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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are all within at least one of the four categories.
The independent claim 1 recites:
receiving signal data from a non-invasive blood monitor, wherein the signal data comprises frequency data corresponding to a heart rate pulse of a user of the non-invasive blood monitor;
performing a Fourier transform of one or more sections of the signal data;
selecting a subset of modes from the Fourier transform;
using data from the Fourier transform to initialize an adaptive, linear signal model; and
applying Kalman filtering to the linear signal model to update the linear signal model by propagating the subset of modes and processing a dynamic projection of the signal data onto the subset of modes.
The independent claim 9 recites:
receiving signal data from a non-invasive blood monitor, wherein the signal data comprises frequency data corresponding to a heart rate pulse of a user of the non-invasive blood monitor;
extracting one or more features of the signal data by using a dynamic projection of the signal data onto Fourier modes;
correlating one or more extracted features from the signal data with a blood analyte condition; and
predicting the blood analyte condition using the one or more extracted features.
The independent claim 14 recites:
receiving signal data from a non-invasive blood monitor, wherein the signal data comprises frequency data corresponding to a heart rate pulse of a user of the non-invasive blood monitor;
identifying one or more parameters associated with a shape of the heart rate pulse; and
using the one or more parameters to predict a blood analyte condition from the signal data.
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. See footnotes 14 and 15.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018) (performing a resampled statistical analysis to generate a resampled distribution).
The claimed steps of receiving; performing; selecting; using; applying; extracting; correlating; predicting; and identifying can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
Examples of ineligible claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
With respect to the pending claims, for example, a experienced clinician can perform the claimed step of receiving by mentally noting data from a monitor and can then further perform Fourier transformation, extraction of features and identification of parameters by looking at the data. The experienced clinician can then select modes for Fourier transform and use that data to initialize a model and apply filtering to the data mentally. Further, the experienced clinician can correlate the extracted features with a analyte concentration and predict the analyte concentrated based on the extracted features mentally. Further, the experienced clinician can use the identified parameters to predict an analyte condition from the data. Thus, the claims can be readily interpreted as being a mere application of a mental process on a computer.
Regarding the dependent claims, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. For example, dependent claims recite steps (e.g. using; updating; comparing; identifying; and applying) that can be performed in the mind. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
This judicial exception (abstract idea) in claims 1-20 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for receiving; performing; selecting; using; applying; extracting; correlating; predicting; identifying; updating; and comparing merely invoke a computer as a tool.
The data-gathering step (receiving) does not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for receiving; performing; selecting; using; applying; extracting; correlating; predicting; identifying; updating; and comparing.
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to provide information about received signals.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for receiving; performing; selecting; using; applying; extracting; correlating; predicting; identifying; updating; and comparing. The claims do not apply the obtained data to a particular machine. Rather, the data is merely output in an post-solution step.
The additional elements are identified as follows: a non-invasive blood analyte measurement system; and a non-invasive blood monitor.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
The patent publication cited by applicant herewith:
Leabman (US 20200192428 A1; cited by applicant)
See [0102]-[0103] and Fig. 4D
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations 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 or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Leabman (US 20200192428 A1; cited by applicant).
With respect to claim 1, Leabman discloses a method for processing signals from a non-invasive blood analyte measurement system (see paragraph 0103: a method comprising a wearable health device comprising the steps of transmitting, receiving and processing radio waves non-invasively through skin to measure an analyte comprising glucose), the method comprising the steps of:
receiving signal data from a non-invasive blood monitor (see paragraph 0102-0103 and Fig. 4D: receiving radio waves from a wearable device such as a smartwatch), wherein the signal data comprises frequency data corresponding to a heart rate pulse of a user of the non-invasive blood monitor (see paragraph 0158 and 0162: the signal data comprises blood glucose data also comprises frequency data corresponding to a person’s heart rate which can be monitored);
performing a Fourier transform of one or more sections of the signal data (see paragraph 0158: a fast Fourier transform is performed);
selecting a subset of modes from the Fourier transform (see paragraph 0158: Fourier transform is used to isolate and select binned modes that are likely to represent moving objects);
using data from the Fourier transform to initialize an adaptive, linear signal model (see paragraph 0220 and Fig.34: an adaptive model is trained from the data from the Fourier transform); and
applying Kalman filtering to the linear signal model to update the linear signal model by propagating the subset of modes and processing a dynamic projection of the signal data onto the subset of modes (see paragraph 0128: Kalman filter is applied to smooth out noisy data).
