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 .
The following action is in response to the preliminary amendment of 01/23/2023.
By the amendment, claims 1-12 have been amended.
Claims 1-12 are pending and have been considered below.
Drawings
The drawings are objected to for reciting non english words (Fig. 3). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification amendment of 01/23/2023 has been reviewed and entered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
“a calibration model device configured to provide a data-based calibration model” (claim 8, claim 10)
“a calibration device configured to apply a calibration function” (claim 8)
“the calibration model unit is configured to, during a calibration: acquire training data sets” (claim 9).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 6, claim 6 recites “to provide a sensor variable” in line 3. However, parent claim 1 already recites obtaining “the sensor variable”. It is unclear if the sensor variable of claim 6 is a new sensor variable or the same sensor variable as parent claim 1.
Regarding claim 9, claim 9 recites “the calibration model unit” in line 2. There is lack of antecedent basis for this limitation in the claim.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 6 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Regarding claim 6, claim 6 recites limitations (“wherein the at least one calibration parameter is configured to parameterize a calibration function to be applied to the electrical measured variable to provide a sensor variable”) that fail to further limit parent claim 1 (“applying a calibration function parameterized with the at least one calibration parameter to the electrical measured variable to obtain the sensor variable”). Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 and 3-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding claim 1:
Step 1, MPEP 2106.03:
These limitations have been determined, under Step 1, to be statutory categories of invention:
A method for measuring a physical variable with a sensor component and for providing a corresponding sensor variable [..]
Step 2A Prong One MPEP 2106.04, 2106.04(a):
These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations, MPEP 2106.04(a)(2)(I):
[..] applying a calibration function parameterized with the at least one calibration parameter to the electrical measured variable [..]
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] the sensor component having (i) a measuring transducer [..]
[..] and (ii) at least one disturbance variable sensor [..]
[..] a data-based calibration model trained to map [..]
These limitations represent, under Step 2A Prong Two, mere instructions to apply at a high level of generality, MPEP 2106.05:
[..] map the at least one disturbance variable to at least one calibration parameter [..]
[..] using the data-based calibration model to determine at least one calibration parameter depending on the acquired at least one disturbance variable; [..]
These limitations represent, under Step 2A Prong Two, mere data gathering, MPEP 2106.05:
[.. ] provide an electrical measured variable that depends on the physical variable to which the sensor component is exposed [..]
[..] acquire at least one disturbance variable [..]
[..] providing a data-based calibration model [..]
[..] acquiring (i) the electrical measured variable representing the physical variable to be measured and (ii) the at least one disturbance variable; [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] the sensor component having (i) a measuring transducer [..]
[..] and (ii) at least one disturbance variable sensor [..]
[..] a data-based calibration model trained to map [..]
These limitations are considered, under Step 2B, mere instructions to apply to obtain a solution/outcome, MPEP 2106.05(f):
[..] map the at least one disturbance variable to at least one calibration parameter [..]
[..] using the data-based calibration model to determine at least one calibration parameter depending on the acquired at least one disturbance variable; [..]
These limitations are considered, under Step 2B, insignificant extra-solution activity of data gathering/selecting a particular type of data, MPEP 2106.05(g):
[.. ] provide an electrical measured variable that depends on the physical variable to which the sensor component is exposed [..]
[..] acquire at least one disturbance variable [..]
[..] providing a data-based calibration model [..]
[..] acquiring (i) the electrical measured variable representing the physical variable to be measured and (ii) the at least one disturbance variable; [..]
