DETAILED ACTION
This office action is in response to communication filed on September 12, 2025.
Response to Amendment
Amendments filed on September 12, 2025 have been entered.
Claims 1, 3-4, 12 and 15 have been amended.
Claims 2 and 13 remain canceled.
Claims 7-11 have been canceled.
Claim 16 has been added.
Claims 1, 3-6, 12 and 14-16 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 7), filed on 09/12/2025, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn. However, upon further consideration, new objections to the claims are presented below to address additional informalities.
Applicant’s arguments, see Remarks (p. 7), filed on 09/12/2025, with respect to the rejections of claims 1, 3-6, 12 and 14-15 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph have been fully considered. In view of the amendments to the claims addressing the issues raised in the previous office action, the rejections of the claims have been withdrawn.
Regarding claim 12, the examiner submits that the determination of the sensor measured values from the acoustic sensor being faulty is interpreted to be based on all the listed characteristics (i.e., a residual of the least squares regression, information about a termination status of the least squares regression, and at least one further item of information about the least squares regression) in light of the specification details (see [0019], [0021], [0033], [0043]) and new claim 16.
Applicant’s arguments, see Remarks (p. 7-11), filed on 09/12/2025, with respect to the rejection of claims 1, 3-6, 12 and 14-15 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 8) that the claims and application are directed to an apparatus and automated method for evaluating sensor measured values. At Alice step one, the Office Action asserts that the claims are related to a mental process and/or using mathematical concepts to evaluate data and obtain a result. However, the claims as a whole go beyond data evaluation in the abstract.
This argument is not persuasive.
First, the examiner submits that, as indicated in the previous office action (mailed on 06/12/2025), the claimed invention, when considered as a whole, recites:
collecting data from any sensor (see specification at [0018], see also claims 1, 12 and 16) and a model function defined by a parameter vector (see specification at [0004] and [0014]), wherein at least one parameter of the parameter vector corresponds to the signal output by the sensor,
training a neural network with different parameters vectors and corresponding sensor data (see specification at [0015]),
using the trained neural network to estimate the parameter vector corresponding to actual measured data (see specification at [0015]),
using a least square regression to validate the success of the parameter vector in representing the actual measured data (e.g., by comparing residuals to thresholds, see specification at [0015]), and
outputting the parameter having the smallest error and additional information on the assessment (see specification at [0019]-[0025]).
Based on this, the examiner submits that the rejection indicates that the claims include limitations that, under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, which under the current guidance belong to abstract ideas (see MPEP 2106.04).
Regarding the argument about the claims and application being directed to an apparatus and automated method for evaluating sensor measured values, the examiner submits that the claimed invention (see rejection below for details):
recites an abstract idea (e.g., series of mental/mathematical steps used to manipulate data) (Step 2A – Prong One); and
includes additional elements that, when considered individually or in combination, append extra-solution activities (e.g., pre-solution and post-solution activities), generally link the use of a judicial exception to a particular technological environment or field of use (e.g., assessing whether sensor measured values are successful or unsuccessful, neural networks - see July 2024 Subject Matter Eligibility Examples, Example 47 – Anomaly Detection, claim 2) and add generic computer components (see specification at [0040]-[0041]) used to facilitate the application of the abstract idea, which under the current office guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and does not provide significantly more (Step 2B) when considering the claimed invention as a whole.
Furthermore, the examiner submits that evaluating sensor measured values, under the broadest reasonable interpretation in light of the specification, is accomplished by data manipulation using the series of mental/mathematical steps, and according to the current guidance (see MPEP 2106.04(a)(2)):
“It is important to note that a mathematical concept need not be expressed in mathematical symbols, because “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea) …”;
“Examples of mathematical relationships recited in a claim include: … iv. Organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook’s method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.”; and
“In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)”.
Applicant also argues (p. 8) that the claims specify a concrete apparatus comprising sensors, acquisition of sensor-measured values, and a computing and evaluation unit configured to perform automated evaluation against physical criteria. These steps are rooted in technology, are tied to real-world machines, and cannot be performed purely as mental processes because they require sensor hardware to capture measured values. Courts have consistently recognized that claims improving the functionality or reliability of sensors and technical systems are not abstract but directed to specific improvements in technology.
