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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP21206863.9, filed on 11/08/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/08/2024 was considered by the examiner.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A computer-implemented method for implementation on a measurement device, comprising:
a differential measurement operation, comprising:
reading in signals from at least two input channels of the measurement device, the signal from each input channel being received from a respective sensor element;
applying a linearization function to each of the input channels, the linearization function having adjustable parameters;
applying a computation algorithm to the input channels subsequent to applying the linearization function, to derive at least one output measurement;
outputting a data signal indicative of the output measurement; and
a calibration operation performed at a different time than the measurement operation comprising:
applying a plurality of sets of constant-value calibration input signals to the input channels of the measurement device;
applying the computation algorithm to each set of the calibration input
signals without applying the linearization function, and recording each respective output measurement, to form a calibration dataset; and
fitting the adjustable parameters of the linearization function based on the
calibration dataset.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above; the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a process and is therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portion of claim 1 comprises process steps that fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category.
Individually and collectively, the steps:
“reading in signals from at least two input channels of the measurement device, the signal from each input channel being received from a respective sensor element”;
“applying a linearization function to each of the input channels, the linearization function having adjustable parameters”;
“applying a computation algorithm to the input channels subsequent to applying the linearization function, to derive at least one output measurement”;
“outputting a data signal indicative of the output measurement”;
“applying a plurality of sets of constant-value calibration input signals to the input channels of the measurement device”;
“applying the computation algorithm to each set of the calibration input
signals without applying the linearization function, and recording each respective output measurement, to form a calibration dataset”; and
“fitting the adjustable parameters of the linearization function based on the
calibration dataset.”
may be performed as mental processes and/or mathematical calculations. Reading in signals is gathering information, which may be performed as mental processes. Applying a linearization function, computation algorithms, and calibration input signals to the input channels are analysis, which may be performed as mental processes, and/or mathematical operations. Outputting a data signal indicative of the output measurement is the displaying the results of the analysis, which may be performed as mental processes. Fitting adjustable parameters is analysis, which may be performed as mental processes. The type of high-level information collecting and analyzing data recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), 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).
Similar limitations comprise the mental processes type abstract idea recited by independent claim 12.
Step 2A, Prong Two of the analysis entails determining whether a claim includes additional elements that integrate the recited judicial exception (e.g., abstract idea) into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application. Based on the individual and collective limitations of claim 1, applying a broadest reasonable interpretation, the most significant of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” in any combination appear to integrate the abstract idea to technologically improve any aspect of a system that may be used to implement the highlighted steps such a generic computer.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements such as the “differential measurement operation” and “calibration operation” are not utilized in a particularized manner and do not amount to any such particular machine.
Regarding effectuation of a transformation or reduction of a particular article to a different state or thing, the claim includes no such transformation or reduction. Instead the claim as a whole entails gathering information, analyzing and/or applying mathematical calculations on said information, and displaying certain results of the analysis. The fitting of parameters is not a transformation of a particular article. Additionally, “outputting a data signal indicative of the output measurement” is insignificant extra-solution activity, and does not integrate the judicial exception into a practical application (MPEP 2106.05(g)).
Similar analysis applies to independent claim 12, which recites additional elements including “an input/output” and “one or more processors”. The additional elements amount to a generic computer, and therefore does not amount to an improvement in a computer or technology. The claim as a whole amounts to mere instruction to implement the process steps in a generic computer, and therefore does not integrate the judicial exception into a practical application (MPEP 2106.05(f)).
The above additional elements, considered individually and in combination with the claim elements reciting an abstract idea do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under Step 2B.
Regarding Step 2B, independent claims 1 and 12, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional. Further, any alleged inventive concept would be found in the abstract ideas, and would not amount to significantly more (MPEP 2106.05: “As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.")).
Independent claims 1 and 12 are therefore not patent eligible.
Dependent claims 2-11 and 13-14 provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of the independent claims (Step 2A, Prong One). None of dependent claims 2-11 and 13-14 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for the same reasons as discussed with regards to the independent claims. Claims 2 further details the linearization function, which is a mathematical concept. Claims 3 details the signals received. Claims 4 details the computation algorithm, which may be performed as mental processes and/or mathematical concepts. Claims 5 and 7 further details the application of a plurality of vectors, which may be performed as mental processes and/or mathematical calculations. Claims 6 further details the device from which the signals are received. Claims 8 details the input and further details the calibration procedure. Claim 9 further details the optimization of the linearization, which may be performed as mental processes and/or mathematical calculations. Claim 10 further details the signal received and the optimization. Claim 11 recites a “non-transitory computer-readable medium that stores therein a computer program product” and “a processor”, which amounts to a generic computer and the mere instructions to implement the judicial exception on a generic computer. Claim 13 recites a measurement device, comprising “at least two signal input ports”, and claim 14 recites a system, comprising “a signal measurement apparatus comprising at least two sensor elements”, which amounts to extra-solution activity for necessary information gathering (MPEP 2106.05(g)) and is not utilized in a particularized manner.
The dependent claims 2-11 and 13-14 therefore are also ineligible subject matter.