With respect to claim 2, all limitations of claim 1 apply in which Leabman further discloses further comprising using the linear signal model to predict movements in a blood analyte concentration (see paragraph 0157, 0183-0184 and Fig. 28: the method separates moving signals indicative of blood glucose level from non-moving signals and then trains a machine learning model to better predict the glucose level).
With respect to claim 3, all limitations of claim 2 apply in which Leabman further discloses wherein the blood analyte comprises glucose (see paragraph 0157: glucose).
With respect to claim 4, all limitations of claim 1 apply in which Leabman further discloses using the linear signal model to predict a blood analyte concentration (see paragraph 0157, 0183-0184: the method separates moving signals indicative of blood glucose level from non-moving signals and then trains a machine learning model to better predict the glucose level).
With respect to claim 5, all limitations of claim 1 apply in which Leabman further discloses wherein the step of applying Kalman filtering to the linear signal model to update the linear signal model comprises:
adaptively updating the linear signal model to track signal dynamics using Kalman updates (see paragraph 0128: signal is adaptively updated using Kalman updates by being filtered by the Kalman filter to smooth noisy data);
updating an estimate of the linear signal model (see paragraph 0183 and Fig. 28: at step 2810, raw data is correlated to control data resulting in derived data); and
comparing the updated estimate of the linear signal model to new signal data received from the non-invasive blood monitor (see paragraph 0138 and Fig. 28: correlation between new raw data and derived data at step 2806 is compared at step 2812 to see if it is acceptable).
With respect to claim 6, all limitations of claim 1 apply in which Leabman further discloses further comprising identifying one or more parameters associated with a shape of the heart rate pulse (see paragraph 0170: an amplitude which represents a height of a shape of a heart rate pulse, is used to identify the blood glucose level parameter).
With respect to claim 7, all limitations of claim 6 apply in which Leabman further discloses further comprising using the one or more parameters to predict a blood analyte condition (see paragraph 0157-0158, 0170, 0183-0184 and Fig. 28: method separates moving signals indicative of a blood glucose level from non-moving signals and determines a blood glucose level from the amplitude of those signals and then trains a machine learning model to better predict the glucose level).
With respect to claim 8, all limitations of claim 7 apply in which Leabman further discloses wherein the blood analyte condition comprises a blood glucose level (see paragraph 0157: blood glucose level).
With respect to claim 9, Leabman discloses a method for identifying one or more features of a shape of a heart pulse signal using a non-invasive monitor (see paragraph 0103: a method comprising a wearable health device comprising the steps of transmitting, receiving and processing radio waves non-invasively through skin to measure an analyte comprising glucose; and see paragraph 0170: an amplitude which represents a height of a shape of a heart rate pulse, is used to identify the blood glucose level parameter), the method comprising the steps of:
receiving signal data from a non-invasive blood monitor (see paragraph 0102-0103 and Fig. 4D: receiving radio waves from a wearable device such as a smartwatch), wherein the signal data comprises frequency data corresponding to a heart rate pulse of a user of the non-invasive blood monitor (see paragraph 0158 and 0162: the signal data comprises blood glucose data also comprises frequency data corresponding to a person’s heart rate which can be monitored);
extracting one or more features of the signal data by using a dynamic projection of the signal data onto Fourier modes (see paragraph 0158: Fourier transform is used to isolate and select binned modes that are likely to represent moving objects; and see paragraph 0128: Kalman filter is applied to smooth out noisy data);
correlating one or more extracted features from the signal data with a blood analyte condition (see paragraph 0157, 0183-0184 and Fig. 28: the method separates moving signals indicative of blood glucose level from non-moving signals and then trains a machine learning model to better predict the glucose level); and
predicting the blood analyte condition using the one or more extracted features (see paragraph 0157, 0183-0184 and Fig. 28: the method separates moving signals indicative of blood glucose level from non-moving signals and then trains a machine learning model to better predict the glucose level).