These arguments are not persuasive.
The examiner submits that the claimed invention recites:
generic computer components (see specification at [0041]) used to facilitate the application of the judicial exception, which as indicated in the MPEP: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more” (see MPEP 2106.05(f));
extra-solution activities (e.g., mere data gathering/outputting, type/source of data to be manipulated) using elements recited at a high level of generality (i.e., a sensor, an image sensor, see specification at [0002], [0018], [0026]-[0031], [0049]), which according to the Office guidance “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more” (see MPEP 2106.05(b)); and
a particular technological environment or field of use (see specification at [0002], [0018], [0026]-[0031], [0049]) and as indicated in the MPEP: “A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more” (see MPEP 2106.05(f)).
Moreover, the examiner submits that the rejection indicates that the claimed invention recites limitations that, under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or using mathematical concepts (e.g., checking that a convergence criterion is met and evaluating residuals can be performed by mental evaluations and/or mathematical concepts), and as indicated in the October 2019 Update: Subject Matter Eligibility: “Claims can recite a mental process even if they are claimed as being performed on a computer … The courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind” (p. 8, section “ii. A Claim That Requires A Computer May Still Recite A Mental Process”, par. 1).
Also, the examiner submits that according to the current Office’s guidance: “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), section I).
Regarding the argument about the Courts consistently recognizing that claims improving the functionality or reliability of sensors and technical systems are not abstract but directed to specific improvements in technology, the examiner submits that the claimed invention does not improve the functionality or reliability of sensors and technical systems, but instead:
uses any sensor (see specification at [0002], [0018], [0026]-[0031], [0049]) for extra-solution activities (e.g., mere data gathering) which according to the MPEP: “Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering … Selecting a particular data source or type of data to be manipulated …” (see MPEP 2106.05(g)); and
uses computer elements (see specification at [0041]) for facilitating the application of the judicial exception, which as explained in the MPEP: “The programmed computer or “special purpose computer” test of In re Alappat, 33 F.3d 1526, 31 USPQ2d 1545 (Fed. Cir. 1994) (i.e., the rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim for the “special purpose” of executing the algorithm or software) was also superseded by the Supreme Court’s Bilski and Alice Corp. decisions. Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (“[W]e note that Alappat has been superseded by Bilski, 561 U.S. at 605–06, and Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 110 USPQ2d 1976 (2014)”); Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (“An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”)” (see MPEP 2106, section I).
Applicant further argues (p. 8) that At Alice step two, even if an abstract idea were identified, the claims recite “significantly more” than the alleged idea. The apparatus evaluates measured values in an automated manner using at least one parameter estimated vector, a regression module, and an assessment model, thereby improving the reliability of technical systems and reducing the risk of operator error. This is not a generic application of evaluation rules but an integration of specific evaluation mechanisms into a technical architecture. The Federal Circuit has found eligibility in analogous circumstances, for example where claims improved the accuracy or efficiency of data acquisition and processing in a technical field. Here, the claimed invention provides a technological improvement in how sensor systems operate-by automatically filtering, validating, and ensuring trustworthy data-rather than merely performing evaluation “on paper.”
These arguments are not persuasive.
First, the examiner submits that, according to applicant’s own disclosure, the current application uses conventional techniques for data analysis (see specification at [0005]-[0008], [0010] and [0015]), which in accordance with the Office’s guidance: “Examples that the courts have indicated may not be sufficient to show an improvement to technology include: … iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” (see MPEP 2106.05(a)).
Furthermore, the examiner notes that combining neural networks and least square regressions for evaluating sensor data has been implemented before, as evidenced by applicant’s reference presented to the Office for consideration (see Zhang, Chuanwei, Shiyuan Liu, and Tielin Shi. “Metrology of deep trench structures in DRAM using FTIR reflectance spectrum.” 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications. Vol. 7160. SPIE, 2009).
