DETAILED ACTION
This Final Office Action is responsive to the claims filed on June 11, 2026. Claims 1-20 are under examination.
Claims 1-20 are under examination.
Claims 1, 8, and 15 are objected to.
Claims 1-20 are rejected under 35 USC 101 as ineligible subject matter.
Claims 1-20 are rejected under 35 USC 103 over Lastoskie, Hadoux, Telleria, and Multi-Clean.
Response To Amendments/Arguments
35 USC 112(a) Rejections: The Applicant’s amendments and arguments have been considered and are persuasive. The rejections are withdrawn.
35 USC 112(b) Rejections: The Applicant’s amendments and arguments have been considered and are persuasive. The rejections are withdrawn.
35 USC 101 Subject Matter Eligibility: The Applicant’s arguments and amendments have been considered but are not persuasive. The Applicant’s arguments are addressed in the order they are presented in the response.
Clauses [1] and [2] Allegedly Do Not Recite Exceptions: The Applicant asserts that the clauses [1] and [2] do not recite judicial exceptions. Specifically, clause [1] recites, “estimate values of a fixed set of parameters as functions of location on a test surface.” Clause [2] recites, “predict decontamination of the working surface based on the modified parametric model using the estimated values of the fixed set of parameters.”
The Applicant begins by stating that clause [1] does not recite a mathematical concept. This is facially incorrect. The claim element, “estimate,” is a mere placeholder for the term calculate. This is evident from the Applicant’s assertion that the parameters are based on the position on the surface as demonstrated in the equations presented in the Applicant’s specification paragraphs [0040]-[0046]. The Applicant has also failed to address that the Office Action demonstrated that the clause [1] limitation is also a mental process. Least squares or regression analysis conducted on equations such as those demonstrated in the Applicant’s paragraphs [0040]-[0046] are mathematical operations of the kind that were performed in the mind or with pen and paper by those skilled in the art well before the invention of the computer.
The Applicant also cites to Example 38; however example 38 is non-analogous. The limited example 38 exceptions apply to a pseudo random number generator that relies on a statistical distribution to simulate a representation of an analog circuit. There is no analogous feature in the Applicant’s claims. The Applicant’s claims are more akin to Electric Power Group, in which data is input into a model that outputs other data with no real application. The determination that something is dry is similar to a diagnosis being made, which is not an additional limitation that confer eligibility under MPEP 2106.05(g) and 2106.05(d). Accordingly, clause 1 recites an abstract idea, a judicial exception, and does not include any additional limitations that confer eligibility at Step 2A, Prong 2 or at Step 2B.
Clause 2 recites that a function is used to estimate an output. Even if, arguendo, this did not explicitly recite a mathematical concept (though, one would be reasonable to argue that the use of the elements of clause [1] is only inventive in and only confers benefits within mathematical operations), the Applicant’s assertion that a person of ordinary skill in the art cannot input determined values, such as coefficient values, into an equation mentally or using pen and paper to determine whether something is wet or dry, is insupportable. Again, scientists and mathematicians have used equations without computers, such that the operations are mental processes. Accordingly, clause 2 recites an abstract idea, a judicial exception, and fails to provide any additional limitations that confer eligibility at Step 2A, Prong 2 or at Step 2B.
The Applicant also argues that Example 38 does not recite a mathematical concept, however, when looking at the Applicant’s specification paragraphs [0040]-[0047], it is clear that the language of the claim represents the equations presented in the Applicant’s specification in textual form. Even if there were no explicit recitations in the claims that math was conducted, the language of the claims functions as little more than a generic placeholder for the math in the specification.
The Applicant then proceeds to argue that multiparameter math cannot practically be performed mentally or with the aid of pen and paper. However, all of the math used in the Applicant’s specification was invented prior to the advent of computers. This position is insupportable.
The Written Description Allegedly Links Clauses [1] And [2] To Improvements Over The Prior Art: Whether or not the written description links clauses [1] and [2] to improvements over the prior art is not the standard for ascertaining whether the clauses [1] and [2] integrate the judicial exceptions into a practical application at Step 2A, Prong 2. The “link” language is typically reserved for MPEP 2106.05(h), which is an exception to eligibility at both Step 2A, Prong 2 and Step 2B. The question of whether the claim recites any additional limitations that integrate the abstract idea into a practical application is not answered by clauses [1] and [2], because, with the exception of the mere data gathering obtaining step, clauses [1] and [2] are elements of the abstract idea. This is evident from the Applicant’s arguments from the last two paragraphs of Page 15 through the first paragraph on page 17 of the Applicant’s response. The improvements are to the math itself and are not reflected in the additional limitations. If the asserted improvement is entirely attributable to the abstract idea, then the claim is not integrated into a practical application at Step 2A, Prong 2.
The Applicant does not appear to present arguments for Step 2B. In the interest of completeness, using positional parameterization of the coefficients of an equation and regularizing values across image data are both well-understood, routine, and conventional (WURC) activities, as demonstrated below with the provided WURC evidence. Specifically, spatially varying coefficients and regularizing image data (e.g., to assume values between 1 and 0 across the values that are represented in the images) are both operations regularly applied to regression techniques, including machine learning (with regressive backpropagation), least squares analysis, and other mathematical operations).
Accordingly, the rejections are maintained.
35 USC 103: The Applicant’s arguments and amendments have been considered and are not persuasive. The Applicant’s response asserts with respect to Hadoux reference:
It is submitted that neither of Lastoskie nor Hadoux, nor their combination discloses or suggests Applicant's claimed "modify the parametric model to base the set of parameters on location across the test surface of the material; and estimate the values of the fixed set of parameters as functions of location on the test surface" because (1) Lastoskie is limited to estimating the values of the parameters for each pixel, (2) Telleria cannot make up this deficiency because Telleria is limited to determining when materials have set by measuring moisture content, (3) Multi-Clean cannot make up this deficiency because Multi-Clean is limited to laying out the benefits of electrostatic spraying over other forms of spraying such as fogging, misting, and wide area spraying, and (4) Hadoux cannot make up this deficiency because Hadoux is an object classification process for spectral images that uses spatial regularization to smooth the borders between different parts of an image.
The Arguments (1)-(4) will be addressed in order.
Lastoskie is allegedly limited to estimating values of the parameters for each pixel: This is not persuasive. Whatever the equations of Lastoskie are, the regularization of Hadoux can still be applied across the test surface to apply spatially to each pixel value. The language of the claim is sufficiently broad to include this interpretation.
Telleria: Telleria is not used to reject these elements.
Multi-Clean: Multi-Clean is not used to reject these elements.
Hadoux Smoothing: The smoothing in Hadoux is reasonably applied to the parametric determinations of Lastoskie because Hadoux increases resolution between regions of different intensity values in an image. The relationships are parameterized “based on” location because of the relationship illustrated in Hadoux equation 5.
The Applicant then argues, with respect to Hadoux and Lastoskie:
(a) nowhere does Hadoux disclose or suggest Applicant's claimed modifying the parametric model to base the set of parameters on location across a test surface because Hadoux does not disclose a parametric model with a set of parameters, (b) nowhere does Hadoux disclose or suggest estimating the values of the set of parameters as functions of location on the test surface because Hadoux does not disclose a set of parameters, and (c) Hadoux is not analogous art to Applicant's claimed problem of decontaminating a surface of material. With respect to (a) and (b), Hadoux uses a supervised dimension reduction method to lower the background variability in the resulting score image, making edges mostly correspond to borders. Hadoux computes, in Eq. (5), the change of a single channel image at a point x,y over time t. Applicant, on the contrary, estimates values of the set of parameters as functions of location. Applicant's parametric model is of radiation emitted as a function of time, but Applicant's set of parameters vary by location. With respect to (c), the primary purpose of Hadoux is to classify spectral images by enhancing borders so that objects can be identified and classified. The primary purpose of Applicant's claimed invention is to predict decontamination of a surface. The primary purpose, industry, and function of Hadoux are totally disconnected from Applicant's claimed invention. A person of ordinary skill in the art would not logically consult Hadoux to solve the problem of predicting decontamination of a surface. The application of Hadoux is hindsight. Hadoux's evolution of the diffusion process as shown in Hadoux's Eq. (5) is a function of time, not space, and thus cannot achieve Applicant's claimed estimating the values of parameters as function of locations on a surface.