Regarding claim 3 (including limitations of base claim 2):
Step 1, MPEP 2106.03:
These limitations have been determined, under Step 1, to be statutory categories of invention:
A method for calibrating a sensor component with a data-based calibration model [..] (claim 2)
Step 2A Prong One MPEP 2106.04, 2106.04(a):
These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations, MPEP 2106.04(a)(2)(I):
[..] wherein the data-based calibration model is trained with a loss function indicating a difference between the desired sensor variable and a sensor variable resulting from applying the data-based calibration model to the electrical measured variable [..] (claim 3)
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] the sensor component having (i) a measuring transducer [..] (claim 2)
[..] and (ii) at least one disturbance variable sensor [..] (claim 2)
[..] a data-based calibration model [..] (claim 2)
These limitations represent, under Step 2A Prong Two, mere instructions to apply at a high level of generality, MPEP 2106.05:
[..] training the data-based calibration model with the training data sets, to map the at least one disturbance variable to at least one calibration parameter [..] (claim 2)
These limitations represent, under Step 2A Prong Two, mere data gathering, MPEP 2106.05:
[.. ] provide an electrical measured variable that depends on a physical variable to which the sensor component is exposed [..] (claim 2)
[..] acquire at least one disturbance variable [..] (claim 2)
[..] providing a data-based calibration model [..] (claim 2)
[..] acquiring training data sets at a plurality of evaluation times, the training data set at each respective evaluation time being acquired by:
applying the physical variable to the sensor component;
providing a corresponding desired sensor variable to represent a value of the physical variable being applied; and
acquiring the electrical measured variable representing the physical variable and acquiring the at least one disturbance variable with the sensor component at the respective evaluation time; [..] (claim 2)
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] the sensor component having (i) a measuring transducer [..] (claim 2)
[..] and (ii) at least one disturbance variable sensor [..] (claim 2)
[..] a data-based calibration model [..] (claim 2)
These limitations are considered, under Step 2B, mere instructions to apply to obtain a solution/outcome, MPEP 2106.05(f):
[..] training the data-based calibration model with the training data sets, to map the at least one disturbance variable to at least one calibration parameter [..] (claim 2)
These limitations are considered, under Step 2B, insignificant extra-solution activity of data gathering/selecting a particular type of data, MPEP 2106.05(g):
[.. ] provide an electrical measured variable that depends on a physical variable to which the sensor component is exposed [..] (claim 2)
[..] acquire at least one disturbance variable [..] (claim 2)
[..] providing a data-based calibration model [..] (claim 2)
[..] acquiring training data sets at a plurality of evaluation times, the training data set at each respective evaluation time being acquired by:
applying the physical variable to the sensor component;
providing a corresponding desired sensor variable to represent a value of the physical variable being applied; and
acquiring the electrical measured variable representing the physical variable and acquiring the at least one disturbance variable with the sensor component at the respective evaluation time; [..] (claim 2)
Regarding claim 4:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] wherein the data-based calibration model is also configured to map the electrical measured variable to the at least one calibration parameter [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] wherein the data-based calibration model is also configured to map the electrical measured variable to the at least one calibration parameter [..]
Regarding claim 5:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to apply the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] wherein the at least one disturbance variable indicates one of a temperature, a magnetic field strength, an acting electromagnetic radiation, an acceleration effect of mechanical disturbances, vibrations of the mechanical disturbances, and an acting electric field [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, as reciting only the idea of a solution/outcome, MPEP 2106.05(f):
[..] wherein the at least one disturbance variable indicates one of a temperature, a magnetic field strength, an acting electromagnetic radiation, an acceleration effect of mechanical disturbances, vibrations of the mechanical disturbances, and an acting electric field [..]
Regarding claim 6:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations, MPEP 2106.04(a)(2)(I):
[..] wherein the at least one calibration parameter is configured to parameterize a calibration function to be applied to the electrical measured variable to provide a sensor variable [..]
Step 2A Prong Two, MPEP 2106.04(d):
All limitations are part of the abstract idea.
Step 2B, MPEP 2106.05:
All limitations are part of the abstract idea.
Regarding claim 7:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to apply at a high level of generality, MPEP 2106.05:
[..] wherein the data-based calibration model is formed with one of a neural network, a probabilistic regression model, a Bayesian neural network, and a variational autoencoder [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] wherein the data-based calibration model is formed with one of a neural network, a probabilistic regression model, a Bayesian neural network, and a variational autoencoder [..]
Regarding claim 8:
Step 1, MPEP 2106.03:
These limitations have been determined, under Step 1, to be statutory categories of invention:
A sensor component for measuring a physical variable [..]
Step 2A Prong One MPEP 2106.04, 2106.04(a):
These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations, MPEP 2106.04(a)(2)(I):
[..] apply a calibration function parameterized with the at least one calibration parameter to the electrical measured variable to provide a sensor variable [..]
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] a measuring transducer [..]
[..] at least one disturbance variable sensor [..]
[..] a calibration model device [..]
[..] a calibration device [..]
These limitations represent, under Step 2A Prong Two, mere instructions to apply at a high level of generality, MPEP 2106.05:
[..] data-based calibration model trained to determine at least one calibration parameter depending on the acquired at least one disturbance variable; [..]
These limitations represent, under Step 2A Prong Two, mere data gathering, MPEP 2106.05:
[.. ] provide an electrical measured variable that depends on the physical variable to which the sensor component is exposed [..]
[..] acquire at least one disturbance variable [..]
