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kendall in view of Simpson, Umekawa, and Gutierrez, 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-Simpson-Umekawa-Gutierrez 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 (¶ [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-Gutierrez, 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-Gutierrez-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 (¶ [0361]).
Simpson teaches that the analytes that can be correlated to each other can be triglycerides, uric acid, and creatinine (¶ [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-Gutierrez-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.
Claims 9 and 16 rejected under 35 U.S.C. 103 as being unpatentable over Kendall in view of Simpson, Umekawa, and Gutierrez, 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-Gutierrez 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 (¶ [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-Gutierrez, 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, Umekawa, and Gutierrez, 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-Gutierrez 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 (¶ [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 (¶ [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-Gutierrez, 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 (¶ [0056]).
Response to Arguments
Applicant's arguments filed 4/16/2026 have been fully considered but they are not persuasive.
Applicant contends that the claims are directed to an improvement to computer functionality and therefore should be found patent eligible under 35 U.S.C. 101, on pages 11-12 of the Remarks. Examiner disagrees. Applicant relies on ¶[0064] of the specification to support that the processor may measure basic blood components more accurately. Additionally, Applicant relies on Ex Parte Desjardins decision to support the contention (page 11 of the Remarks). However, Applicant’s argument is misguided and unpersuasive. In Desjardin, the improvements in computer functionality were based on examples pertaining to the machine learning model protecting the model’s knowledge about previous tasks while allowing it to effectively learn new tasks and/or based on adjustments to parameters of a machine learning model (page 11 of the Remarks). In this case, the only line that recites a possibility of an improvement is in ¶[0064], but this alleged “improvement” is directed to the calculation of a basic blood component, not to a computer component or machine learning model. There are no details on how this model makes an improvement related to the state of art of other multi-output machine learning models or what is accurate. Stated in other words, the specification merely recites that the processor may measure the concentration more accurately, but what is the comparison to? A different processor, model, concentration value? In Desjardin, the improvement was towards catastrophic forgetting in learning systems, here, the specification is not clear on the problem solved.
Applicant can overcome the rejection by pointing to the details of the improvement in the specification, showing a practical application of the abstract idea in the field of medical diagnostics, e.g. providing therapy to a condition, and/or showing a particular machine is used.
Applicant contends that the claims do not recite mathematical concepts, on pages 12-14 of the Remarks. Examiner disagrees. In this case, the abstract idea is guiding remeasurement based on a correlation, which is in itself a mathematical calculation MPEP 2106.04(a)(2)(I)(C). A mathematical calculation is considered a mathematical operation or an act of calculating using mathematical methods to determine a variable or number MPEP 2106.04(a)(2)(I)(C). Examiner invites Applicant to explain how guiding remeasurement to obtain a number based on calculating a correlation is not considered a mathematical concept, more specifically, a mathematical calculation.
Applicant argues that the claims are not directed to a mental process, on pages 14-17 of the Remarks. Applicant’s arguments are misguided because the rejection does not invoke the 35 U.S.C. 101 rejection based on z mental process, instead it is based on mathematical calculation. Thus, Applicant’s arguments related to mental process will not be addressed.
Examiner has updated the 35 U.S.C. 101 rejection to omit “streamlined analysis,” thus, any related arguments by Applicant will not be addressed. Applicant’s arguments with respect to 35 U.S.C. 103 rejection of independent claims have been considered but are moot because amendments require new grounds of rejection .
Applicant contends that Umekawa does not teach a correlation between the basic blood component and at least one auxiliary blood component, on page 21 of the Remarks. Examiner disagrees. As interpreted by Examiner, a basic and auxiliary blood component is at least two analytes found within the blood. This view is supported by the present disclosure that refers to blood components as triglycerides, uric acid, creatinine, cholesterol, glucose, not limited therefrom (para. [0042,0058]). As such, the “basic” and “auxiliary” titles are merely titles meant to represent any combination of analytes/blood components found in blood and interchangeable between the titles for any one analyte and not limited to representing the same analyte between the two. As interpreted by the examiner, a basic and auxiliary blood component is at least two analytes found within the blood, this claim language is extremely broad. The question stands whether Umekawa teaches a basic and one auxiliary blood component is considered as the basis for a guide remeasurement. In view of the interpretation above, the titles can be given to different analytes or the same analyte, whether a different one is the intended interpretation materially changes the requirements of the claim and may require further consideration before using Umekawa, however this is not the case in this instance. In this case, nothing precludes blood oxygen being the blood component for each title, instead the disclosure supports that the blood component (basic, auxiliary) can be blood glucose for both (para. [0042] of the instant application, “The processor 120 may receive data from the sensor 110 and measure the concentration of a blood component, such as triglyceride, uric acid, creatinine, blood carotenoid, glucose, lactate, total protein, cholesterol, ethanol, but is not limited thereto.” (emphasis added). The provided shows how the blood component is the above, therefore, the two titles must be related to this expansive list). In other words, the claim requires two measurements (basic and aux.) related to the blood components (glucose) and does not positively recite that they are different, just that two blood components are needed. Note. What is basic? What is aux.? are they different, are they the same, if the same are they consecutive to each other or is there a time period of other measurements in between? Is one training data previously obtained? Is one actual data received by an implanted sensor? Therefore, Umekawa teaches the claimed limitation. When determining the analyte concentration, a correlation between two signals that are correlated to blood components is analyzed and determined to be high or low (¶ [0273,0296,0347] of Umekawa). When the determination is high, the analyte concentration is considered inappropriate, and remeasurement is recommended (¶ [0296] of Umekawa, range is 0.1 to 1.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wu teaches preprocessing a to-be-predicted physical examination record and then inputting the record into the shared layer convolutional neural network of the chronic disease prediction model for feature extraction to obtain a feature map. A structure of the shared layer convolutional neural network is as follows: firstly, through a multi-layer task shared convolutional layer, feature extraction is performed by using 3 and 6 convolutional cores with a size of 3*3, and a step length of the convolutional core is set as 1. US 20220254493
Itu teaches a machine learning model trained to predict the measures of interest for the primary task and the one or more secondary tasks using multitask learning. The parameter sharing may be, e.g., hard sharing where hidden layers are shared between all tasks while a number of output layers are task specific, or soft sharing where each task has a distinct model with its own parameters but constraints are added to maximize similarity of the parameters across tasks. US 20210064936
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/MARTIN NATHAN ORTEGA/Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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