Also, the examiner submits that as described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence: “Even if the judicial exception is narrow ( e.g., a particular mathematical formula or detailed mental process), the Court has held that a claim may not preempt that judicial exception” (see “III. Update on Certain Areas of the USPTO’s Patent Subject Matter Eligibility Guidance Applicable to AI Inventions”, section “A. Evaluation of Whether a Claim Is Directed to a Judicial Exception (Step 2A)”).
Moreover, the examiner submits that applicant’s invention can be applicable in many different fields as described in the specification (see [0002], [0018], [0026]-[0031], [0049]), which according to the MPEP: “A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more” (see MPEP 2106.05(f)).
Additionally, applicant argues (p. 9) that the claims are directed to a practical application under MPEP 2106.05. The recited elements improve the functioning of a sensor-based apparatus itself. By checking plausibility and consistency of measured values, the invention reduces erroneous input into control or diagnostic systems, thereby enhancing performance, safety, and efficiency. The claim integrates any abstract concepts into a practical application to improve a specific technological environment.
This argument is not persuasive.
The examiner submits that, as indicated above, the claimed invention seeks patent protection for a series of mental and mathematical steps to manipulate collected data, which as indicated in the current guidance:
“For data, mere “manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’” has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994))” (see MPEP 2106.05(c); see also July 2024 Subject Matter Eligibility Examples, Example 47 – Anomaly Detection, claim 2);
“It is important to note, the judicial exception alone cannot provide the improvement” (see MPEP 2106.05(a));
“a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two” (see MPEP 2106.04(d), section I); and
“An inventive concept “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.” Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, “we then ask, ‘[w]hat else is there in the claims before us?”) (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”). Instead, an “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)” (see MPEP 2106.05).
Applicant also argues (p. 9-10) that the claims provide an improvement to specific types of sensors as claimed. In particular, the system calculates a residual between an observed sensor value and a predicted sensor value (predicted using a regression model based on other sensor data). Based on the residual (how large or small it is), the system outputs a determination of whether the sensor data is trustworthy, potentially faulty, or anomalous. As described in the Applicants’ specification, the described device may be used for a spectral evaluation in high-resolution spectroscopy, for example spectroscopy based on tunable lasers … The described device may be used for a spectral evaluation of timeseries, such as for example of measured voltage/current, ultrasonic vibrations or the like, to check the state or establish the state of technical devices or apparatuses ... One example is the state monitoring of the current of a motor of unknown size and speed. The described device may also be used for an analysis of audio data, such as speech.
These arguments are not persuasive.
The examiner submits that applicant’s invention can be applicable in many different fields as described in the specification (see [0002], [0018], [0026]-[0031], [0049]) and as indicated in the arguments, and according to the MPEP:
“A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more” (see MPEP 2106.05(f)); and
“Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more” (see MPEP 2106.05(b)).
Moreover, applicant argues (p. 10-11) that Instead of a simple check, the claimed system and method provides an improved technique using a regression model to predict what a sensor’s value should be and taking into account the values from other sensors and applying a parameter vector to them … The improved system and method of the claims uses dynamic, model-based predictions tailored to the current state of the system. The system learns relationships between sensors … The system catches anomalies invisible to threshold checks … The system can quantify reliability precisely with residuals rather than crude binary flags … This more advanced and innovative method for evaluating sensor measured values is not a mental process or something that could be practically performed using, for example, pen and paper … the claims and application provide an unconventional technical solution to a technical problem. The claims improve upon a technical method, not, for example, taking a previous mental process and adding a computer. The claims are integrated into a practical application.
These arguments are not persuasive.
First, the examiner submits that, as indicated above and evidenced by applicant’s own disclosure, the current application uses conventional techniques for data analysis (see specification at [0005]-[0008], [0010] and [0015]), which in accordance with the Office’s guidance: “Examples that the courts have indicated may not be sufficient to show an improvement to technology include: … iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” (see MPEP 2106.05(a)).