The Arguments (a)-(d) will be addressed in order.
Hadoux allegedly does not disclose a parametric model: The broadest reasonable interpretation of a parametric model is one that has parameters. Hadoux’s Anisotropic regularization on page 2363 uses parameters, including “space variables,” to relate the images and time elements with del and other operators. These are parametric models in the broadest reasonable sense.
Applicant’s parameters vary by location: This is not in the language of the claim. The claim uses “based on” language, which is broad enough to include elements other than parameters that are functions of location. Also, as shown in the references Hadoux’s Anisotropic regularization on Page 2363, “ div and
∇
are, respectively, the divergence operator and the gradient operator with respect to the space variables, and where time t is the time used to define the evolution of the diffusion process.” As the Hadoux Abstract states, “[t]his paper proposes a novel approach to classify hyperspectral images using both spectral and spatial information.”
Analogous Art: Both Hadoux and Lastoskie are computer vision applications, where Hadoux can provide better resolved boundaries for wet versus dry regions in Lastoskie. The problems and solutions in Lastoskie consists of several subproblems and subsolutions. That Hadoux is not specifically a solution to drying time determination problems does not mean that Hadoux cannot improve the resolution of recognized image features, including wet and dry areas, or any other spectrally distinguishable regions of an image.
Accordingly, the Applicant’s arguments are not persuasive, and the rejections are maintained.
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informalities:
Claims 1, 8, and 15 recite, “continuation functions.” There is no basis in the specification for this, and it does not really make sense. Continuation functions are computer programming functions used with regression operations (See the Reis reference of record). It appears this is a typo and was intended to state “continuous functions,” support for which can be found in the Applicant’s specification paragraph [0043], and this Final Office Action interprets the limitation as such.
Claims 1, 8, and 15 recite, “a second thermographic images,” which appears to be a typo.
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 1-20 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Subject Matter Eligibility
Independent Claims
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical concept, which are abstract ideas.
Claim 1 (Statutory Category – Machine)
MPEP 2106.04(a)(2)(Ill): “Accordingly, 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. […] The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
MPEP 2106.04(a)(2)(I): “When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept […] 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).
MPEP 2106.04(a)(2)(I)(A): “Mathematical Relationships. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Claim 1 recites (claim features in italics, paragraph references are to the Applicant’s specification):
determine a statistically significant difference between the first thermographic images and the second thermographic images by comparing intensity values at respective positions on the first thermographic images and the second thermographic images using a two-sample t-test and corrected for multiple tests, wherein a coverage of the decontamination liquid across the test surface is a percent of the intensity values in which there was the statistically significant difference; (Evaluation, Mathematical Relationships – Conducting 2-sample t-tests corrected for multiple tests is practically performable in the mind or with the aid of pen and paper, so this is an evaluation, a mental process, an abstract idea. Also, these are explicitly mathematical operations, which are mathematical concepts, abstract ideas.)
determine a parametric model related to the coverage of the test surface based on the intensity values, an amount of radiation emitted being a function of the coverage and time it takes the test surface to dry, the parametric model including a set of parameters with values that are unknown; (Evaluation, Mathematical Relationships – Performing regression operations is practically performable in the mind or with the aid of pen and paper, so this is an evaluation, a mental process, an abstract idea. Also, these are explicitly mathematical operations in the specification (See Applicant’s specification paragraphs [0040]-[0047]), which are mathematical concepts, abstract ideas.)
modify the parametric model to base the fixed set of parameters on location across a test surface of the material by approximating model parameters as continuation functions across the test surface; (Evaluation, Mathematical operations on sets of mathematical relationships (e.g., in a parametric mathematical model). Regularization merely mathematically changes the scale of values to make them more manageable, which is a standard evaluation of a mathematical relationship: [0040]-[0046] – Spatial regularization is applied to the parametric model, an evaluation.)
estimate the values of the fixed set of parameters as functions of location on the test surface; and (Evaluation, Mathematical operations on sets of mathematical relationships (e.g., in a mathematical function parameterized by variables representing relative position in an image): [0040]-[0046] – The model is fit by modifying the parameters, an evaluation.)
predict the decontamination and the drying time of the working surface based on the modified parametric model using the estimated values of the fixed set of parameters. (Evaluation, Mathematical operations on sets of mathematical relationships (e.g., in a mathematical function with parameters): [0040]-[0046]– The model predicts decontamination based on radiation emitted, an evaluation.)
The modify, estimate, and predict steps of claim 1 are elements of an evaluation, a mental process, which can be performed in the mind of a person or with a pen and paper. ([0044], [0059]) Further, the apply, fit, and deploy steps of claim 1 as described in the claim and specification include and/or are expressed as mathematical calculations on mathematical relationships (e.g., models), which are mathematical concepts. Being a mental process and a mathematical concept, the modify, estimate, and predict steps are an abstract idea.
Claim 8 (Statutory Category – Machine)
Claim 8 recites the method which the system of claim 1 is configured to execute and recites an abstract idea for at least the same reasons as claim 1.
Claim 15 (Statutory Category – Machine)
Claim 15 recites a CRM that is an embodiment of the memory of claim 1 and recites an abstract idea for at least the same reasons as claim 1.
Claims 1, 8, and 15 recite an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
MPEP 2106.04(d): “[A]fter determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. Whether or not a claim integrates a judicial exception into a practical application is evaluated using the considerations set forth in subsection I below, in accordance with the procedure described below in subsection II.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to […] more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(g): “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, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional limitations:
Claim 1
An apparatus for decontaminating a surface of a working surface of a material, the apparatus comprising:
a memory configured to store computer-readable program code; and
processing circuitry configured to access the memory, and execute the computer-readable program code to cause the apparatus to at least:
Claim 15
A computer-readable storage medium for decontaminating a surface of a material, the computer-readable storage medium being non-transitory and having computer-readable program code stored therein that, in response to execution by processing circuitry, causes an apparatus to at least:
(Generic computer implementations: [0031], [0058]-[0060])
The claimed system includes a generic memory and a processor with no specific system alterations to execute the claimed steps. The computer implementation is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, the computer implementation implements the recited abstract idea on a generic computer, and, under MPEP 2106.05(f) does not integrate the abstract idea into a practical application in Step 2A Prong Two.
obtain training data including first thermographic images taken at a first time and a second thermographic images taken at a second time of a test surface covered with decontamination liquid;
The obtain step merely gathers existing information (a model composed entirely of mathematical relationships) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). The obtain step adds nothing more than insignificant extra solution activity, so they do not integrate the abstract idea into a practical application in Step 2A Prong Two.
Should it be found that spatial regularization is other than an abstract idea, spatial regularization is a longstanding practice that on its face is known to be used to better represent spatial heterogeneity in data as demonstrated in the Wang reference of record. Specifically, the Wang Abstract states, “When performing spatial regression analysis in environmental data applications, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regression and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations.” This is an element of the judicially created Step 1 that would precede the Step 2A, Prong 2 analysis.
Therefore, there are no additional limitations in the claims that integrate the abstract idea into a practical application at Step 2A, Prong 2.
The claims fail to integrate the abstract idea into a practical application and are directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. […] 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.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “[I]mplementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(d)(II)(i): “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. […] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”
MPEP 2106.05(g): “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).”