[..] provide a data-based calibration model [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] a measuring transducer [..]
[..] at least one disturbance variable sensor [..]
[..] a calibration model device [..]
[..] a calibration device [..]
These limitations are considered, under Step 2B, mere instructions to apply to obtain a solution/outcome, MPEP 2106.05(f):
[..] data-based calibration model trained to determine at least one calibration parameter depending on the acquired at least one disturbance variable; [..]
These limitations are considered, under Step 2B, insignificant extra-solution activity of data gathering/selecting a particular type of data, MPEP 2106.05(g):
[.. ] provide an electrical measured variable that depends on the physical variable to which the sensor component is exposed [..]
[..] acquire at least one disturbance variable [..]
[..] provide a data-based calibration model [..]
Regarding claim 9:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] the calibration model unit [..]
These limitations represent, under Step 2A Prong Two, mere instructions to apply at a high level of generality, MPEP 2106.05:
[..] train the data-based calibration model, with the training data sets, to map the at least one disturbance variable to at least one calibration parameter [..]
These limitations represent, under Step 2A Prong Two, mere data gathering, MPEP 2106.05:
[..] acquiring training data sets at a plurality of evaluation times, the training data set at each respective evaluation time being acquired by:
receiving a desired sensor variable that represents a value of the physical variable currently acting on the sensor component; and
acquiring an electrical measured variable representing the physical variable and acquiring the at least one disturbance variable with the sensor component at the respective evaluation time; [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] the calibration model unit [..]
These limitations are considered, under Step 2B, mere instructions to apply to obtain a solution/outcome, MPEP 2106.05(f):
[..] train the data-based calibration model, with the training data sets, to map the at least one disturbance variable to at least one calibration parameter [..]
These limitations are considered, under Step 2B, insignificant extra-solution activity of data gathering/selecting a particular type of data, MPEP 2106.05(g):
[..] acquiring training data sets at a plurality of evaluation times, the training data set at each respective evaluation time being acquired by:
receiving a desired sensor variable that represents a value of the physical variable currently acting on the sensor component; and
acquiring an electrical measured variable representing the physical variable and acquiring the at least one disturbance variable with the sensor component at the respective evaluation time; [..]
Regarding claim 10:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
These limitations represent, under Step 2A Prong One, mathematical concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations, MPEP 2106.04(a)(2)(I):
[..] wherein the calibration model device is configured to use, as a loss function for training the data-based calibration model, a difference between the desired sensor variable and the sensor variable [..]
Step 2A Prong Two, MPEP 2106.04(d):
All limitations are part of the abstract idea.
Step 2B, MPEP 2106.05:
All limitations are part of the abstract idea.
Regarding claim 11:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] wherein the method is carried out by a computer program having program code that is run on a data processing device [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] wherein the method is carried out by a computer program having program code that is run on a data processing device [..]
Regarding claim 12:
Step 1, MPEP 2106.03:
Analysis of respective parent is incorporated.
Step 2A Prong One MPEP 2106.04, 2106.04(a):
Analysis of respective parent is incorporated.
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
[..] wherein the computer program is stored on a machine-readable storage medium [..]
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
[..] wherein the computer program is stored on a machine-readable storage medium [..]
Claim Rejections - 35 USC § 102
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 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 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gu, Jingjing, et al. "Dynamic measurement and data calibration for aerial mobile IoT." IEEE Internet of Things Journal 7.6 (2020): 5210-5219. [“GU”]
Regarding claim 1, GU discloses a method for measuring a physical variable with a sensor component and for providing a corresponding sensor variable (page 5212 col 2 ¶3-4: Problem Statement, measuring physical variable and adjusting/calibrating based on measured environmental factors, page 5213 col 2 ¶2: Data Calibration Design, ¶5212 Fig. 1), the sensor component having (i) a measuring transducer configured to provide an electrical measured variable that depends on the physical variable to which the sensor component is exposed and (ii) at least one disturbance variable sensor configured to acquire at least one disturbance variable (page 5214 col 1 ¶1: a number of sensors are configured to provide electrical measured variable si , and acquired environmental disturbance factors ei), the method comprising:
providing a data-based calibration model trained to map the at least one disturbance variable to at least one calibration parameter (page 5213 col 2 ¶2: Data Calibration Design, mapping provided by neural network model DC-NN, page 5214 col 1 ¶4-5: Correlation Layer, equation (4) provides mapping of si and ei as external-correlation representation xex, page 5214 col 2 ¶1-2: Interaction Layer, equation (5) provides calibration parameter zp based on xin and xex as well as a weight matrix and bias vector);
acquiring (i) the electrical measured variable representing the physical variable to be measured and (ii) the at least one disturbance variable (page 5214 col 1 ¶1: Input Layer, inputs measured values si and scaled environmental disturbance factors ei);
using the data-based calibration model to determine at least one calibration parameter depending on the acquired at least one disturbance variable (page 5214 col 2 ¶1-2: Interaction Layer, zp); and
applying a calibration function parameterized with the at least one calibration parameter to the electrical measured variable to obtain the sensor variable (page 5214 col 2 ¶3: Output Layer, equation (6) is a calibration function to obtain predicated sensor variable
v
^
i using parameterized calibration parameter zL and output weights and bias).