In addition, the examiner notes that combining neural networks and least square regressions for evaluating sensor data has been implemented before, as evidenced by applicant’s reference presented to the Office for consideration (see Zhang, Chuanwei, Shiyuan Liu, and Tielin Shi. “Metrology of deep trench structures in DRAM using FTIR reflectance spectrum.” 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications. Vol. 7160. SPIE, 2009).
Furthermore, the examiner submits that using mathematical concepts and mental processes to manipulate data corresponds to the judicial exception (i.e., abstract idea) as explained in the Office’s guidance:
“A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (see MPEP 2106.04(a)(2), section “I. Mathematical Concepts”, subsection “C. Mathematical Calculations”); and
“the “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions” (see MPEP 2106.04(a)(2), section “III. Mental Processes”) (e.g., comparing residuals to thresholds can be performed in the mind).
Additionally, applicant argues (p. 11) that As opposed to claiming a result or resulting system, the claims recite specific steps/operations which accomplish a desired result. The Applicants assert that the USPTO and § 101 do not preclude protection for innovation in the field of evaluating sensor measured values. As such, the claims are integrated into a practical application of the exception and are subject matter eligible.
This argument is not persuasive.
The examiner submits that as described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence: “Even if the judicial exception is narrow ( e.g., a particular mathematical formula or detailed mental process), the Court has held that a claim may not preempt that judicial exception” (see “III. Update on Certain Areas of the USPTO’s Patent Subject Matter Eligibility Guidance Applicable to AI Inventions”, section “A. Evaluation of Whether a Claim Is Directed to a Judicial Exception (Step 2A)”).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/09/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “a sensor configured to detect sensor measured values for an evaluation of a chromatogram in gas chromatography … wherein a model function suitable for the least squares regression and configured to be defined by a parameter vector is provided for an evaluation of the sensor measured values of the sensor …” should read “a sensor configured to detect the sensor measured values for an evaluation of a chromatogram in gas chromatography … wherein a model function suitable for the least squares regression and configured to be defined by a parameter vector is provided for [[an]]the evaluation of the sensor measured values of the sensor …” in order to provide appropriate antecedence basis.
Claim language “wherein the evaluation unit includes an assessment module … the assessment module configured to output the success status, the information, and the at least one further item of information as a further sensor output signal … wherein if the evaluation is unsuccessful a fault message is output” should read “wherein the evaluation unit includes an assessment module … the assessment module configured to output the success status, the information about the termination status, and the at least one further item of information as a further sensor output signal … wherein if the evaluation is unsuccessful, a fault message is output” in order to clarify the recited subject matter and correct for minor informalities (i.e., add comma).
Appropriate correction is required.
Claim 12 is objected to because of the following informalities:
Claim language “receiving, from a sensor, sensor measured values comprising time-domain signals of a component or environment …” should read “receiving, from an acoustic sensor, the sensor measured values comprising time-domain signals of a component or environment …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 15 is objected to because of the following informalities:
Claim language should read “The automated method of claim 12, wherein quality information about the evaluation is ascertained from the information about the termination status of the least squares regression[[,]] and the at least one further item of information about the least squares regression, and output as a further sensor output signal” in order to correct for minor informalities.
Appropriate correction is required.
Claim 16 is objected to because of the following informalities:
Claim language “determining if the sensor measured values from the acoustic sensor are faulty based on a residual of the least squares regression …” should read “determining if the sensor measured values from the image sensor are faulty based on a residual of the least squares regression …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
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, 3-6, 12 and 14-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a machine, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see italic text for abstract idea):
the limitation “an evaluation unit including the neural network that is configured to estimate the parameter vector based on actually ascertained sensor measured values, and a least squares regression module, wherein the neural network is trained with parameter vectors and associated sensor measured values, the neural network further configured to ascertain at least one parameter estimated vector as input quantity for the least squares regression of the least squares regression module for the sensor measured values measured by the sensor by way of the trained neural network, to terminate the least squares regression after a convergence criterion is met when carrying out the least squares regression, and to output the at least one parameter of a last ascertained parameter vector from the least squares regression with a smallest square error as the sensor output signal” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or using mathematical concepts to evaluate data and obtain a result (e.g., using parameters and actual measurements in order to predict a parameter, which is used in a least square regression for evaluation with actual measurements in order to validate the parameter as sensor output; see specification at [0041]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use (e.g., neural networks), and the generic computer elements (see specification at [0041]), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data and obtain a result.
