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 .
Formal Matters
Applicant's response, filed 27 July 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-11 are currently pending and have been examined.
Claims 1 and 6 have been amended.
Claims 1-11 have been rejected.
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 for Application No. KR10-2022-0177319, filed on 01/08/2024.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 16 December 2022 claiming benefit to KR10-2022-0177319.
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 1-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-11 are drawn to a device or a method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a device for determining an operational status of a target sensor. Independent claim 6 recites a method for determining an operational status of a sensor.
These independent claims recite the following steps best characterized as mathematical concepts under MPEP § 2106.04(a)(2)(I) citing the abstract idea grouping for mathematical concepts in general:
determine initial values of parameters of a Bayesian model and a degree of a polynomial regression model based on historical data of the target sensor and historical data of a reference sensor
infer a posterior distribution of a regression coefficient and an error term of a regression curve representing a correlation between the historical data of the target sensor and the historical data of the reference sensor using the polynomial regression model and the Bayesian model
set a credible interval based on the posterior distribution of the regression coefficient and the error term of the regression curve, and set control lines of data of the target sensor using the credible interval
determine an accuracy of the target sensor based on current data of the target sensor and the control lines
Under the broadest reasonable interpretation of the limitations, these limitations are best characterized as representing mathematical relationships - see MPEP § 2106.04(a)(2)(I)(A).
Dependent claims 2 & 7 recite, in part, determine the historical data of the reference sensor using a distance correlation between the historical data of the target sensor and historical data of a plurality of sensors not selected as the target sensor.
Dependent claims 3 & 8 recite, in part, replace a prior distribution determined based on the polynomial regression model with a posterior distribution of a previous polynomial regression model, and set a likelihood function.
Dependent claims 4 & 9 recite, in part, validate the posterior distribution of the regression coefficient and the error term of the regression curve based on a preset method.
Dependent claims 5 & 10 recite, in part, set the credible interval based on a highest posterior density value having a preset percentage (%) of an expected value of the posterior distribution of the regression coefficient and the error term of the regression curve, and set the control lines of the data of the target sensor using lower and upper boundary values of the posterior distribution of the regression coefficient and the error term of the regression curve, with the lower and upper boundary values corresponding to lower and upper limits of the credible interval, respectively.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 6 accordingly, and hence are nonetheless directed towards fundamentally the same mathematical concept abstract idea grouping as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites a device comprising: at least one processor, and at least one memory coupled to the at least one processor. Claim 6 recites a computing device comprising at least one processor and at least one memory. Claim 11 recites a non-transitory computer-readable storage medium storing instructions of executing the method of claim 6. The instant disclosure does not require any specific hardware for the computer/processor and corresponding hardware (see the instant disclosure on p. 8 lines 10-21). The use of a device comprising: at least one processor, and at least one memory coupled to the at least one processor or a non-transitory computer-readable storage medium storing instructions, in this case to determine an operational status of a sensor, only recites the computer and corresponding hardware as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 1 recites a device comprising: at least one processor, and at least one memory coupled to the at least one processor. Claim 6 recites a computing device comprising at least one processor and at least one memory. Claim 11 recites a non-transitory computer-readable storage medium storing instructions of executing the method of claim 6. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-11 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claims 6-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Berger et al., Bayesian Sensor Calibration, 22(20) IEEE SENSORS JOURNAL 19384- 19399 (Oct. 15, 2022)[hereinafter Berger].