Allowable Subject Matter
Claims 1-14 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome the rejection under 35 USC 101.
The closest art was considered.
Regarding claim 1, Burns et al. (US 20110251817 A1, provided by applicant) teaches A computer-implemented method for implementation on a measurement device (Abstract), comprising:
a differential measurement operation ([0051] lines 24-28, “As can be appreciated by those skilled in the art, other embodiments employing an A/D converter 308 with a differential input and differential input multiplexer 310 may serve to simplify the process of measuring the differential voltage V1.”), comprising:
reading in signals from at least two input channels of the measurement device, the signal from each input channel being received from a respective sensor element ([0016] Exemplary output signals may include biopotential electrical signals derived from a patient, such as ECG, EMG, or EEG signals. In brief, exemplary embodiments may include a system and method for measuring the combined impedance of a patient's skin plus the skin/electrode interface, whereby the gain of the overall measurement device may be modified to compensate for measured variations in the total skin/electrode impedance);
applying a computation algorithm to the input channels (Fig. 3; see [0041] and [0046]);
outputting a data signal indicative of the output measurement (display unit 304); and
a calibration operation performed at a different time than the measurement operation (Calibration mode of [0044-0050]) comprising:
applying a plurality of sets of constant-value calibration input signals to the input channels of the measurement device ([0049-0050] );
applying the computation algorithm to each set of the calibration input
signals ([0049-0053]).
Johansson et al. (US 20180316367 A1) teaches a method, comprising:
applying a computation algorithm (Fig. 7B; [0028] lines 3-7, “The correction terms are continuously generated as a function of the original input signal; and the feedback signal is used to adapt this function, so as to minimize the difference between the feedback signal and the original input signal.”) to the input channels subsequent to applying the linearization function (Fig. 7A)
applying a linearization function to each of the input channels (Fig. 7A; [0041] lines 5-7, “The pre-distorting linearizer could be analogue or digital, but here we will assume that it is digital, e.g. a so-called Digital Pre-Distorter, DPD 101”), the linearization function having adjustable parameters;
; and
fitting the adjustable parameters of the linearization function
([0101] “An iterative approach to solve for the linearization coefficients would be to update the two vector equations according to the following Least Mean Squares (LMS) algorithm”).
None of the prior art, alone or in combination, teaches the method, comprising:
applying the computation algorithm to each set of the calibration input signals without applying the linearization function, and recording each respective output measurement, to form a calibration dataset.
Regarding claim 12, Burns teaches A processing arrangement (Abstract) comprising:
an input/output ([0016]); and
one or more processors (microprocessor 302),
wherein the one or more processors are operable in a first mode in which the one or more processors perform a differential measurement operation ([0051] lines 24-28, “As can be appreciated by those skilled in the art, other embodiments employing an A/D converter 308 with a differential input and differential input multiplexer 310 may serve to simplify the process of measuring the differential voltage V1.”), comprising:
reading in signals from at least two input channels of the measurement device ([0016] Exemplary output signals may include biopotential electrical signals derived from a patient, such as ECG, EMG, or EEG signals. In brief, exemplary embodiments may include a system and method for measuring the combined impedance of a patient's skin plus the skin/electrode interface, whereby the gain of the overall measurement device may be modified to compensate for measured variations in the total skin/electrode impedance), the signal from each channel being received from a respective sensor element;
applying a linearization function to each of the input channels, the linearization function having adjustable parameters;
applying a computation algorithm to the input channels (Fig. 3; see [0041] and [0046]);
outputting a data signal indicative of the output measurement (display unit 304); and
wherein the one or more processors are operable in a second mode in which they are configured to perform a calibration operation (Calibration mode), comprising:
applying a plurality of sets of constant-value calibration input signals to the input channels of the measurement device ([0049-0050]);
applying the computation algorithm to each set of the calibration input signals ([0049-0053]).
Johansson teaches an arrangement, comprising:
applying a computation algorithm (Fig. 7B; [0028] lines 3-7, “The correction terms are continuously generated as a function of the original input signal; and the feedback signal is used to adapt this function, so as to minimize the difference between the feedback signal and the original input signal.”) to the input channels subsequent to applying the linearization function (Fig. 7A)
applying a linearization function to each of the input channels (Fig. 7A; [0041] lines 5-7, “The pre-distorting linearizer could be analogue or digital, but here we will assume that it is digital, e.g. a so-called Digital Pre-Distorter, DPD 101”), the linearization function having adjustable parameters;
; and
fitting the adjustable parameters of the linearization function
([0101] “An iterative approach to solve for the linearization coefficients would be to update the two vector equations according to the following Least Mean Squares (LMS) algorithm”).
None of the prior art, alone or in combination, teaches the arrangement, comprising:
applying the computation algorithm to each set of the calibration input signals without applying the linearization function, and recording each respective output measurement, to form a calibration dataset.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN GEISS whose telephone number is (571)270-1248. The examiner can normally be reached Monday - Friday 7:30 am - 4:30 pm.
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/B.B.G./ Examiner, Art Unit 2857
/Catherine T. Rastovski/ Supervisory Primary Examiner, Art Unit 2857