The additional limitations:
Claim 1
An apparatus for decontaminating a surface of a working surface of a material, the apparatus comprising:
a memory configured to store computer-readable program code; and
processing circuitry configured to access the memory, and execute the computer-readable program code to cause the apparatus to at least:
Claim 15
A computer-readable storage medium for decontaminating a surface of a material, the computer-readable storage medium being non-transitory and having computer-readable program code stored therein that, in response to execution by processing circuitry, causes an apparatus to at least:
(Generic computer implementations: [0031], [0058]-[0060])
The claimed system includes a generic memory and a processor with no specific system alterations to execute the claimed steps. The computer implementation is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, the computer implementation implements the recited abstract idea on a generic computer, and, under MPEP 2106.05(f) fails to combine with the other elements of the claim to provide significantly more, and, therefore, fails to confer an inventive concept at Step 2B.
obtain training data including first thermographic images taken at a first time and a second thermographic images taken at a second time of a test surface covered with decontamination liquid;
The obtain step is storing and retrieving information from memory and also indicative of sending or receiving data, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) representing well-understood, routine, and conventional functions.
Because the additional limitations of the obtain step is insignificant extra-solution activity (as illustrated under Step 2A Prong 2) and well-understood, routine, and conventional functions, it fails to provide the abstract idea with significantly more to render the combination of the additional limitations with the other claim elements an inventive concept, under MPEP 2106.05(f) and MPEP 2106.05(g) respectively, at step 2B.
Should it be found that spatial regularization is other than an abstract idea, spatial regularization is a well-understood, routine, and conventional activity for modeling spatial heterogeneity in data, as demonstrated in the Wang reference of record. Specifically, the Wang Abstract states, “When performing spatial regression analysis in environmental data applications, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regression and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations.”
Therefore, there are no additional limitations in the independent claims that furnish the independent claims with an inventive concept to ensure that independent claims, as a whole, amount to significantly more than the bolded abstract idea at Step 2B.
Claims 1, 8, and 15 are ineligible.
Dependent Claims
The dependent claims 2-7, 9-14, and 16-20 are also ineligible for the following reasons.
Claims 2, 9, and 16
Claim 2 recites,
wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further […] (Generic Computing Element: As demonstrated, the processor is a generic computing element that fails to confer eligibility under Steps 2A, Prong 2 and 2B.)
[…] predict a drying time for the working surface of the material by evaporation of the decontamination liquid, from the radiation emitted as predicted across the working surface over time. (Evaluation, Mathematical operations on sets of mathematical relationships (e.g., in a model): [0047] – The drying time is predicted based on the warming asymptote, and evaluation.)
The predict step deploys mathematical evaluations, which are mental processes, of mathematical relationships using mathematical operations, which are mathematical concepts. Mental processes and mathematical concepts are abstract ideas, so the predict step is an element of the abstract idea and does not contribute any additional limitations beyond the abstract idea.
The features of claim 2 do not provide further additional limitations to integrate the abstract idea into a practical application at Step 2A, Prong 2, or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept at Step 2B.
Regarding claim 9, claim 9 recites the method steps of the apparatus of claim 2, so the eligibility analysis of claim 2 applies to claim 9.
Regarding claim 16, claim 2 recites a memory that functions as the CRM of claim 16, executing the same steps as claim 2, so the eligibility analysis of claim 2 applies to claim 16.
Claims 2, 9, and 16 are ineligible.
Claims 3, 10, and 17
Claim 3 recites,
wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further […] (Generic Computing Element: As demonstrated, the processor is a generic computing element that fails to confer eligibility under Steps 2A, Prong 2 and 2B.)
determine that the working surface of the material is decontaminated when the drying time is at least a specified decontamination time. (Evaluation, Mathematical operations on sets of mathematical relationships (e.g., in a model): [0050] – The drying time is compared with a threshold to determine if the surface is decontaminated, an evaluation.)
The determine step deploys mathematical evaluations, which are mental processes, of mathematical relationships using mathematical operations, which are mathematical concepts. Mental processes and mathematical concepts are abstract ideas, so the determine step is an element of the abstract idea and does not contribute any additional limitations beyond the abstract idea.
The features of claim 3 do not provide further additional limitations to integrate the abstract idea into a practical application at Step 2A, Prong 2, or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept at Step 2B.
Regarding claim 10, claim 10 recites the method steps of the apparatus of claim 3, so the eligibility analysis of claim 3 applies to claim 10.
Regarding claim 17, claim 3 recites a memory that functions as the CRM of claim 17, executing the same steps as claim 3, so the eligibility analysis of claim 3 applies to claim 17.
Claims 3, 10, and 17 are ineligible.
Claims 4, 11, and 18
MPEP 2106.05(h): “Instead, the additional element in Flook regarding the catalytic chemical conversion of hydrocarbons was not sufficient to make the claim eligible, because it was merely an incidental or token addition to the claim that did not alter or affect how the process steps of calculating the alarm limit value were performed. Further, the Supreme Court found that this limitation did not amount to an inventive concept. 437 U.S. at 588-90, 198 USPQ at 197-98. The Court reasoned that to hold otherwise would "exalt form over substance", because a competent claim drafter could attach a similar type of limitation to almost any mathematical formula. 437 U.S. at 590, 198 USPQ at 197.”
Claim 4 recites,
wherein the training data is obtained from an experiment designed to test an effect of a number of experimental factors on the radiation emitted across the test surface over time, and the modified parametric model is deployed to predict the radiation emitted under particular levels of the number of experimental factors.
The sources of the training data and the parametric model obtained and accessed respectively are merely elements of the mere data gathering applied to the obtaining and accessing steps of the independent claim. Accordingly, the analysis applied to the obtaining and accessing steps applies to the limitation of claim 4.
Further, the features of claim 4 discuss data sources that merely limit the abstract idea to a particular field of use, decontaminating a surface. The nature of the data sources do not “affect how the process steps of [predicting radiation emitted are] performed.” Therefore, under MPEP 2106.05(h) and Flook, the features of claim 4 merely limit the abstract idea to a particular field of use and do not integrate the abstract idea into a practical application at Step 2A, Prong 2 or combine with the other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
The features of claim 4 do not provide further additional limitations to integrate the abstract idea into a practical application at Step 2A, Prong 2, or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept at Step 2B.
Regarding claim 11, claim 11 recites the method steps of the apparatus of claim 4, so the eligibility analysis of claim 4 applies to claim 11.
Regarding claim 18, claim 4 recites a memory that functions as the CRM of claim 18, executing the same steps as claim 4, so the eligibility analysis of claim 4 applies to claim 18.
Claims 4, 11, and 18 are ineligible.
Claims 5, 12, and 19
Claim 5 recites,
wherein the experiment is also designed to test the effect of the number of experimental factors on coverage of the decontamination liquid across the test surface, and […] carry out the experiment to determine the particular levels of the number of experimental factors that optimize the coverage of the decontamination liquid. (Evaluation, Mathematical operations on sets of mathematical relationships (e.g., in a model): [0037]-[0038] – The number of experimental factors that optimize the coverage of the liquid is determined, an evaluation.)
The wherein clause and carry out step deploy mathematical evaluations, which are mental processes, of mathematical relationships using mathematical operations, which are mathematical concepts. Mental processes and mathematical concepts are abstract ideas, so the wherein clause and carry out step are elements of the abstract idea and do not contribute any additional limitations beyond the abstract idea.
the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further (Generic Computing Element: As demonstrated, the processor is a generic computing element that fails to confer eligibility under Steps 2A, Prong 2 and 2B.)
The features of claim 5 do not provide further additional limitations to integrate the abstract idea into a practical application at Step 2A, Prong 2, or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept at Step 2B.
Regarding claim 12, claim 12 recites the method steps of the apparatus of claim 5, so the eligibility analysis of claim 5 applies to claim 12.
Regarding claim 19, claim 5 recites a memory that functions as the CRM of claim 19, executing the same steps as claim 5, so the eligibility analysis of claim 5 applies to claim 19.
