DETAILED ACTION
Receipt of Applicant’s amendment filed 11/13/2025 is acknowledged.
Claims 1, 8, and 15 have been amended.
Claim 7 has been canceled.
Claims 1-6 and 8-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the
references as applied to the claims below for the convenience of the applicant. Although
the specified citations are representative of the teachings in the art and are applied to
the specific limitations within the individual claim, other passages and figures may apply
as well. Examiner may also include cited interpretations encompassed within parenthesis, e.g. (Examiner' s interpretation), for clarity. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 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.
Response to Arguments
Claim Rejections under 35 U.S.C. § 101:
Acknowledgement is made of amended independent claims 1, 8 and 15. Applicants arguments have been fully considered, and after careful re-evaluation
under the Office’s subject matter eligibility test, Applicant’s arguments were not persuasive. Rejections to claims are maintained.
Applicant argues [Pg.2-3] that the recited claims do not fall within the enumerated groupings of abstract ideas, specifically Mental Processes and Mathematical Concepts as the Examiner determined in previous Office Action dated 09/23/2025. After careful consideration and re-evaluation, Examiner respectfully disagrees. As described in Claim Rejections - 35 USC § 101 section below, the amended claim limitations further recite Mental Processes performed on a computer and/or Mathematical Concepts, given the broadest reasonable interpretation. For clarity, additional information was provided in rejection section below.
Applicant also argues [Pg.3 P.2] the amended claims incorporate into a practical application (i.e. provides an improvement to technology), therefore amounting to significantly more than the abstract idea. The example disclosed as incorporation into a practical application is the limitation of “automatically triggering an air filtration system” based on recalibrated model output. Examiner has considered this argument and respectfully disagrees. As described in Claim Rejection 35 USC 101 section below, “automatically triggering an air filtration system”, when considering the claim as a whole, amounts to post-solution activity. Per MPEP 2106.05(g), “An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.” This limitation is analogous to cited example. i.e. The air filtration system is equivalent to the printer, yet the claim as a whole is directed towards the calibration of a model - i.e. the additional element is not integrated into the claim as a whole. Also, per MPEP 2106.05(g), “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept”.
Claim Rejections under 35 U.S.C. § 103:
Acknowledgement is made of amended independent claims 1, 8 and 15. Applicants arguments have been fully considered, and after careful re-evaluation,
Applicant’s arguments were not persuasive. Rejections to claims are maintained.
Applicant argues [Pg.4-5] that the amendments to independent claims introduce limitations that are not disclosed by White. Applicant arguments have been fully considered and are persuasive. Therefore, rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of White, Viguerie, and Chakrabarty, as detailed in the updated rejection of this Office Action. Bhaskaran (prior art made of record but not relied upon within Office Action dated 09/23/2025) discloses “searching a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem” and “wherein the identified similar problem is applicable to a domain unrelated to the modeling problem”, Chakrabarty discloses “calibrating the modeling problem using information of the identified similar problem” and “the calibrating changes parameters for the modeling problem according to the identified similar problem”. Remaining limitations are disclosed by additional prior art references, as stated herein, wherein the prior art references would have been obvious to combine, as further stated herein in the rejections of this Office Action, such that one of ordinary skill would arrive at the claimed invention. For the reasons stated in this response, in conjunction with the updated rejection of this Office Action, the pending claims are rejected under 35 U.S.C 103.
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 1-6, 8-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception (an abstract idea), as it has not been integrated into a practical application and the claim(s) further do/does not recite significantly more than the judicial exception. Examiner has evaluated the claim(s) under the framework provided in MPEP 2106 and has provided such analysis below.
To determine if a claim is directed to patent ineligible subject matter, the Court
has guided the Office to apply the Alice/Mayo test, which requires:
Step 1. Determining if the claim falls within a statutory category of a Process, Machine, Manufacture, or a Composition of Matter (see MPEP 2106.03);
Step 2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea (MPEP 2106.04);
Step 2A is a two-prong inquiry. MPEP 2106.04(II)(A).
Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2).
The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Step 1:
Claims 1-6 are directed to a method, as such these claims fall within the statutory category of a process.