the limitation “wherein the evaluation unit includes an assessment module connected downstream of the least squares regression module, the assessment module configured to ascertain a success status of an evaluation from a residual of the least squares regression, information about a termination status of the least squares regression, and at least one further item of information about the least squares regression, the assessment module configured to output the success status, the information, and the at least one further item of information as a further sensor output signal, wherein the success status may be successful or unsuccessful” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or using mathematical concepts to evaluate data and obtain a result (e.g., evaluation of success of analysis; see specification at [0043]-[0044]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated, data outputting), and the generic computer elements (see specification at [0041]), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare information and obtain a result.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application when considering the claim as a whole. In particular, the claim recites (see non-italic text for additional elements):
“A device for assessing whether sensor measured values are successful or unsuccessful using a combination of a neural network and a least squares regression without requiring a separate starting value estimation”, which generally links the use of the judicial exception to a particular technological environment or field of use (see also claims 12 and 16; see MPEP 2106.05(h)), while merely using a computer as a tool to perform an abstract idea (see specification at [0040]-[0041]; see MPEP 2106.05(f));
“a sensor configured to detect sensor measured values for an evaluation of a chromatogram in gas chromatography, the sensor further configured to provide the sensor measured values, wherein a model function suitable for the least squares regression and configured to be defined by a parameter vector is provided for an evaluation of the sensor measured values of the sensor, wherein at least one parameter of the parameter vector forms a sensor output signal,” which adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) using elements recited at a high level of generality (i.e., a sensor) (see MPEP 2106.05(g));
an evaluation unit including the neural network that is configured to estimate the parameter vector based on actually ascertained sensor measured values, and a least squares regression module, wherein the neural network is trained with parameter vectors and associated sensor measured values, the neural network further configured to ascertain at least one parameter estimated vector as input quantity for the least squares regression of the least squares regression module for the sensor measured values measured by the sensor by way of the trained neural network, to terminate the least squares regression after a convergence criterion is met when carrying out the least squares regression, and to output the at least one parameter of a last ascertained parameter vector from the least squares regression with a smallest square error as the sensor output signal” which adds extra-solution activities (e.g., source/type of data being evaluated) using elements recited at a high level of generality (i.e., sensor) (see MPEP 2106.05(g)), a particular technological environment or field of use (e.g., neural networks) (see MPEP 2106.05(h)), and generic computer elements as tools to perform an abstract idea (see specification at [0041]) (see MPEP 2106.05(f));
the limitation “wherein the evaluation unit includes an assessment module connected downstream of the least squares regression module, the assessment module configured to ascertain a success status of an evaluation from a residual of the least squares regression, information about a termination status of the least squares regression, and at least one further item of information about the least squares regression, the assessment module configured to output the success status, the information, and the at least one further item of information as a further sensor output signal, wherein the success status may be successful or unsuccessful” which adds extra-solution activities (e.g., source/type of data being evaluated, data outputting) (see MPEP 2106.05(g)), and generic computer elements as tools to perform an abstract idea (see specification at [0041]) (see MPEP 2106.05(f)); and
“wherein if the evaluation is successful, the sensor measured values deliver a desired quantity or information that is required, wherein if the evaluation is unsuccessful a fault message is output” which adds extra-solution activities (e.g., mere data output) (see MPEP 2106.05(g)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim, when considered as a whole, is directed to an abstract idea under Step 2A of the test.
Additionally, under Step 2B of the test, the claim, when considered as a whole, does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (i.e., assessing whether sensor measured values are successful or unsuccessful, neural networks), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself” (see MPEP 2106.05(h));
recite extra-solution activities (i.e., mere data gathering/outputting by selecting a particular data source/type to be manipulated/output) using elements (i.e., a sensor) specified at a high level of generality, which as indicated in the MPEP: “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process” (see MPEP 2106.05(g)) and “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more” (see MPEP 2106.05(b), section III); and
append generic computer components (see specification at [0040]-[0041]) used to facilitate the application of the abstract idea (i.e., mere computer implementation), which as indicated in the MPEP: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more” (see MPEP 2106.05(f), item 2).