With respect to claim 10, all limitations of claim 9 apply in which Leabman further discloses wherein the blood analyte condition comprises a concentration of the analyte (see paragraph 0157 and 0181: blood glucose level concentration).
With respect to claim 11, all limitations of claim 9 apply in which Leabman further discloses wherein the blood analyte condition comprises a trend over time of the analyte (see paragraph 0157 and 0181: blood glucose level concentration over time).
With respect to claim 12, all limitations of claim 9 apply in which Leabman further discloses wherein the blood analyte comprises glucose (see paragraph 0157: glucose).
With respect to claim 13, all limitations of claim 9 apply in which Leabman further discloses wherein the step of extracting one or more features of the signal data comprises use of a Kalman filter (see paragraph 0128: Kalman filter is applied to smooth out noisy data).
With respect to claim 14, Leabman discloses
A method for predicting a blood analyte condition using data from a non-invasive blood monitor (see paragraph 0103: a method comprising a wearable health device comprising the steps of transmitting, receiving and processing radio waves non-invasively through skin to measure an analyte comprising glucose), the method comprising the steps of:
receiving signal data from a non-invasive blood monitor (see paragraph 0102-0103 and Fig. 4D: receiving radio waves from a wearable device such as a smartwatch), wherein the signal data comprises frequency data corresponding to a heart rate pulse of a user of the non-invasive blood monitor (see paragraph 0158 and 0162: the signal data comprises blood glucose data also comprises frequency data corresponding to a person’s heart rate which can be monitored);
identifying one or more parameters associated with a shape of the heart rate pulse (see paragraph 0158: a fast Fourier transform is performed); and
using the one or more parameters to predict a blood analyte condition from the signal data (see paragraph 0157, 0183-0184 and Fig. 28: the method separates moving signals indicative of blood glucose level from non-moving signals and then trains a machine learning model to better predict the glucose level).
With respect to claim 15, all limitations of claim 14 apply in which Leabman further discloses wherein the blood analyte condition comprises a trend over time in a concentration of the blood analyte (see paragraph 0157 and 0181: blood glucose level concentration trend over time).
With respect to claim 16, all limitations of claim 14 apply in which Leabman further discloses wherein the blood analyte condition comprises a concentration of the blood analyte (see paragraph 0157 and 0181: blood glucose level concentration over time).
With respect to claim 17, all limitations of claim 14 apply in which Leabman further discloses wherein the blood analyte comprises blood glucose (see paragraph 0157: blood glucose).
With respect to claim 18, all limitations of claim 14 apply in which Leabman further discloses wherein the step identifying one or more parameters comprises performing a Fourier transform of the signal data (see paragraph 0158: a fast Fourier transform is performed)
With respect to claim 19, all limitations of claim 18 apply in which Leabman further discloses further comprising applying Kalman filtering to data resulting from the Fourier transform (see paragraph 0128: Kalman filter is applied to smooth out noisy data from the Fourier transform).
With respect to claim 20, all limitations of claim 19 apply in which Leabman further discloses wherein the step of applying Kalman filtering to data resulting from the Fourier transform comprises:
using data from the Fourier transform to initialize an adaptive, linear signal model (see paragraph 0220 and Fig.34: an adaptive model is trained from the data from the Fourier transform); and
applying Kalman filtering to the linear signal model to update the linear signal model (see paragraph 0128: Kalman filter is applied to smooth out noisy data).
Conclusion
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/N.N.P./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