Claims 5, 12, and 19 are ineligible.
Claims 6, 13, and 20
Claim 6 recites,
wherein the first thermographic images are captured before the decontamination liquid is deposited on the test surface, and the second thermographic images are captured after the decontamination liquid is deposited on the test surface, and
The sources of the training data obtained are merely elements of the mere data gathering applied to the obtaining and accessing steps of the independent claim. Accordingly, the analysis applied to the obtaining step applies to this limitation of claim 6.
Further, the features of claim 6 discuss data sources that merely limit the abstract idea to a particular field of use, decontaminating a surface. The nature of the data sources do not “affect how the process steps of [predicting radiation emitted are] performed.” Therefore, under MPEP 2106.05(h) and Flook, the features of claim 6 merely limit the abstract idea to a particular field of use and do not integrate the abstract idea into a practical application at Step 2A, Prong 2 or combine with the other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
wherein the apparatus caused to carry out the experiment includes the apparatus caused to determine the coverage of the decontamination liquid across the test surface from the first thermographic images and the second thermographic images. (Generic Computing Element: As demonstrated, the apparatus is a generic computing element that fails to confer eligibility under Steps 2A, Prong 2 and 2B.)
The features of claim 6 do not provide further additional limitations to integrate the abstract idea into a practical application at Step 2A, Prong 2, or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept at Step 2B.
Regarding claim 13, claim 13 recites the method steps of the apparatus of claim 6, so the eligibility analysis of claim 6 applies to claim 13.
Regarding claim 20, claim 6 recites a memory that functions as the CRM of claim 20, executing the same steps as claim 6, so the eligibility analysis of claim 6 applies to claim 20.
Claims 6, 13, and 20 are ineligible.
Claims 7 and 14
Claim 7 recites,
wherein the decontamination liquid is deposited on the test surface in an environment and using an electrostatic sprayer, and
wherein the training data is obtained from the experiment designed to test the effect of the number of experimental factors including multiple ones of the material, a profile of the test surface of the material, a temperature of the environment, a humidity of the environment, the electrostatic sprayer, the decontamination liquid, or an orientation of the electrostatic sprayer with respect to the test surface when the decontamination liquid is deposited.
The sources of the training data obtained are merely elements of the mere data gathering applied to the obtaining and accessing steps of the independent claim. Accordingly, the analysis applied to the obtaining step applies to this limitation of claim 7.
Further, the features of claim 7 discuss data sources that merely limit the abstract idea to a particular field of use, decontaminating a surface. The nature of the data sources do not “affect how the process steps of [predicting radiation emitted are] performed.” Therefore, under MPEP 2106.05(h) and Flook, the features of claim 7 merely limit the abstract idea to a particular field of use and do not integrate the abstract idea into a practical application at Step 2A, Prong 2 or combine with the other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
The features of claim 7 do not provide further additional limitations to integrate the abstract idea into a practical application at Step 2A, Prong 2, or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept at Step 2B.
Regarding claim 14, claim 14 recites the method steps of the apparatus of claim 7, so the eligibility analysis of claim 7 applies to claim 14.
Claims 7 and 14 are ineligible.
Claim Rejections - 35 USC § 103
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 factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-20: Lastoskie, Hadoux, and one or both of Telleria and/or Multi-Clean
Claims 1-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over NPL: “Selection and Characterization of Semi-Automated Disinfection Devices: Findings and Recommendations from Boeing Research & Technology” by Lastoskie et al. (Lastoskie) and NPL: “A Spectral–Spatial Approach for Hyperspectral Image Classification Using Spatial Regularization on Supervised Score Image“ to Hadoux et al. (Hadoux). Additionally or Alternatively, Claims 1-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Lastoskie in view of one or both of US 2018/0283019 A1 to Telleria et al (Telleria) and/or NPL: “Electrostatic Spray Disinfecting – Differences between fogging, misting Spraying” by Multi-Clean (Multi-Clean).
Claims 1, 8, and 15
Regarding claim 1, Lastoskie teaches:
An apparatus for decontaminating a working surface of a material, the apparatus comprising: (Lastoskie Page 4, Second and Third Paragraph “In each test, a tester used an electrostatic sprayer to deposit disinfectant on a 1-by 1-foot material coupon. The outputs were quantified using a FLIR® E40 Thermal Imaging Camera. The camera was mounted to a tripod for stable data collection and used to record a 15 minute video for each line item in the test matrix. The settings on the camera were manually adjusted so that a fixed temperature range could be used. To eliminate noise, the data collector left the site immediately after spray application during the recording. This reduced airflow and vibration effects.” – This discloses and apparatus for decontaminating a surface of a material.)
a memory (Lastoskie Page 4, Second Paragraph “The camera was mounted to a tripod for stable data collection and used to record a 15 minute video for each line item in the test matrix.” – The image data is recorded in some form of memory.)
obtain training data including first thermographic images taken at a first time and a second thermographic images taken at a second time of a test surface covered with decontamination fluid; (Lastoskie Page 4, Second Paragraph “In each test, a tester used an electrostatic sprayer to deposit disinfectant on a 1-by 1-foot material coupon. The outputs were quantified using a FLIR® E40 Thermal Imaging Camera. The camera was mounted to a tripod for stable data collection and used to record a 15 minute video for each line item in the test matrix.” Page 4, Fourth Paragraph “Temperature changes, captured with pixel density analysis, were used to measure deposition and drying time.” – The training data including dot matrix thermographic imaging of the surfaces with the disinfectant is obtained. Videos have frame rates that can be varied, there is no difference between a video and a set of time-lapse images by the terms of the claims. Also, sampling techniques for reducing calculation complexity for fitting a model include reducing the number of samples.)
determine a statistically significant difference between the first thermographic images and the second thermographic images by comparing intensity values at respective positions on the first thermographic images and the second thermographic images using a two-sample t-test and corrected for multiple tests, wherein a coverage of the decontamination liquid across the test surface is a percent of the intensity values in which there was the statistically significant difference; (Lastoskie Page 27, Last Paragraph - Page 28, First Paragraph “In order to characterize the spray coverage over a target object, a binary value was assigned to each pixel to indicate whether the portion of the target object represented by that pixel received a measurable amount of spray. Specifically, the test evaluated whether or not there was a significant drop in pixel intensity between a four second duration at the start of the video and a one second duration after the identified spray-time for that video. For each pixel in the video’s ROI, we calculated p-values using a two-sample t-test and corrected for multiple tests to have a false discovery rate of 0.05. Spray coverage was reported as the percentage of pixels within a ROI that had a significant change.” – A two-way t-test with corrected for multiple test is applied to images over time to determine statistically distinguishable changes (e.g., going from dark to light, indicating drying).
determine a parametric model related to the coverage of the test surface based on the intensity values, an amount of radiation emitted being a function of the coverage and time it takes the test surface to dry, the parametric model including a set of parameters with values that are unknown; (Lastoskie Page 4, Fourth Paragraph “Temperature changes, captured with pixel density analysis, were used to measure deposition and drying time. Automated processing was used to remove subjective interpretation of deposition and drying time, and a mathematical model was fit to the IR data.” Page 26, Last Paragraph “Once loaded, each frame initially contained three color channels and was first converted to a grayscale image. The grayscale image was a 240 x 320 matrix of 8-bit unsigned integers each taking on values from 0 – 255.” Page 28, First Paragraph “For each pixel in the video’s ROI, we calculated p-values using a two-sample t-test and corrected for multiple tests to have a false discovery rate of 0.05. Spray coverage was reported as the percentage of pixels within a ROI that had a significant change.” Page 29, First Paragraph “This functional form is fit to each individual pixel using non-linear least squares.” Also, see the parameterized model forms on page 28–
PNG
media_image1.png
125
461
media_image1.png
Greyscale
PNG
media_image2.png
104
602
media_image2.png
Greyscale
Page 28, Third Paragraph “where 𝑡 is time, 𝑎1:3 are the model coefficients for the evaporative cooling phase, 𝑏1:3 are the model coefficients for the warming phase, and 𝛾 is the breakpoint time where the model switches from cooling to warming. The parameters 𝑎2,𝑎3, and 𝑏2 are constrained to be less than zero.”