Regarding claim 2, GU discloses a method for calibrating a sensor component with a data-based calibration model (page 5212 col 2 ¶3-4: Problem Statement, measuring physical variable and adjusting/calibrating based on measured environmental factors, page 5213 col 2 ¶2: Data Calibration Design, ¶5212 Fig. 1), the sensor component having (i) a measuring transducer configured to provide an electrical measured variable that depends on a physical variable to which the sensor component is exposed and (ii) at least one disturbance variable sensor configured to acquire a disturbance variable (page 5214 col 1 ¶1: a number of sensors are configured to provide electrical measured variable si , and acquired environmental disturbance factors ei), the method comprising:
acquiring training data sets at a plurality of evaluation times (page 5214 col 1 ¶1: measured values S from each sample S of number of sensors n, page 5215 col 2 ¶3: ex. sampling rate of sensors 100 Hz), the training data set at each respective evaluation time being acquired by:
applying the physical variable to the sensor component (page 5214 col 1 ¶1: measured values S from each sample S of number of sensors n, page 5214 col 2 ¶4-5: Model Inference, sampling cases m across number of sensors n);
providing a corresponding desired sensor variable to represent a value of the physical variable being applied (page 5214 col 1 ¶1: to predict targeted values vi, page 5214 col 2 ¶4-5: Model Inference, provided targeted values vi); and
acquiring the electrical measured variable representing the physical variable and acquiring the at least one disturbance variable with the sensor component at the respective evaluation time (page 5214 col 1 ¶1: measured values S from each sample S of number of sensors n represented as variable si and environmental factor disturbance variable for each sample represented as ei, page 5215 col 2 ¶3: ex. sampling rate of sensors 100 Hz); and
training the data-based calibration model with the training data sets, to map the at least one disturbance variable to at least one calibration parameter (page 5214 col 2 ¶4-5: Model Inference, training model DC-NN, page 5213 col 2 ¶2: Data Calibration Design, mapping provided by neural network model DC-NN, page 5214 col 1 ¶4-5: Correlation Layer, equation (4) provides mapping of si and ei as external-correlation representation xex, page 5214 col 2 ¶1-2: Interaction Layer, equation (5) provides calibration parameter zp based on xin and xex as well as a weight matrix and bias vector).
Regarding claim 3, GU discloses the method according to claim 2, wherein the data-based calibration model is trained with a loss function indicating a difference between the desired sensor variable and a sensor variable resulting from applying the data-based calibration model to the electrical measured variable (page 5214 col 2 ¶4-5, page 5215 col 1 ¶1-3: Model Inference, equations (7) and (8) are loss functions for training DC-NN model indicating difference between desired sensor variable vi and sensor variable from model
v
^
i).
Regarding claim 4, GU discloses the method according to claim 1, wherein the data-based calibration model is also configured to map the electrical measured variable to the at least one calibration parameter (page 5214 col 1 ¶4-5: Correlation Layer, equation (2) provides mapping of si as internal-correlation representation xin, equation (4) provides mapping of si and ei as external-correlation representation xex, page 5214 col 2 ¶1-2: Interaction Layer, equation (5) provides calibration parameter zp based on xin and xex as well as a weight matrix and bias vector).
Regarding claim 5, GU discloses the method according to claim 1, wherein the at least one disturbance variable indicates one of a temperature, a magnetic field strength, an acting electromagnetic radiation, an acceleration effect of mechanical disturbances, vibrations of the mechanical disturbances, and an acting electric field (page 5210 col 1 ¶1: Abstract, group of factors affecting measurement includes examples such as temperature, humidity, pressure, wind movement).