Claims 8-14 are directed to a system, as such these claims fall within the statutory category of a machine.
Claims 15-20 are directed to a computer readable storage medium, as such these claims fall within the statutory category of manufacture. Examiner notes Applicant discloses “A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se” Spec. Para [0084].
Step 2A, Prong 1:
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims cover Mental Processes performed on a computer and/or Mathematical Concepts, given the broadest reasonable interpretation.
In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
As per claim 1, the claim recites the limitations of:
A method performed by at least one hardware processor, the method
comprising: searching a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid. Specifically, this limitation recites mental processes performed on a computer. Per MPEP 2106.04(a)(2)(A), “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; [and] a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind”. The recited limitation is analogous to the aforementioned examples.)
the similar problem being identified based on creating a mapping between parameter sets of at least some of the initial conditions of the modeling problem and associated error values, (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. A mathematical relationship is a relationship between variables or numbers (i.e. mapping parameter sets and associated error values). A mathematical relationship may be expressed in words or using mathematical symbols. Creating this mapping merely consists of defining that mathematical relationship, which additionally, can reasonably be performed in the human mind with/without the aid of pen/paper.)
and searching the database for mappings associated with the prior calibrated models similar to the created mapping, the associated error values representing sensitivities of parameter values against an output of the modeling problem; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer. Per MPEP 2106.04(a)(2)(A), “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; [and] a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind”. The recited limitation is analogous to the aforementioned examples.)
Step 2A, Prong 2:
As per claim 1, this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present Mere Instructions To Apply An Exception and/or Insignificant Extra Solution Activity. In particular, the claim recites the additional limitations:
receiving a modeling problem having undefined parameter range to
calibrate, the modeling problem including a disease transmission modeling with initial conditions that include at least numbers of susceptible, exposed, infectious, recovered and not-survived entities in a dynamic physical environment; (The additional feature(s) are directed towards Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). 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.)
calibrating the modeling problem using information of the identified similar problem, wherein the identified similar problem is applicable to a domain unrelated to the modeling problem, and wherein the calibrating changes parameters for the modeling problem according to the identified similar problem; (The additional feature(s) are directed towards Mere Instructions to Apply an Exception per MPEP 2106.05(f). 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) or are more than mere instructions to implement an abstract idea or other exception on a computer. Specifically, the limitation recites only the idea of a solution or outcome i.e. fails to recite details of how the modeling problem is calibrated “according to the identified similar problem”. Additionally, the limitation invokes computers or other machinery merely as a tool to perform an existing process (e.g. changing parameters of a mathematical model.)
monitoring the accuracy of calibrated modeling problem; (The additional feature(s) are directed towards Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, the limitation invokes computers or other machinery merely as a tool to perform an existing process. Per MPEP 2106.05(f)(2), “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 integrate a judicial exception into a practical application or provide significantly more. [ ] Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: [ ] iii. A process for monitoring audit log data that is executed on a general-purpose computer”.)
recalibrating the modeling problem until a performance criterion is met; (The additional feature(s) are directed towards Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, the limitation recites only the idea of a solution or outcome i.e. fails to recite details of how the modeling problem is recalibrated “until a performance criterion is met”. Additionally, the limitation invokes computers or other machinery merely as a tool to perform an existing process (e.g. changing parameters of a mathematical model while evaluating associated criteria.)
storing the recalibrated modeling problem in the database; (The additional feature(s) are considered to disclose Mere Instructions to Apply an Exception per MPEP 2106.05(f). Per MPEP 2106.05(f)(2), “Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 integrate a judicial exception into a practical application or provide significantly more.”)
and based on an output of the recalibrated modeling problem, automatically triggering an air filtration system to circulate air in a space (The additional feature(s) are considered to disclose Insignificant Extra-solution Activity (post-solution activity, mere data outputting) per MPEP 2106.05(g) and/or Mere Instructions to Apply an Exception per MPEP 2106.05(f). Per MPEP 2106.05(g), “An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.” This limitation is analogous to cited example. i.e. The air filtration system is equivalent to the printer, yet the claim as a whole is directed towards the calibration of a model - i.e. the limitation is not integrated into the claim as a whole. Per MPEP 2106.05(f), “When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.” The cited limitation fails to disclose what output from the modelling problem, and how that output “automatically” triggers an air filtration system.)