Claim 6 is rejected because Berger teaches on all elements of the claim:
a method for determining an operational status of a sensor, comprising is taught in the § I. Introduction on p. 19384 col 1 and § I. Introduction on p. 19385 col 2 (teaching on a system for determining an accuracy goal, X(Vtmin) in the example, (treated as synonymous to an operational status) of a sensor for calibration)
determining initial values of parameters of a Bayesian model and a degree of a polynomial regression model based on historical data of a target sensor and historical data of a reference sensor; inferring a posterior distribution of a regression coefficient and an error term of a regression curve representing a correlation between the historical data of the target sensor and the historical data of the reference sensor using the polynomial regression model and the Bayesian model is taught in the § I. Introduction on p. 19385 col 2 - p. 19384 col 1 and § B. From Prior to Posterior on p. 19387 col 1 (teaching on utilizing a Bayesian regression technique wherein one variable is described by a linear combination of other variables (i.e. a polynomial regression curve), with the goal of obtaining the posterior probability of the regression coefficients by modeling x-specimens of prior sensors and updating with new data of the new specimen sensor)
setting a credible interval based on the posterior distribution of the regression coefficient and the error term of the regression curve, and is taught in the § B. From Prior to Posterior on p. 19387 col 1 (teaching on a set univariate normal distribution interval (i.e. the credible interval) based on the posterior distribution of the regression curve)
setting control lines of data of the target sensor using the credible interval is taught in the § B. From Prior to Posterior on p. 19388 col 1 and C. Calibration at a Single Temperature on p. 19392 col 2 (teaching on setting an updated probability distribution of the accuracy goal, X(Vtmin) in the example, for the new specimen sensor (i.e.. target sensor))
determining an accuracy of the target sensor based on current data of the target sensor and the control lines; and is taught in the C. Calibration at a Single Temperature on p. 19392 col 1-2 (teaching on determining the accuracy of the sensor, X(Vtmin) in the example, utilizing the Bayesian regression technique)
controlling an operational status of the target sensor based on the accuracy is taught in the § I. Introduction on p. 19384 col 1 and C. Calibration at a Single Temperature on p. 19392 col 2 (teaching on calibrating (treated as synonymous to controlling an operational status) the sensor based on the determined accuracy)
As per claim 7, Berger discloses all of the limitations of claim 6. Berger also discloses the following:
determining the historical data of the reference sensor using a distance correlation between the historical data of the target sensor and historical data of a plurality of sensors not selected as the target sensor is taught in the § I. Introduction on p. 19385 col 2 - p. 19384 col 1, § B. From Prior to Posterior on p. 19387 col 1, and Fig. 2 on p. 19387 (teaching on utilizing a Bayesian regression technique wherein the distance between the new specimen sensor (i.e.. target data) and historical data is managed by evaluating historical data prediction accuracy for the new data utilizing the posterior predictive distribution)
As per claim 8, Berger discloses all of the limitations of claim 6. Berger also discloses the following:
replacing a prior distribution determined based on the polynomial regression model with a posterior distribution of a previous polynomial regression model, and set a likelihood function is taught in the § B. From Prior to Posterior on p. 19387 col 1-2 (teaching on the process for replacing the prior distribution with the posterior distribution in Bayesian regression)
As per claim 9, Berger discloses all of the limitations of claim 6. Berger also discloses the following:
validating the posterior distribution of the regression coefficient and the error term of the regression curve based on a preset method is taught in the § A. Comments on the Present Gaussian Approach on p. 19393 col 1 (teaching on a 14-fold cross validation as a quality measure of the Bayesian inference)
As per claim 10, Berger discloses all of the limitations of claim 6. Berger also discloses the following:
setting the credible interval based on a highest posterior density value having a preset percentage (%) of an expected value of the posterior distribution of the regression coefficient and the error term of the regression curve, and set the control lines of the data of the target sensor using lower and upper boundary values of the posterior distribution of the regression coefficient and the error term of the regression curve, with the lower and upper boundary values corresponding to lower and upper limits of the credible interval, respectively is taught in the Fig. 2 on p. 19387 (teaching on applying the highest posterior density (HPD represented by the shaded purple and dashed ellipse) for the 2D multivariate Gaussian prior distribution represents a bivariate normal distribution wherein based on calibration data obtained with the new specimen, p0(w) is updated into the narrower posterior distribution p1(w|X,y) of the specimen’s response parameter vector)
Claim Rejections - 35 USC § 103
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Berger et al., Bayesian Sensor Calibration, 22(20) IEEE Sensors Journal 19384- 19399 (Oct. 15, 2022)[hereinafter Berger] in view of Malvar Maua et al. (US Patent Application No. 2023/0282316)[hereinafter Malvar Maua].