Regarding claim 6, GU discloses the method according to claim 1, wherein the at least one calibration parameter is configured to parameterize a calibration function to be applied to the electrical measured variable to provide a sensor variable (page 5214 col 2 ¶3: Output Layer, equation (6) is a calibration function to obtain predicated sensor variable
v
^
i using parameterized calibration parameter zL and output weights and bias).
Regarding claim 7, GU discloses the method according to claim 1, wherein the data-based calibration model is formed with one of a neural network, a probabilistic regression model, a Bayesian neural network, and a variational autoencoder (page 5213 col 2 ¶2: Data Calibration Design, employ neural network).
Regarding claim 8, GU discloses a sensor component for measuring a physical variable (page 5212 col 2 ¶3-4: Problem Statement, measuring physical variable and adjusting/calibrating based on measured environmental factors, page 5213 col 2 ¶2: Data Calibration Design, ¶5212 Fig. 1), the sensor component comprising:
a measuring transducer configured to provide an electrical measured variable that depends on the physical variable to which the sensor component is exposed (page 5214 col 1 ¶1: a number of sensors are configured to provide electrical measured variable si , and acquired environmental disturbance factors ei);
at least one disturbance variable sensor configured to acquire at least one disturbance variable (page 5214 col 1 ¶1: a number of sensors are configured to provide electrical measured variable si , and acquired environmental disturbance factors ei);
a calibration model device configured to provide a data-based calibration model trained to determine at least one calibration parameter depending on the acquired at least one disturbance variable (page 5213 col 2 ¶2: Data Calibration Design, mapping provided by neural network model DC-NN, page 5214 col 1 ¶4-5: Correlation Layer, equation (4) provides mapping of si and ei as external-correlation representation xex, page 5214 col 2 ¶1-2: Interaction Layer, equation (5) provides calibration parameter zp based on xin and xex as well as a weight matrix and bias vector, page 5214 col 2 ¶1-2: Interaction Layer, zp); and
a calibration device configured to apply a calibration function parameterized with the at least one calibration parameter to the electrical measured variable to provide a sensor variable (page 5214 col 2 ¶3: Output Layer, equation (6) is a calibration function to obtain predicated sensor variable
v
^
i using parameterized calibration parameter zL and output weights and bias).
Regarding claim 9, GU discloses the sensor component according to claim 8, wherein the calibration model unit is configured to, during a calibration:
acquire training data sets at a plurality of evaluation times (page 5214 col 1 ¶1: measured values S from each sample S of number of sensors n, page 5215 col 2 ¶3: ex. sampling rate of sensors 100 Hz), training data set at each respective evaluation time being acquired by:
receiving a desired sensor variable that represents a value of the physical variable currently acting on the sensor component (page 5214 col 1 ¶1: to predict targeted values vi, page 5214 col 2 ¶4-5: Model Inference, provided targeted values vi); and
acquiring an electrical measured variable representing the physical variable and acquiring the at least one disturbance variable with the sensor component at the respective evaluation time (page 5214 col 1 ¶1: measured values S from each sample S of number of sensors n represented as variable si and environmental factor disturbance variable for each sample represented as ei, page 5215 col 2 ¶3: ex. sampling rate of sensors 100 Hz); and
train the data-based calibration model, with the training data sets, to map the at least one disturbance variable to the corresponding at least one calibration parameter (page 5214 col 2 ¶4-5: Model Inference, training model DC-NN, page 5213 col 2 ¶2: Data Calibration Design, mapping provided by neural network model DC-NN, page 5214 col 1 ¶4-5: Correlation Layer, equation (4) provides mapping of si and ei as external-correlation representation xex, page 5214 col 2 ¶1-2: Interaction Layer, equation (5) provides calibration parameter zp based on xin and xex as well as a weight matrix and bias vector).
Regarding claim 10, GU discloses the sensor component according to claim 8, wherein the calibration model device is configured to use, as a loss function for training the data-based calibration model, a difference between the desired sensor variable and the sensor variable (page 5214 col 2 ¶4-5, page 5215 col 1 ¶1-3: Model Inference, equations (7) and (8) are loss functions for training DC-NN model indicating difference between desired sensor variable vi and sensor variable from model
v
^
i).
Regarding claim 11, GU discloses the method according to claim 1, wherein the method is carried out by a computer program having program code that is run on a data processing device (page 5212 col 1 ¶6-7, col 2 ¶1-2: System Overview).
Regarding claim 12, GU discloses the method according to claim 11, wherein the computer program is stored on a machine-readable storage medium (page 5212 col 1 ¶6-7, col 2 ¶1-2: System Overview).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141