DETAILED ACTION
Note: The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s arguments filed in the reply on April 13, 2026 were received and fully considered. Claims 1, 7, and 13 were amended. The current action is FINAL. Please see corresponding rejection headings and response to arguments section below for more detail.
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 1-18 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. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites method for monitoring blood glucose. Thus, the claim is directed to a method/process, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
“…generating, by a machine learning model on the cloud server, based on the input information, a glucose prediction of the patient based on a combination of the photoplethysmography signal, the blood pressure measurement, and the demographic information.”
These limitations describe a mathematical calculation. When given their broadest reasonable interpretation in light of the specification, the limitations identified above including the recited machine learning model are mathematical calculations. Moreover, the plain meaning of a machine learning model is a series of mathematical calculations. See also 2024 AI SME Update, which held a similar claim construction was not patent eligible (see claim 2 of example 47, using a trained artificial neural network to analyze anomalies on input data was not patent eligible). The 2024 AI SME Update also sets forth that a machine learning model amounts to a mental process (claim 2 of example 47). As such, the limitations also describe a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human, or using simple pen/paper.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application.
For this part of the 101 analysis, the following additional limitations are considered:
“…continuously capturing a photoplethysmography signal of a patient in real-time; inputting a blood pressure measurement, demographic information, and the photoplethysmography signal into a mobile application; transmitting, as input information, the blood pressure measurement, the demographic information, and the photoplethysmography signal to a cloud server…”
These additional limitations do not integrate the judicial exception into a practical application. Rather, the additional limitations are each recited at a high level of generality such that it amounts to insignificant extra-solution activity, i.e., mere data gathering steps necessary to perform the identified judicial exception fail to integrate the claims into a practical application. See MPEP 2106.05(g).
The additional limitations also do not add significantly more to the identified judicial exception because they relate to widely-understood, routine, and conventional techniques for obtaining known types of physiological data. Moreover, the additional limitations are recited at a high level of generality.
Independent claims 7 and 13 are also not patent eligible for substantially similar reasons.
Dependent claims 2-6, 8-12, and 14-18 also fail to add something more to the abstract independent claims as they merely further limit the abstract idea, recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above, and/or do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above.
Therefore, claims 1-18 are not patent eligible under 35 USC 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lokare et al. (US PG Pub. No. 2024/0057900 A1) (hereinafter “Lokare”).
Lokare was applied in the previous office action.
With respect to claims 1, 7, and 13, Lokare teaches a method, system, and computer program embodied on a non-transitory computer readable medium for monitoring blood glucose (see title, abstract), comprising: continuously capturing a photoplethysmography signal of a patient in real-time (abstract “blood glucose estimation for a subject includes receiving real-time PPG data”; par.0062 “continuous PPG measurements”); inputting a blood pressure measurement, demographic information, and the photoplethysmography signal into a mobile application (par.0062 “continuous PPG measurements from a subject, combined with at least one BP… to improve estimation accuracy”; par.0065 “wearable PPG devices 12-16 may be in communication… with a blood pressure monitoring device”; par.0087 “at least one data bus 102 for receiving PPG data… environmental sensor data… blood pressure… store the [PPG] features in memory; store the blood pressure measurement in memory… and generate a biometric estimation for the subject”; par.0106 “may receive external meta data (i.e., height, weight, age, sex, medication usages, and the like) for the subject and store this data in memory”); and generating, by a machine learning model based on the input information, a glucose prediction of the patient based on a combination of the photoplethysmography signal, the blood pressure measurement, and the demographic information (abstract “generating blood glucose estimation”, which takes into consideration sensor inputs 102, which include PPG Data, Blood pressure measurement data, and demographic information, as disclosed in par.0065, 0087, and 0106; par.0007 “generating a blood glucose estimation for the subject… using… machine learning models”).
Although Lokare does not explicitly teach transmitting, as input information, the blood pressure measurement, the demographic information, and the photoplethysmography signal to a cloud server, further modification to incorporate transmitting to a cloud server would have been prima facie obvious to person having ordinary skill in the art (“PHOSITA”) when the invention was filed for the following reasons. First, Lokare expressly defines various terms including utilization of a cloud server (par.0056); and “communicating wirelessly with an external system… well known to those skill in the art” (par.0104). Therefore, PHOSITA would have had predictable success modifying Lokare to incorporate transmitting, as input information, the blood pressure measurement, the demographic information, and the photoplethysmography signal to a cloud server in order to communicate wirelessly with an external system (e.g. via a known cloud server).