As per claim 1, Berger teaches on the following limitations of the claim:
a device for determining an operational status of a sensor, comprising is taught in the § I. Introduction on p. 19384 col 1 and § I. Introduction on p. 19385 col 2 (teaching on a system for determining an accuracy goal, X(Vtmin) in the example, (treated as synonymous to an operational status) of a sensor for calibration)
determine initial values of parameters of a Bayesian model and a degree of a polynomial regression model based on historical data of the target sensor and historical data of a reference sensor; infer a posterior distribution of a regression coefficient and an error term of a regression curve representing a correlation between the historical data of the target sensor and the historical data of the reference sensor using the polynomial regression model and the Bayesian model is taught in the § I. Introduction on p. 19385 col 2 - p. 19384 col 1, § A. Definitions on p. 19386 col 2, § B. From Prior to Posterior on p. 19387 col 1 and Fig. 2, and Fig. 4. on p. 19390 (teaching on utilizing a Bayesian regression technique from a polynomial regression model of multiple sensor variables with a determined degree of 4 wherein the goal is obtaining the posterior probability of the regression coefficients by modeling x-specimens of prior sensors and updating with new data of the new specimen sensor and using the output of the Bayesian model and polynomial regression model to determine an error between measured and predicted values))
set a credible interval based on the posterior distribution of the regression coefficient and the error term of the regression curve, and is taught in the § B. From Prior to Posterior on p. 19387 col 1 (teaching on a set univariate normal distribution interval (i.e. the credible interval) based on the posterior distribution of the regression curve)
set control lines of data of the target sensor using the credible interval is taught in the § B. From Prior to Posterior on p. 19388 col 1 and C. Calibration at a Single Temperature on p. 19392 col 2 (teaching on setting an updated probability distribution of the accuracy goal, X(Vtmin) in the example, for the new specimen sensor (i.e.. target sensor))
determine an accuracy of the target sensor based on current data of the target sensor and the control lines; and is taught in the C. Calibration at a Single Temperature on p. 19392 col 1-2 (teaching on determining the accuracy of the sensor, X(Vtmin) in the example, utilizing the Bayesian regression technique)
Berger fails to teach the following limitation of claim 1. Malvar Maua, however, does teach the following:
at least one processor, and at least one memory coupled to the at least one processor, wherein the at least one memory is configured to provide the at least one processor with instructions which, when executed, causes the at least one processor to is taught in the Detailed Description in ¶ 0027 and ¶ 0138-140 (teaching on performing a Bayesian regression technique for monitoring a sensor on a computer with a processor, memory, and corresponding hardware)
One of ordinary skill in the art would have recognized that applying the known technique of utilizing a computer to perform the Bayesian regression analysis of Malvar Maua would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Malvar Maua to the Bayesian regression teachings of Berger would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows “those skilled in the art will also appreciate that the subject matter described herein can be practiced with various computer system configuration” (Malvar Maua in the Detailed Description in ¶ 0138) and would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the automation of the mathematical concept.
Claim 6 is rejected under the same rational.
As per claim 2, the combination of Berger and Malvar Maua discloses all of the limitations of claim 1. Berger also discloses the following:
the device of claim 1, wherein the at least one processor is further configured to determine the historical data of the reference sensor using a distance correlation between the historical data of the target sensor and historical data of a plurality of sensors not selected as the target sensor is taught in the § I. Introduction on p. 19385 col 2 - p. 19384 col 1, § B. From Prior to Posterior on p. 19387 col 1, and Fig. 2 on p. 19387 (teaching on utilizing a Bayesian regression technique wherein the distance between the new specimen sensor (i.e.. target data) and historical data is managed by evaluating historical data prediction accuracy for the new data utilizing the posterior predictive distribution)
Claim 7 is rejected under the same rational.
As per claim 3, the combination of Berger and Malvar Maua discloses all of the limitations of claim 1. Berger also discloses the following:
wherein the at least one processor is further configured to replace a prior distribution determined based on the polynomial regression model with a posterior distribution of a previous polynomial regression model, and set a likelihood function is taught in the § B. From Prior to Posterior on p. 19387 col 1-2 (teaching on the process for replacing the prior distribution with the posterior distribution in Bayesian regression)
Claim 8 is rejected under the same rational.