Page 28, Last Paragraph “The piecewise function 𝑢̂ is implemented using max so that”
PNG
media_image3.png
89
759
media_image3.png
Greyscale
– A parametric model related to the coverage of the test surface based on the intensity values is determined, wherein an amount of radiation emitted being a function of the coverage and time it takes the test surface to dry. Prior to the fit, the values are unknown.)
modify the parametric model by approximating model parameters as [continuous] functions across the surface; (Lastoskie Page 4, Fourth Paragraph “Temperature changes, captured with pixel density analysis, were used to measure deposition and drying time. Automated processing was used to remove subjective interpretation of deposition and drying time, and a mathematical model was fit to the IR data.” Page 26, Last Paragraph “Once loaded, each frame initially contained three color channels and was first converted to a grayscale image. The grayscale image was a 240 x 320 matrix of 8-bit unsigned integers each taking on values from 0 – 255.” Page 28, First Paragraph “For each pixel in the video’s ROI, we calculated p-values using a two-sample t-test and corrected for multiple tests to have a false discovery rate of 0.05. Spray coverage was reported as the percentage of pixels within a ROI that had a significant change.” Page 29, First Paragraph “This functional form is fit to each individual pixel using non-linear least squares.” Also, see the parameterized model forms on page 28–
PNG
media_image1.png
125
461
media_image1.png
Greyscale
PNG
media_image2.png
104
602
media_image2.png
Greyscale
Page 28, Third Paragraph “where 𝑡 is time, 𝑎1:3 are the model coefficients for the evaporative cooling phase, 𝑏1:3 are the model coefficients for the warming phase, and 𝛾 is the breakpoint time where the model switches from cooling to warming. The parameters 𝑎2,𝑎3, and 𝑏2 are constrained to be less than zero.”
Page 28, Last Paragraph “The piecewise function 𝑢̂ is implemented using max so that”
PNG
media_image3.png
89
759
media_image3.png
Greyscale
- The spatially regularized pixel values are fit to the parameterized model, which is a continuous function (piecewise functions are continuous if there is no gap, the max function will transition at the intersection of the functions). Also, because pixel values are in a matrix, they are spatially organized and minimum squares analysis is conducted for each pixel at each fixed location relative to the matrix representing the image. Further, regularization is established by conversion from color to gray scale pixel values being between 0 and 255 for each 8-bit image.)
PNG
media_image4.png
408
583
media_image4.png
Greyscale
estimate the values of the fixed set of parameters (Lastoskie Page 29, First Paragraph “This functional form is fit to each individual pixel using non-linear least squares.” Also see Figure 27 in which the red curve indicates the fit curve. – The parametric model with special regularization to the training data is fit to produce a fitted parametric model)
predict the decontamination and the drying time of the working surface based on the modified parametric model using the estimated values of the fixed set of parameters. (Lastoskie Page 1, Executive Summary “
Boeing conducted two studies, called Design of Experiment (DOE) 1: Characterization of Electrostatic Sprayer Application of Disinfectants and DOE 2: Wetting and Drying of Representative Substrates, to characterize electrostatic spray disinfection devices (one type of semi-automated device). Based on those studies and as discussed in greater detail below, the following is recommended with respect to electrostatic spray devices:” Page 1-2, Introduction “Thorough and efficient cleaning procedures must be implemented to return to pre-pandemic air traffic levels quickly and cost-effectively. For air carriers to retain an average turn-around time of 30 minutes for a single aisle aircraft, the target disinfection duration is 10 minutes. Manual application of disinfectant, with spray bottles and cleaning cloths, cannot be completed in the targeted timeframe without a significant increase in the cleaning crew manpower and therefore cost. Another challenge is the possible variability of manual cleaning. Evenness of application on contaminated surfaces is important, but cannot be guaranteed between individuals who may spray with different timing, frequency and force. This underscores the important potential of using semi-automated mechanisms for disinfectant application.” - Drying time is calculated as disinfecting time. Page 25, 8.6 Expanded DOE 2 Methodology “The goal of the IR video analysis was to quantify spray coverage and dry time of a material sample when sprayed with an ES device. It was desirable that the process for generating these estimates was as automated as possible in order to enhance reproducibility and reduce subjective bias. Therefore, an analysis pipeline was developed which included minimal manual steps and could calculate spray coverage and dry-times from IR videos directly.” Also, see Figure 22 on Page 25 (shown below) – The model is deployed to predicting the radiation emitted across a working surface, which is used to determine a drying time. It is also noteworthy that the Applicant’s specification [0003], [0005], [0035], and [0050] indicate that drying time can be considered decontamination time.)
PNG
media_image5.png
252
947
media_image5.png
Greyscale
Lastoskie suggests (Lastoskie Page 4 “Automated processing was used to remove subjective interpretation of deposition and drying time, and a mathematical model was fit to the IR data.” Page 25, 8.6 Expanded DOE 2 Methodology “The goal of the IR video analysis was to quantify spray coverage and dry time of a material sample when sprayed with an ES device. It was desirable that the process for generating these estimates was as automated as possible in order to enhance reproducibility and reduce subjective bias. Therefore, an analysis pipeline was developed which included minimal manual steps and could calculate spray coverage and dry-times from IR videos directly.”) but does not appear to explicitly teach, but Telleria teaches:
a memory configured to store computer-readable program code; and processing circuitry configured to access the memory, and execute the computer-readable program code to cause the apparatus to at least: (Telleria [0038] “Accordingly, the control system 322 can drive the drywalling system 100 to perform various suitable tasks, with some or all portions of such tasks being automated and performed with or without user interaction. The control system can comprise various suitable computing systems, including one or more processor and one or more memory storing instructions that if executed by the one or more processer, provide for the execution of tasks by the automated drywalling system 100 as discussed in detail herein. “ [0170] “The system 100 can also determine when such materials have set and/or dried by measuring moisture content of such materials, thermal conductivity of a covered seam 620, using a thermal imaging camera or thermometer (contact or non-contact), by detecting differences in colors using a camera, and the like.” – Drying times are determined by automation using a processor and memory to analyze dryness based on thermal images.)
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claims to modify the automation of modeling drying times as expressed in Lastoskie with the automation of the modeling using a processor and memory as taught in Telleria because a person of ordinary skill in the art would be motivated based on the expressed desire to automate the modeling and drying determinations of Lastoskie to look to the memory and processor of Telleria that are also used to model drying times using thermographic imaging. (Lastoskie Page 4 “Automated processing was used to remove subjective interpretation of deposition and drying time, and a mathematical model was fit to the IR data.” Page 25, 8.6 Expanded DOE 2 Methodology “The goal of the IR video analysis was to quantify spray coverage and dry time of a material sample when sprayed with an ES device. It was desirable that the process for generating these estimates was as automated as possible in order to enhance reproducibility and reduce subjective bias. Therefore, an analysis pipeline was developed which included minimal manual steps and could calculate spray coverage and dry-times from IR videos directly.”; Telleria [0038] “Accordingly, the control system 322 can drive the drywalling system 100 to perform various suitable tasks[…] The control system can comprise […] one or more processor and one or more memory storing instructions that if executed by the one or more processer, provide for the execution of tasks by the automated drywalling system 100 […]“ [0170] “The system 100 can also determine when such materials have set and/or dried […] using a thermal imaging camera […])
Additionally or alternatively, it would have been obvious to try using a processor and memory to automate the modeling and monitoring of the decontamination process.