The claim is not patent eligible.
Similarly, independent claims 12 and 16 are directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
With regards to the dependent claims, they are also directed to the non-statutory subject matter because they just extend the abstract idea of the independent claims by additional limitations (Claims 3-6 and 14-15) that, under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts.
Subject Matter Not Rejected Over Prior Art
Claims 1, 3-6, 12 and 14-16 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Zhang (Zhang, Chuanwei, Shiyuan Liu, and Tielin Shi. “Metrology of deep trench structures in DRAM using FTIR reflectance spectrum.” 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications. Vol. 7160. SPIE, 2009, IDS reference) discloses/teaches:
A device for assessing whether sensor measured values are successful or unsuccessful using a combination of a neural network and a least squares regression without requiring a separate starting value estimation (Abstract: metrology of critical dimension of deep trenches in DRAM uses an artificial network and Levenberg-Marquardt combined algorithm to find the values of the modeling parameters which should produce the best fit to a given measured spectrum (see also p. 4, section “2.3 Extraction of geometric parameters”, par. 1); examiner notes that, in light of the specification (see [0036]), the described method does not require a separate starting value estimation), the device comprising:
a sensor configured to detect sensor measured values for an evaluation, the sensor configured to provide the sensor measured values (p. 1-2, section ‘Introduction’: a given measure spectrum is used during analysis, the spectrum being measured using a nondestructive method of infrared reflectance spectrometry (see also Fig. 1)), wherein a model function suitable for the least squares regression and configured to be defined by a parameter vector is provided for an evaluation of the sensor measured values of the sensor (p. 2-4, section “2. Mathematical Equations”: reflectance spectrum is simulated by equations using multiple parameters), wherein at least one parameter of the parameter vector forms a sensor output signal (p. 4, section “2.3 Extraction of geometric parameters”: critical dimension and trench depth are parameters obtained from the analysis); and
an evaluation unit including the neural network that is configured to estimate the parameter vector based on actually ascertained sensor measured values and a least squares regression module, wherein the neural network is trained with parameter vectors and associated sensor measured values (p. 4-5, section “2.3 Extraction of geometric parameters”: a neural network is trained using training data obtained using the reflectance equations and measured reflectance spectrum), the neural network further configured to ascertain at least one parameter estimated vector as input quantity for the least squares regression of the least squares regression module for the sensor measured values measured by the sensor by way of the trained neural network, to terminate the least squares regression after a convergence criterion is met when carrying out the least squares regression, and to output the at least one parameter of a last ascertained parameter vector from the least squares regression with a smallest square error as the sensor output signal (p. 4-5: after training the neural network, a theoretical spectrum is calculated and compared with the measured spectrum and if the evaluation function (difference between spectrums using the Levenberg-Marquardt algorithm) meets a criterion, the critical dimension and trench depth are considered as the geometric parameters resulting from the measured spectrum);
wherein the evaluation unit includes an assessment module connected downstream of the least squares regression module, the assessment module configured to ascertain a success status of an evaluation from a residual of the least squares regression, the assessment module configured to output the success status, wherein the success status may be successful or unsuccessful, wherein if the evaluation is successful, the sensor measured values deliver a desired quantity or information that is required, wherein if the evaluation is unsuccessful a fault message is output (Fig. 3, outputs of Step 6: outputs yes or no correspond to success status (see p. 5, par. 1 regarding if the evaluation function is successful, the variables being considered as the geometric parameters of the measured trench structure, otherwise the process is being optimized; see also p. 6, par. 1 regarding extraction error falling in 0.1% and extraction process finished in 3 seconds)).
