DETAILED ACTION
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 1-5 and 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A device for cuff-less blood pressure estimation, the device comprising:
a photoplethysmogram (PPG) sensor configured to obtain PPG measurements from a user of the device;
a processing unit comprising one or more processors configured to:
obtain, from the PPG sensor, a PPG signal;
decompose the PPG signal into one or more source components, the one or more source components comprising at least an AC component of the PPG signal;
map, using one or more trained machine learning models, the one or more source components to a target AC component and a target DC component; and
generate a transformed target AC component from the target AC component such that a first average value determined from the transformed target AC component matches a second average value determined from the target DC component, wherein the transformed target AC component corresponds to an approximate arterial blood pressure (ABP) signal for the user; and
an output unit configured to provide one or more features of the transformed target AC component for review by the user.
Claim 7 is copied below, with the limitations belonging to an abstract idea being underlined.
An electronically-implemented method for translation of signals having shared origin, the method comprising:
decomposing a source signal into a first source component and a second source component, wherein the first source component encodes oscillatory behavior of the source signal and the second source component encodes steady-state behavior of the source signal;
mapping, using one or more trained models, the first source component and the second source component to a first target component and a second target component respectively, wherein the first target component encodes estimated oscillatory behavior of a target signal and the second target component encodes estimated steady-state behavior of the target signal; and
reconstructing the target signal from the first target component and the second target component.
Claim 14 is copied below, with the limitations belonging to an abstract idea being underlined.
A non-transitory computer readable medium storing instructions which, when executed by a device comprising one or more processors, cause the device to carry out the steps of:
obtaining a source stochastic signal;
transforming, using a first transformation process, the source stochastic signal to a first complex map, wherein the first complex map encodes an oscillatory behavior of the source stochastic signal;
transforming, using a second transformation process, the source stochastic signal to a second complex map, wherein the second complex map encodes a steady-state behavior of the source stochastic signal;
mapping, using one or more neural networks, the first complex map and the second complex map to a third complex map and a fourth complex map respectively, wherein the third complex map and the fourth complex map are associated with a target stochastic signal;
transforming, using a first inverse transformation process associated with the first transformation process, the third complex map to a first component of the target stochastic signal, wherein the first component of the target stochastic signal represents the oscillatory behavior of the target stochastic signal;
transforming, using a second inverse transformation process associated with the second transformation process, the fourth complex map to a second component of the target stochastic signal, wherein the second component of the target stochastic signal represents the steady-state behavior of the target stochastic signal; and
reconstructing the target stochastic signal from the first component of the target stochastic signal and the second component of the target stochastic signal.
The limitations underlined can be considered to describe a mathematical concept, namely a series of calculations leading to one or more numerical results or answers, obtained by a sequence of mathematical operations on numbers and/or mental steps. The lack of a specific equation in the claim merely points out that the claim would monopolize all possible appropriate equations for accomplishing this purpose in all possible systems. These steps recited by the claim therefore amount to a series of mental and/or mathematical steps, making these limitations amount to an abstract idea.
In summary, the highlighted steps in the claim above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font.
The additional limitations in relation to the processing unit comprising one or more processors and an output unit does not offer a meaningful limitation beyond generally linking the use of the method to a computer (see ALICE CORP. v. CLS BANK INT’L 573 U. S. 208 (2014)). The claim does not recite a particular machine applying or being used by the abstract idea.
The additional limitations of obtaining a PPG signal from a PPG sensor equates to extrasolution data activity, i.e. data gathering (see MPEP 2106.05(g)).
The additional limitation of providing one or more features of the transformed target AC component for review by the user equates to extrasolution data activity, i.e. data reporting (see MPEP 2106.05(g)).
The additional limitation of the photoplethysmogram (PPG) sensor configured to obtain PPG measurements from a user of the device is recited at a high level of generality and does not amount to something significantly more than the recited abstract idea.
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, claims 1, 7, and 14 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-5, 8-13, 15-20 are similarly ineligible. The dependent claims merely add limitations which further detail the abstract idea, namely further mathematical/mental steps detailing how the data processing algorithm is implemented, i.e. additional software limitations, and/or further define or add insignificant extrasolution data activity. These do not help to integrate the claim into a practical application or make it significantly more than the abstract idea (which is recited in slightly more detail, but not in enough detail to be considered to narrow the claim to a particular practical application itself).
Dependent claim 6 equates to a practical application when viewing the claim as a whole. Claim 6 reads as follows:
6. The device of claim 1 wherein the one or more features provided by the output unit include one or both of an estimated diastolic blood pressure and an estimated systolic blood pressure estimated from one or more peaks and one or more troughs of the transformed target AC component.
In combination with the additional limitation present in independent claim 1, claims 1 and 6 encompass the practical application of using a sensor of the device to obtain PPG measurements from a user, process the received signals from a user using a processor of the device, and outputting a particular practical result to the user of the device, i.e. an estimated diastolic blood pressure and/or an estimated systolic blood pressure. These measurements values are practical/useful measurement values for evaluating/tracking/monitoring the health of the user.