MPEP 2144.05(I)(E): “’Obvious To Try’ - Choosing From a Finite Number of Identified, Predictable Solutions, With a Reasonable Expectation of Success […] To reject a claim based on this rationale, Office personnel must resolve the Graham factual inquiries. Then, Office personnel must articulate the following: (1) a finding that at the relevant time, there had been a recognized problem or need in the art, which may include a design need or market pressure to solve a problem; (2) a finding that there had been a finite number of identified, predictable potential solutions to the recognized need or problem; (3) a finding that one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success; and (4) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness.
Lastoskie expresses a desire to automate processes of modeling and monitoring drying times to the largest extent possible with a processor and memory, as in, Telleria. (1) The need for automation of processes has been known for a long time, including at and before the effective filing date of the claims. (2) At the generic level at which the solution for automation is claimed in claim 1, there were and are few to no alternatives to a processor with memory for automated processing of images and modeling. (3) One of ordinary skill in the art could and would likely have pursued the use of some processor or memory with a reasonable expectation of success at or before the time of filing. (4) At the level at which the claim automates its processes using a processor and memory, it is not likely that a person of ordinary skill in the art would use anything other than some form of processor that at least has some memory (e.g., cache, register entries, or even transistors).
Lastoskie (Lastoskie Page 1, Last Paragraph – Page 2, First Paragraph “Thorough and efficient cleaning procedures must be implemented to return to pre-pandemic air traffic levels quickly and cost-effectively. For air carriers to retain an average turn-around time of 30 minutes for a single aisle aircraft, the target disinfection duration is 10 minutes. Manual application of disinfectant, with spray bottles and cleaning cloths, cannot be completed in the targeted timeframe without a significant increase in the cleaning crew manpower and therefore cost. Another challenge is the possible variability of manual cleaning. Evenness of application on contaminated surfaces is important, but cannot be guaranteed between individuals who may spray with different timing, frequency and force. This underscores the important potential of using semi-automated mechanisms for disinfectant application.” Page 10, Third Paragraph “Boeing recommends spraying at distances between 2 and 4 feet. By keeping the spray distance under 4 feet, the operator can avoid insufficient deposition, especially if the spraying is upwards, or towards a vertical surface (Figure 7). As spray deposition amount is correlated to disinfection effectiveness, low deposition should be avoided. By keeping the distance at greater than 2 feet, the operator can control dry time. Spraying closer than 2 feet may result in significantly longer dry times (Figure 4).” Page 29, Second Paragraph “Setting the threshold to 90% of the warming asymptote is a heuristic, which is rationalized since it is where the pixel intensity is “close enough” to thermal equilibrium, and well past the point governed by evaporative cooling.” Also, see Figure 27), at the very least suggests or implies, if it is not explicit (for which this rejection is applied in the alternative, should the next reference be unnecessary), that the surfaces are disinfected by the time the substances dry, so the applying and drying time is treated like a disinfecting time. but does not appear to explicitly teach, but Multi-Clean teaches:
predict the decontamination and the drying time of the working surface based on the modified parametric model using the estimated values of the fixed set of parameters. (Multi-Clean Page 4/7, Item 4. “Occupancy delays are short with spraying, as you only need to wait until the surfaces are dry, usually less than 15 minutes” Page 5/7 “The EPA is asking for submission of some additional data on electrostatic spray application of disinfectants. One piece of data is a “wetness” test to insure surfaces sprayed with an electrostatic sprayer remain moist that is consistent with the recommended contact time associated with the disinfectant being used.” – This illustrates what is suggested and implied (and also inherent, as demonstrated) in Lastoskie. Once the substrates are dry, it is presumed that sufficient liquid has been applied to disinfect and that the surfaces are disinfected by the time the surfaces are dry, a time at which the disinfectants/ionized molecules are no longer effective because not in aqueous solution.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the drying time of Lastoskie by the disinfectant time of Multi-Clean because a person of ordinary skill in the art would be motivated, based on the express object of Lastoskie to determine wetting and drying times for determining properties of disinfecting using an electrostatic fluid, look to Multi-Clean to discern that the disinfecting time is limited by the application and drying (“wetness”) time. (Lastoskie Page 1, Executive Summary “In the wake of the global COVID-19 crisis, thorough and efficient cleaning procedures are desired to support a healthy environment and cost-effective return to pre-pandemic air traffic levels. This paper presents initial research findings on semi-automated disinfection methods, which can provide the ability to disinfect surfaces more consistently and efficiently than manual application of chemicals. Boeing conducted two studies, called Design of Experiment (DOE) 1: Characterization of Electrostatic Sprayer Application of Disinfectants and DOE 2: Wetting and Drying of Representative Substrates, to characterize electrostatic spray disinfection devices (one type of semi-automated device).; Muti-Clean Page 4/7, Item 4 “Targeted Spraying: An electrostatic sprayer adds an electric charge to the spray droplets so they are naturally attracted to the surfaces being sprayed. Each charged liquid particle is attracted to a surface much like opposite poles of a magnet. This targeting results in less wasteful overspray and more uniform coverage. Droplet size is 40-110 microns. Occupancy delays are short with spraying, as you only need to wait until surfaces are dry, usually less than 15 minutes.” Page 5/7 “The EPA is asking for submission of some additional data on electrostatic spray application of disinfectants. One piece of data is a “wetness” test to insure surfaces sprayed with an electrostatic sprayer remain moist that is consistent with the recommended contact time associated with the disinfectant being used.”)
Lastoskie and Lastoskie in view of Telleria and Multi-Clean do not appear to explicitly teach but Lastoskie in view of Hadoux and Lastoskie in view of Telleria (if necessary), Multi-Clean (if necessary) and Hadoux teaches
modify the parametric model to base the fixed set of parameters on location across the test surface of the material by approximating model parameters as continuation functions across the test surface;
estimate the values of the fixed set of parameters as functions of locations on the test surface; and
(Hadoux Abstract “ In the second step, applying an edge-preserving spatial regularization on this score image leads to a lowered background variability. Therefore, in the third step, the pixel-wise classification of the regularized score image is greatly improved.” See Also equation (5) on Page 2364)
PNG
media_image6.png
72
1012
media_image6.png
Greyscale
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the parameterized function of Lastoskie by the spatial regularization of Hadoux because the person of ordinary skill in the art would be motivated, based on the mention in Lastoskie that to simplify the equations, the model assumed no spatial variation in drying properties, to look to Hadoux’s spatial regularization that yields better results in accuracy and robustness relative to other classification methods. (Lasotskie Page 28, Fourth Paragraph “This model makes several simplifying assumptions. The evaporative cooling phase assumes the target object is a sufficiently thick semi-infinite body,9, and the warming phase assumes conductive heat transfer with no spatial variation and an energy rate density proportional to the difference between the target object and the temperature of the room.”; Hadoux Abstract “In the second step, applying an edge-preserving spatial regularization on this score image leads to a lowered back ground variability. Therefore, in the third step, the pixel-wise classification of the regularized score image is greatly improved. […] The effectiveness of our method was evaluated with three remotely sensed HS images. Its robustness was also assessed for different training sets, since the latter has a crucial influence on classification performance. On average, our method gave better results in terms of classification accuracy and was more robust than other classification methods tested with the same images.”)
Regarding claim 8, claim 8 recites the method features executed by the apparatus of claim 1, so claim 8 is rejected based at least on the rationale applied to claim 1.
Regarding claim 15, claim 15 recites a CRM that effectively operates as the memory of claim 1, so claim 15 is rejected based at least on the rationale applied to claim 1.
Claims 2, 9, and 16
Regarding claim 2, Lastoskie, Telleria, and Multi-Clean teach the features of claim 1. Lastoskie further teaches:
wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further predict a drying time for the working surface of the material by evaporation of the decontamination liquid, from the radiation emitted as predicted across the working surface over time. (Lastoskie Page 28, Second Paragraph “In order to quantify the dry-time, a mathematical model was developed and fit to the raw IR pixel data. This model allowed for more accurate processing than working with the raw data directly.”; - This teaches that drying time is determined based on radiation emitted.)