Regarding “evaluation of a chromatogram in gas chromatography” Noda (US 20190179874 A1) teaches:
“An analysis data processing method for processing analysis data collected with an analyzing device for each of a plurality of samples, by applying an analytical technique using statistical machine learning to multidimensional analysis data formed by output values obtained from a plurality of channels of a multichannel detector provided in the analyzing device, the method including: acquiring a non-linear regression or non-linear discrimination function expressing analysis data obtained for known samples; calculating a contribution value of each of the output values obtained from the plurality of channels forming the analysis data of the known samples, to the acquired non-linear regression or non-linear discrimination function, based on a differential value of the non-linear regression function or non-linear discrimination function; and identifying one or more of the plurality of channels of the detector, which are to be used for processing analysis data obtained for an unknown sample, based on the contribution value” (Abstract: analysis of data using machine learning is applied to chromatograms (see [0001])).
The closest prior art of record, taken individual or in combination, fail to teach or suggest:
“the assessment module configured to ascertain a success status of an evaluation from information about a termination status of the least squares regression, and at least one further item of information about the least squares regression, the assessment module configured to output the information, and the at least one further item of information as a further sensor output signal,”
in combination with all other features of the claim, as claimed and defined by the applicant.
Regarding claim 12.
Zhang (Zhang, Chuanwei, Shiyuan Liu, and Tielin Shi. “Metrology of deep trench structures in DRAM using FTIR reflectance spectrum.” 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications. Vol. 7160. SPIE, 2009, IDS reference) discloses/teaches:
An automated method for evaluating sensor measured values to determine if the sensor measured values are faulty using a combination of a neural network and a least squares regression module (Abstract: metrology of critical dimension of deep trenches in DRAM uses an artificial network and Levenberg-Marquardt combined algorithm to find the values of the modeling parameters which should produce the best fit to a given measured spectrum (see also p. 4, section “2.3 Extraction of geometric parameters”, par. 1)), the automated method comprising:
providing a model function configured for a least squares regression and configured to be defined by a parameter vector for an evaluation of the sensor measured values (p. 2-4, section “2. Mathematical Equations”: reflectance spectrum is simulated by equations using multiple parameters), wherein a sensor output signal is formed by at least one parameter of the parameter vector (p. 4, section “2.3 Extraction of geometric parameters”: critical dimension and trench depth are parameters obtained from the analysis);
receiving, from a sensor, sensor measured values comprising time-domain signals of a component or environment (p. 1-2, section ‘Introduction’: a given measure spectrum is used during analysis, the spectrum being measured using a nondestructive method of infrared reflectance spectrometry (see also Fig. 1));
transforming the time-domain signals into a frequency-domain representation by performing a spectral analysis of the sensor measured values, and determining spectral features from the frequency-domain representation (p. 1-2, section ‘Introduction’: analysis comprises Fourier transform, which implies determination of frequency-domain features (see also abstract; see also Cong reference below));
providing the neural network configured to estimate the parameter vector based on actually ascertained sensor measured values (p. 1-2, section ‘Introduction’: a given measure spectrum is used during analysis, the spectrum being measured using a nondestructive method of infrared reflectance spectrometry (see also Fig. 1)) and the least squares regression module, wherein the neural network is trained with parameter vectors and associated sensor measured values (p. 4-5, section “2.3 Extraction of geometric parameters”: a neural network is trained using training data obtained using the reflectance equations and measured reflectance spectrum);
ascertaining at least one parameter estimated vector as input quantity for the least squares regression of the least squares regression module for the sensor measured values by the trained neural network, wherein the least squares regression is terminated when a convergence criterion is met when carrying out the least squares regression and the at least one parameter of a last ascertained parameter vector from the least squares regression with a smallest square error is output as the sensor output signal (p. 4-5: after training the neural network, a theoretical spectrum is calculated and compared with the measured spectrum and if the evaluation function (difference between spectrums using the Levenberg-Marquardt algorithm) meets a criterion, the critical dimension and trench depth are considered as the geometric parameters resulting from the measured spectrum); and
determining if the sensor measured values are faulty based on a residual of the least squares regression (Fig. 3, outputs of Step 6: outputs yes or no correspond to success status (see also p. 6, par. 1 regarding extraction error falling in 0.1% and extraction process