As per claim 4, the combination of Berger and Malvar Maua discloses all of the limitations of claim 1. Berger also discloses the following:
wherein the at least one processor is further configured to validate the posterior distribution of the regression coefficient and the error term of the regression curve based on a preset method is taught in the § A. Comments on the Present Gaussian Approach on p. 19393 col 1 (teaching on a 14-fold cross validation as a quality measure of the Bayesian inference)
Claim 9 is rejected under the same rational.
As per claim 5, the combination of Berger and Malvar Maua discloses all of the limitations of claim 1. Berger also discloses the following:
wherein the at least one processor is further configured to: set the credible interval based on a highest posterior density value having a preset percentage (%) of an expected value of the posterior distribution of the regression coefficient and the error term of the regression curve, and set the control lines of the data of the target sensor using lower and upper boundary values of the posterior distribution of the regression coefficient and the error term of the regression curve, with the lower and upper boundary values corresponding to lower and upper limits of the credible interval, respectively is taught in the Fig. 2 on p. 19387 (teaching on applying the highest posterior density (HPD represented by the shaded purple and dashed ellipse) for the 2D multivariate Gaussian prior distribution represents a bivariate normal distribution wherein based on calibration data obtained with the new specimen, p0(w) is updated into the narrower posterior distribution p1(w|X,y) of the specimen’s response parameter vector)
Claim 10 is rejected under the same rational.
As per claim 11, the combination of Berger and Malvar Maua discloses the limitations of claim 6. Berger fails to teach the following limitation of claim 11. Malvar Maua, however, does teach the following:
a non-transitory computer-readable storage medium storing instructions of executing the method of claim 6 is taught in the Detailed Description in ¶ 0027 and ¶ 0138-140 (teaching on performing a Bayesian regression technique for monitoring a sensor on a computer with a processor, memory, and corresponding hardware)
One of ordinary skill in the art would have recognized that applying the known technique of utilizing a computer to perform the Bayesian regression analysis of Malvar Maua would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Malvar Maua to the Bayesian regression teachings of Berger would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows “those skilled in the art will also appreciate that the subject matter described herein can be practiced with various computer system configuration” (Malvar Maua in the Detailed Description in ¶ 0138) and would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the automation of the mathematical concept.
Response to Arguments
Applicant's arguments filed with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the application of the abstract idea on a computer necessarily ties the claim to a particular machine. Examiner disagrees. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions). Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)(see MPEP § 2106.05(b)(I) for more information).
Applicant’s arguments with respect to 35 USC § 102 have been considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Malvar Maua, as per the rejection above.
Applicant's arguments filed with respect to 35 USC § 103 have been fully considered but they are not persuasive. Applicant asserts that Berger merely discloses a standard Bayesian framework wherein the key feature of Berger is that the probability distribution of the initial sensor response parameter vector for the recursive update can be determined as a multivariate t-distribution of the response parameter vector distribution for many sensors, or simply as a multivariate normal distribution where all vectors have the same mean and the same covariance. Applicant noted that while Berger discloses the optimization of initial parameter values of a Bayesian model, Berger fails to disclose all other configurations, including the selection of the order of a polynomial regression model. Examiner disagrees. Berger teaches on Bayesian regression technique relying on the unsharply defined prior knowledge p0(w) determined utilizing a polynomial regression model of multiple sensor variables with a determined degree of 4. Berger then teaches on using the output of the Bayesian model and polynomial regression model to determine an error between measured and predicted values. After processing the historical sensor variables through first the polynomial model and then the Bayesian model, the system compares the final output against the physical measurements to calculate the exact deviation between predicted and measured values. The rejection is therefore sustained.
Examiner has provided a clarifying citation in the rejection above (not considered a new ground for rejection under MPEP § 1207.03(a)(II) Factual Situations That Do Not Constitute a New Ground of Rejection: 1. Citing a different portion of a reference to elaborate upon that which has been cited previously).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bartel et al., Force calibration using errors-in-variables regression and Monte Carlo uncertainty evaluation, 53 Metrologia 965-980 (2016) teaching on an errors-in-variables regression for sensor calibration wherein degree of the polynomial to use for the calibration function is selected based on two criteria used in conjunction: a numerical model selection Bayesian Information Criterion and the examination of plots of residuals in the § 3.2. Solution on p. 967-968 and § 4.3. Comparison results on p. 973-974
THIS ACTION IS MADE FINAL. 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 JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