The motivations to combine, if necessary, are the same as for claim 1.
Regarding claim 9, claim 9 teaches the method carried out by the apparatus of claim 2, so claim 9 is rejected based at least on the rationale applied to claim 2.
Regarding claim 16, claim 16 recites a CRM that effectively operates as the memory of claim 2, so claim 16 is rejected based at least on the rationale applied to claim 2.
Claims 3, 10, and 17
Regarding claim 3, Lastoskie, Telleria, and Multi-Clean teach the features of claim 1. Lastoskie further teaches:
wherein the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further determine that the working surface of the material is decontaminated when the drying time is at least a specified decontamination time. (Lastoskie Page 1, Last Paragraph – Page 2, First Paragraph “Thorough and efficient cleaning procedures must be implemented to return to pre-pandemic air traffic levels quickly and cost-effectively. For air carriers to retain an average turn-around time of 30 minutes for a single aisle aircraft, the target disinfection duration is 10 minutes. Manual application of disinfectant, with spray bottles and cleaning cloths, cannot be completed in the targeted timeframe without a significant increase in the cleaning crew manpower and therefore cost. Another challenge is the possible variability of manual cleaning. Evenness of application on contaminated surfaces is important, but cannot be guaranteed between individuals who may spray with different timing, frequency and force. This underscores the important potential of using semi-automated mechanisms for disinfectant application.” Page 10, Third Paragraph “Boeing recommends spraying at distances between 2 and 4 feet. By keeping the spray distance under 4 feet, the operator can avoid insufficient deposition, especially if the spraying is upwards, or towards a vertical surface (Figure 7). As spray deposition amount is correlated to disinfection effectiveness, low deposition should be avoided. By keeping the distance at greater than 2 feet, the operator can control dry time. Spraying closer than 2 feet may result in significantly longer dry times (Figure 4).” Page 29, Second Paragraph “Setting the threshold to 90% of the warming asymptote is a heuristic, which is rationalized since it is where the pixel intensity is “close enough” to thermal equilibrium, and well past the point governed by evaporative cooling.” Page 10, Section 8.2 “The disinfectants considered were: […] 4. Oxidizers, including chlorine dioxide, and peroxygens, such as hydrogen peroxide and peracetic acid, which inactivate virus by denaturation of proteins, disruption of the lipid bilayer, and oxidation of sulfur bonds in proteins. 5. Quaternary ammonium salts, such as benzylalkonium chloride, which inactivate virus by disruption of the lipid bilayer and denaturation of proteins. The following criteria were applied to eliminate disinfectants: […] d. Certain Oxidizers were eliminated because they are highly corrosive depending on the concentration and contact time. Chlorines and Peracetic acid were ruled out especially at lower concentrations because they react to oxygen and water quickly after application.” Also, see Figure 27 – Lastoskie at least implies if not expressly states that the decontamination time is at least as long the drying time. That is why Lastoskie is quantifying the drying time. Also, As previously discussed, a drying time is inherently treated as the least decontamination time because it is a representation of time when the electrostatic aqueous solution is effective to disinfect/decontaminate. If necessary, Multi-Clean also teaches this feature (Multi-Clean Page 4/7, Item 4 “Targeted Spraying: An electrostatic sprayer adds an electric charge to the spray droplets so they are naturally attracted to the surfaces being sprayed. Each charged liquid particle is attracted to a surface much like opposite poles of a magnet. This targeting results in less wasteful overspray and more uniform coverage. Droplet size is 40-110 microns. Occupancy delays are short with spraying, as you only need to wait until surfaces are dry, usually less than 15 minutes.” Page 5/7 “The EPA is asking for submission of some additional data on electrostatic spray application of disinfectants. One piece of data is a “wetness” test to insure surfaces sprayed with an electrostatic sprayer remain moist that is consistent with the recommended contact time associated with the disinfectant being used.” – This teaches the drying time is at least as long as the decontamination time/ time when occupancy is not allowed.)
The motivations to combine, if necessary, are the same as for claim 1.
Regarding claim 10, claim 10 teaches the method carried out by the apparatus of claim 3, so claim 10 is rejected based at least on the rationale applied to claim 3.
Regarding claim 17, claim 17 recites a CRM that effectively operates as the memory of claim 3, so claim 17 is rejected based at least on the rationale applied to claim 3.
Claims 4, 11, and 18
Regarding claim 4, Lastoskie, Telleria, and Multi-Clean teach the features of claim 1. Lastoskie further teaches:
wherein the training data is obtained from an experiment designed to test an effect of a number of experimental factors on the radiation emitted across the test surface over time, and the modified parametric model is deployed to predict the radiation emitted under particular levels of the number of experimental factors. (Lastoskie Page 24 “The data collection for the DOE 1 study was done in phases with an initial DOE, called Bridge 0 DOE, and then additional DOEs, called Bridge 1 to 3 DOEs. The goal of the bridge DOEs was to systematically build bridges between the existing test conditions and new test conditions so that all of the data could be combined into a final statistical model that related inputs to outputs and also to quantify which inputs had a larger effect on the outputs.“ Also, see Tables 8-11 specifying the different parameters on pages 24-25. – The experiment was designed to test the effect of a number of experimental factors on the radiation emitted across the test surfaces over time, and the resulting model is fitted for deployment under particular levels of the experimental factors. If necessary, Telleria also explicitly teaches the processing circuitry configured to execute the program code: Telleria [0038] “Accordingly, the control system 322 can drive the drywalling system 100 to perform various suitable tasks, with some or all portions of such tasks being automated and performed with or without user interaction. The control system can comprise various suitable computing systems, including one or more processor and one or more memory storing instructions that if executed by the one or more processer, provide for the execution of tasks by the automated drywalling system 100 as discussed in detail herein. “ [0170] “The system 100 can also determine when such materials have set and/or dried by measuring moisture content of such materials, thermal conductivity of a covered seam 620, using a thermal imaging camera or thermometer (contact or non-contact), by detecting differences in colors using a camera, and the like.”)
The motivations to combine, if necessary are the same as for claim 1.
Regarding claim 11, claim 11 teaches the method carried out by the apparatus of claim 4, so claim 11 is rejected based at least on the rationale applied to claim 4.
Regarding claim 18, claim 18 recites a CRM that effectively operates as the memory of claim 4, so claim 18 is rejected based at least on the rationale applied to claim 4.