With respect to claims 2, 8, and 14, Lokare does not explicitly teach wherein the input information is transmitted to the cloud server at every 10 seconds, and wherein the glucose prediction of the patient is generated at every 10 seconds. However, such a modification would have been prima facie obvious to PHOSITA when the invention is filed as is it widely known to transmit data over a network (cloud server) at various speeds, rates, etc. Moreover, Lokare suggests that communicating wirelessly with an external system is well known to those skilled in the art (par.0104). As such, merely modifying the rate at which data is transmitted to the cloud server would only require routine skill in the art.
With respect to claims 3, 9, and 15, Lokare teaches wherein the photoplethysmography signal of the patient is captured by a measurement device, and wherein the photoplethysmography signal is captured non-invasively (par.0002 “wearable”).
With respect to claims 4, 10, and 16, Lokare teaches classifying, based on the glucose prediction, a glucose level of the patient as a warning, as normal, or as abnormal (par.0111 “low vs. high levels”; par.0119 “may benefit from knowing that their blood glucose levels are about to rise/drop sharply”).
With respect to claims 5, 11, and 17, Lokare teaches providing an alert based on the classification (par.0017 “send an alert to a remote device that the generated blood glucose estimation is above or below a threshold”).
With respect to claims 6, 12, and 18, Lokare teaches wherein the transmission of the input information to the cloud server comprises transmitting the input information to a machine learning model to generate the glucose prediction (par.0016 “adaptive predictive models may include, but are not limited to, regression models, machine learning models, and classifier models”).
Response to Arguments
Applicant’s arguments filed with respect to the 35 USC 101 rejections raised in the previous office action were fully considered, but they are not persuasive. When considered as an ordered combination, Examiner maintains that the claims recite an abstract idea that is not integrated into a practical application, and the additional limitations do not recite significantly more. While applicant’s “improvement” argument is acknowledged (see remarks, pg. 8 “improves the robustness and utility of non-invasive glucose prediction”), Examiner does not find this persuasive as the purported improvement lies within the judicial exception itself1. Moreover, there is no improvement to the additional (structural) limitations. Rather, the additional limitations (e.g. processor in claim 13) are merely being utilized as tools to implement the abstract idea. See also 2024 AI SME Update, which held a similar claim construction was not patent eligible (see claim 2 of example 47, using a trained artificial neural network to analyze anomalies on input data was not patent eligible). The 2024 AI SME Update also sets forth that a machine learning model amounts to a mental process (claim 2 of example 47). For at least these reasons, the 35 USC 101 rejections are maintained. Please see corresponding rejection heading above for more detailed analysis.
Applicant’s arguments filed with respect to the 35 USC 103 rejections raised in the previous office action were fully considered, but they are not persuasive. Applicant argues that Lokare does not teach and/or suggest estimating glucose based on a combination of a PPG signal, blood pressure measurement, and demographic information. More specifically, applicant calls into question whether Lokare suggests utilizing demographic information in order to estimate blood glucose. Examiner respectfully disagrees. Lokare expressly discloses estimating glucose by considering various inputs including PPG and BP (par.0062 “continuous PPG measurements from a subject, combined with at least one BP… to improve estimation accuracy (of glucose)”. Lokare also suggests that other input sources can also be taken into consideration, including environmental sensor data, external meta data, height, weight, age, sex (par.0106). As such, Examiner argues that Lokare, at the very least, suggests taking into consideration demographic information. Moreover, the claims do not provide any specificity as to how the claimed inputs are combined such that it would be nonobvious over Lokare’s disclosure for estimating glucose based on the recited inputs. Notwithstanding the above, and solely for purposes of compact prosecution, Examiner also cites additional reference(s) that further demonstrate the known nature of utilizing demographic information in order to generate a more robust calculation of blood glucose. See prior art reference cited below for additional example teaching. For at least these reasons, the 35 USC 103 rejections are maintained.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG Pub. No. 2023/0298764, see par.0074, 97; Fig. 1
Conclusion
No claim is allowed.
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 PUYA AGAHI whose telephone number is (571)270-1906. The examiner can normally be reached M-F 8 AM - 5 PM.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791
1 “the judicial exception alone cannot provide the improvement.” See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981).
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