Examiner Note Regarding Prior Art
As per claim 1, the prior art Ferber (US 20170245767) discloses a device for cuff-less blood pressure estimation (see Abstract and paragraph 0045: non-invasive/cuffless blood pressure estimation);
a photoplethysmogram (PPG) sensor configured to obtain PPG measurements from a user of the device (see paragraphs 0011 and 0012: measurement apparatus, measurement signal may be a PPG signal);
a processing unit comprising one or more processors configured to obtain, from the PPG sensor, a PPG signal (see Fig. 3A and paragraph 0122-0123);
decompose the PPG signal into one or more source components, the one or more source components comprising at least an AC component of the PPG signal (see paragraphs 0015 and 0106: determines AC component and DC component of the signal, i.e. PPG signal);
and calculate a blood pressure value from the signal metrics and an output unit configured to provide a blood pressure value and/or alerts (see paragraph 0119-0120 and 0194: discusses calculation of blood pressure; and paragraphs 0120 and 0261: display to display data/blood pressure to a user).
The prior Athaya (An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach) discloses mapping, using one or more trained machine learning models, the PPG signal to a target blood pressure signal (see page 5 first paragraph and Fig. 4 and Fig. 9: generates a blood pressure waveform from a PPG signal using machine learning network).
However, the prior art of record fails to disclose a processor configured to map, using one or more trained machine learning models, the one or more source components to a target AC component and a target DC component; and
generate a transformed target AC component from the target AC component such that a first average value determined from the transformed target AC component matches a second average value determined from the target DC component, wherein the transformed target AC component corresponds to an approximate arterial blood pressure (ABP) signal for the user.
As per claim 7, the prior art of record discloses the limitation discussed above.
Ferber further discloses an electronically-implemented method for translation of signals having shared origin (see Abstract and paragraph 0045: non-invasive/cuffless blood pressure estimation using PPG signals, i.e. translating PPG signals to blood pressure signal, i.e. signals have a shared origin), the method comprising:
decomposing a source signal into a first source component and a second source component, wherein the first source component encodes oscillatory behavior of the source signal and the second source component encodes steady-state behavior of the source signal (see paragraphs 0015 and 0106: determines AC component and DC component of the signal, i.e. PPG signal).
The prior Athaya further discloses mapping, using one or more trained models, an oscillatory PPG signal with a the DC component removed, i.e. the first source component to a first target component, wherein the first target component encodes estimated oscillatory behavior of a target signal (see page 5 first paragraph and Fig. 4 and Fig. 9: generates a blood pressure waveform from a PPG signal using machine learning network; and see page 5 section 3.2 pre-processing of data: PPG signal data is filed to remove baseline wandering, values below 0.5Hz); and
reconstructing the target signal from the first target component (see Fig. 9: shows estimated blood pressure signal).
However the prior art of record fails to disclose mapping, using one or more trained models, the first source component and the second source component to a first target component and a second target component respectively, wherein the first target component encodes estimated oscillatory behavior of a target signal and the second target component encodes estimated steady-state behavior of the target signal; and
reconstructing the target signal from the first target component and the second target component.
As per claim 14, the prior art of record discloses the limitations discussed above.
The prior art Ferber discloses a non-transitory computer readable medium storing instructions which, when executed by a device comprising one or more processors, cause the device to carry out the steps of obtaining a source stochastic signal (see Fig. 3A and paragraph 0122-0123: processor/computer with memory obtain PPG signal, i.e. the recited source stochastic device; see paragraphs 0258-0259: non-transitory memory with instructions).
Lychagov (US 20240268720) further discloses transforming, using a first transformation process, the source stochastic signal to a first complex map, wherein the first complex map encodes an oscillatory behavior of the source stochastic signal (see paragraph 0138: separates PPG signal into an AC component and transforms the AC signal to frequency domain, i.e. complex map as described in the specification);
transforming, using a second transformation process, the source stochastic signal to a second complex map, wherein the second complex map encodes a steady-state behavior of the source stochastic signal (see paragraph 0138: separates PPG signal into a DC component and transforms the DC signal to frequency domain, i.e. complex map as described in the specification).
The prior art Li (US 20230363651) transforming, using a first transformation process, the source stochastic signal to a first complex map, wherein the first complex map encodes an oscillatory behavior of the source stochastic signal (see Abstract and Fig. 6);
transforming, using a first inverse transformation process associated with the first transformation process, the third complex map to a first component of the target stochastic signal, wherein the first component of the target stochastic signal represents the oscillatory behavior of the target stochastic signal to generate an arterial blood pressure data (see Abstract and Fig. 6).
However the prior art of record fails to disclose the combination of mapping, using one or more neural networks, the first complex map and the second complex map to a third complex map and a fourth complex map respectively, wherein the third complex map and the fourth complex map are associated with a target stochastic signal;
transforming, using a first inverse transformation process associated with the first transformation process, the third complex map to a first component of the target stochastic signal, wherein the first component of the target stochastic signal represents the oscillatory behavior of the target stochastic signal;
transforming, using a second inverse transformation process associated with the second transformation process, the fourth complex map to a second component of the target stochastic signal, wherein the second component of the target stochastic signal represents the steady-state behavior of the target stochastic signal; and
reconstructing the target stochastic signal from the first component of the target stochastic signal and the second component of the target stochastic signal.
Allowable Subject Matter
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. This is because dependent claim 6 is current not part of the outstanding 101 Claim Rejections.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Park (EP 3649928) discusses generating a first and second complex map from AC and DC components of a blood pressure signal measured by a cuff.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J DALBO whose telephone number is (571)270-3727. The examiner can normally be reached M-F 9AM - 5PM.
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/MICHAEL J DALBO/ Primary Examiner, Art Unit 2857