Claims 5, 12, and 19
Regarding claim 5, Lastoskie, Telleria, and Multi-Clean teach the features of claim 4. Lastoskie further teaches:
wherein the experiment is also designed to test the effect of the number of experimental factors on coverage of the decontamination liquid across the test surface, and the processing circuitry is configured to execute the computer-readable program code to cause the apparatus to further carry out the experiment to determine the particular levels of the number of experimental factors that optimize the coverage of the decontamination liquid. (Lastoskie Page 27, Last Paragaph- Page 28, First Paragraph “In order to characterize the spray coverage over a target object, a binary value was assigned to each pixel to indicate whether the portion of the target object represented by that pixel received a measurable amount of spray. Specifically, the test evaluated whether or not there was a significant drop in pixel intensity between a four second duration at the start of the video and a one second duration after the identified spray-time for that video. For each pixel in the video’s ROI, we calculated p-values using a two-sample t-test and corrected for multiple tests to have a false discovery rate of 0.05. Spray coverage was reported as the percentage of pixels within a ROI that had a significant change.” – Coverage of the liquid is determined. Page 24 “The data collection for the DOE 1 study was done in phases with an initial DOE, called Bridge 0 DOE, and then additional DOEs, called Bridge 1 to 3 DOEs. The goal of the bridge DOEs was to systematically build bridges between the existing test conditions and new test conditions so that all of the data could be combined into a final statistical model that related inputs to outputs and also to quantify which inputs had a larger effect on the outputs.“ Also, see Tables 8-11 specifying the different parameters on pages 24-25. Page 10, Conclusions “Electrostatic sprayers have an advantage over conventional spray and wipe in that a large area can be covered with disinfectant in an efficient manner with minimal time. Importantly, spray droplets can attach to areas not directly in the sprayed path. No assessment of the effectiveness of periodic wiping after electrostatic spray application was conducted. However, periodic wiping of surfaces is recommended to remove surface contaminants and reduce the accumulation of surfactants. Per the results in section 4, the following recommended best practices for the operation of ES devices were created for air carriers. Based on the potential risks that mists present to environmental control system components, Boeing does not recommend the use of fogger type semi-automated disinfection. Commercially available ES devices fitted with nozzles to create cone shaped spray patterns had equivalent performance related to deposition and dry time throughout the studies. The sprayer brand did control the positive or negative charge on the aerosolized liquid. The charge did not impact operational performance, but may contribute to the ability to destroy virus. Boeing recommends spraying at distances between 2 and 4 feet. By keeping the spray distance under 4 feet, the operator can avoid insufficient deposition, especially if the spraying is upwards, or towards a vertical surface (Figure 7). As spray deposition amount is correlated to disinfection effectiveness, low deposition should be avoided. By keeping the distance at greater than 2 feet, the operator can control dry time. Spraying closer than 2 feet may result in significantly longer dry times (Figure 4). When spraying non-porous materials, the DOE 2 results suggest that temperature and humidity had a definitive impact on dry time, particularly for non-porous materials. From contour plots presented in Figure 12, an ambient temperature (in the tested range of 68 to 90.5 Fahrenheit) and high humidity led to the longest dry times, exceeding 15 minutes. Boeing also recommends that spraying be done using a sweeping motion, traversing a 90degree arc in about 5 seconds. This appears to capture the natural human motion of spraying. We recognize that operators may not mimic the exact motion that was utilized in this study, and, in practice, a certain amount of learning will be required to create the appropriate motion to obtain the nominal amount of deposition.” – The experiment was designed to test the effect of a number of experimental factors on the radiation emitted across the test surfaces over time, and the resulting model is fitted for deployment under particular levels of the experimental factors. The conclusion suggests the optimal values of the experimental factors and deploys the model for those parameters and to optimize the coverage.)
The motivations to combine, if necessary are the same as for claim 1.
Regarding claim 12, claim 12 teaches the method carried out by the apparatus of claim 5, so claim 12 is rejected based at least on the rationale applied to claim 5.
Regarding claim 19, claim 19 recites a CRM that effectively operates as the memory of claim 5, so claim 19 is rejected based at least on the rationale applied to claim 5.
Claims 6, 13, and 20
PNG
media_image7.png
391
350
media_image7.png
Greyscale
Regarding claim 6, Lastoskie, Telleria, and Multi-Clean teach the features of claim 5. Lastoskie further teaches:
wherein the first thermographic images are captured before the decontamination liquid is deposited on the test surface, and the second thermographic images are captured after the decontamination liquid is deposited on the test surface, and (Lastoskie Figure 25, Left Figure – Figure 25 shows the data from the thermographic images is collected prior to spraying/liquid deposition, at the time of spraying (which lasts 5 or 10 seconds for eac position), and after the time of spraying.)
wherein the apparatus caused to carry out the experiment includes the apparatus caused to determine the coverage of the decontamination liquid across the test surface from the first thermographic images and the second thermographic images. (Lastoskie Page 27, Last Paragaph- Page 28, First Paragraph “In order to characterize the spray coverage over a target object, a binary value was assigned to each pixel to indicate whether the portion of the target object represented by that pixel received a measurable amount of spray. Specifically, the test evaluated whether or not there was a significant drop in pixel intensity between a four second duration at the start of the video and a one second duration after the identified spray-time for that video. For each pixel in the video’s ROI, we calculated p-values using a two-sample t-test and corrected for multiple tests to have a false discovery rate of 0.05. Spray coverage was reported as the percentage of pixels within a ROI that had a significant change.” – Coverage of the liquid is also determined by the automation of Lastoskie.)
The motivations to combine, if necessary are the same as for claim 1.
Regarding claim 13, claim 13 teaches the method carried out by the apparatus of claim 6, so claim 13 is rejected based at least on the rationale applied to claim 6.
Regarding claim 20, claim 20 recites a CRM that effectively operates as the memory of claim 6, so claim 20 is rejected based at least on the rationale applied to claim 6.
Claims 7 and 14
Regarding claim 7, Lastoskie, Telleria, and Multi-Clean teach the features of claim 4. Lastoskie further teaches:
wherein the decontamination liquid is deposited on the test surface in an environment and using an electrostatic sprayer, and (Lastoskie Page 1, Second Paragraph “Boeing conducted two studies, called Design of Experiment (DOE) 1: Characterization of Electrostatic Sprayer Application of Disinfectants and DOE 2: Wetting and Drying of Representative Substrates, to characterize electrostatic spray disinfection devices (one type of semi-automated device). Based on those studies and as discussed in greater detail below, the following is recommended with respect to electrostatic spray devices:” – The test is conducted using a electrostatic sprayer with decontamination fluids.)
wherein the training data is obtained from the experiment designed to test the effect of the number of experimental factors including multiple ones of the material, a profile of the test surface of the material, a temperature of the environment, a humidity of the environment, the electrostatic sprayer, the decontamination liquid, or an orientation of the electrostatic sprayer with respect to the test surface when the decontamination liquid is deposited. (Lastoskie Page 1, Second Paragraph and Bullet Points “Based on those studies and as discussed in greater detail below, the following is recommended with respect to electrostatic spray devices: Do not use fogger type semi-automated disinfection, based on potential risks to environmental control system components. When using an electrostatic spray device: o Use a device that generates a cone shaped spray pattern. o Maintain a distance of 2-4 feet between the nozzle and surface. o Move the nozzle of the sprayer through a 90 degree arc in roughly 5 seconds to establish a baseline spray traverse speed. o Note that within a 40-80% humidity and 68-90.5°F range, the required dry time of nonporous materials increases inversely with temperature, proportionally to the percentage relative humidity.” - The claimed experimental factors were considered and these are the recommended values of the experimental factors based on the experiment conducted. Page 3, First Two Paragraphs “A test matrix was created using principles of statistical Design of Experiments (DOE) to vary input parameters (Table 1). The deposition were measured by weight gain and dry time. By systematically controlling the variables, 695 tests were used to build a statistical model to predict the outputs of all possible combinations. For more information, refer to Appendix 8.5. In each test, a 3-by 4-foot material coupon was sprayed by a person. The ES device was swept through a 90-degree arc in 5 or 10 seconds; an approximation of the speed at which a crew person might disinfect a space.” Also, see Table 1: The variable input parameters of the DOE 1 study – This provides a list of the experimental factors considered, which include the claimed factors.)
Regarding claim 14, claim 14 teaches the method carried out by the apparatus of claim 7, so claim 14 is rejected based at least on the rationale applied to claim 7.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL: “Imaging the Drying of Surfaces by Infrared Thermography” by Fike et al. (Teaches using thermographic imaging to determine drying of surfaces)
NPL: “Control of citrus surface drying by image analysis of infrared thermography” by Fito et al. (Teaches using thermographic imaging to determine drying of surfaces of citrus fruit)
US 2023/0222822 A1 to Jefferson et al. (Teaches using thermal imaging data as a model input to determine drying times for fruit)
US 20190093373 A1 to Telleria et al. (Teaches using thermal imaging data as a model input to determine drying times for paint on dry wall)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY MICHAEL WHITE whose telephone number is (571) 272-7073. The examiner can normally be reached Mon-Fri 11:00-7:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.M.W./Examiner, Art Unit 2188
/MICHAEL EDWARD COCCHI/Primary Examiner, Art Unit 2188
Prosecution Insights