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B:
For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are considered directed towards mere instructions to apply an exception and/or insignificant extra-solution activity.
Per MPEP 2106.05(d)(II), 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, (i.e. receiving a modelling problem, automatically triggering an air filtration system)
ii. Performing repetitive calculations, (i.e. calibrating/recalibrating the modelling problem)
iii. Electronic recordkeeping,
iv. Storing and retrieving information in memory, (i.e. storing the recalibrated modelling problem)
For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Independent claim 8 is directed to a system comprising at least one hardware processor; and a memory device coupled with said at least one hardware processor. The additional feature(s) are considered to disclose Mere Instructions To Apply An Exception (abstract idea) using a generic computer - MPEP 2106.05(f). The remaining limitations recite substantially the same subject matter as Independent claim 1 and are rejected under similar rationale.
Independent claim 15, is directed to a computer program product comprising a computer readable storage medium having program instructions. The additional feature(s) are considered to disclose Mere Instructions To Apply An Exception (abstract idea) using a generic computer - MPEP 2106.05(f). The remaining limitations recite substantially the same subject matter as Independent claim 1 and are rejected under similar rationale.
Claim 2 further recites, wherein the information includes statistical description of initial conditions and calibrated model results with parameter distributions. The additional feature(s) elaborate on the information used to calibrate the modelling problem, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Claim 3 further recites, further including generating the database of the prior calibrated models, the database storing statistical description of initial conditions and calibrated model results with parameter distributions associated with each of the prior calibrated models. The additional feature(s) elaborate on the data stored within the database, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Claim 4 further recites, wherein said calibrating includes performing parameter reduction technique. The additional feature(s) elaborate on calibrating the modelling problem, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Claim 5 further recites, wherein said monitoring includes monitoring for convergence to an error threshold. The additional feature(s) elaborate on accuracy monitoring, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Claim 6 further recites, further including testing with historical results and tracking loss functions to determine performance of the calibrated modeling problem. The additional feature(s) elaborate on the performance criterion while recalibrating the modelling problem, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is considered to be ineligible under 35 U.S.C 101
Claim 7 is canceled.
Claims 9 and 16 are directed to substantially the same subject matter as claim 2 and are rejected under similar rationale and further failure to add significantly more.
Claims 10 and 17 are directed to substantially the same subject matter as claim 3 and are rejected under similar rationale and further failure to add significantly more.
Claims 11 and 18 are directed to substantially the same subject matter as claim 4 and are rejected under similar rationale and further failure to add significantly more.
Claims 12 and 19 are directed to substantially the same subject matter as claim 5 and are rejected under similar rationale and further failure to add significantly more.
Claims 13 and 20 are directed to substantially the same subject matter as claim 6 and are rejected under similar rationale and further failure to add significantly more.
Claim 14, the system of claim 8, further recites, wherein an output of the calibrated modeling problem automatically triggers a physical barrier to open or close. The additional feature(s) elaborate on the modelling problem output, thus further amounts to Insignificant Extra-solution Activity (post-solution activity, mere data outputting) per MPEP 2106.05(g) and/or Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is considered to be ineligible under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham V. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 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.
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 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.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chakrabarty et al. US Pub. No. 2023/0106530 A1 (hereinafter referred to as “Chakrabarty”), in view of Viguerie, Alex, et al. "Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion." Applied mathematics letters 111 (2021): 106617 (hereinafter referred to as “Viguerie”), in further view of Bhaskaran US Patent No. 10268963 B2 (hereinafter referred to as “Bhaskaran”).
Regarding independent (amended) claim 1, Chakrabarty teaches, A method performed by at least one hardware processor, (“a calibration system and a calibration method that learns from simulation failures and incorporate this information to increase the probability of selecting sets of parameters that lead to successful simulations while avoiding the expenditure of significant time and resources.” Chakrabarty [P.0007], “The calibration system comprises: at least one processor” Chakrabarty [P.0023])
the method comprising: receiving a modeling problem having undefined parameter range to calibrate, (“a general model of a dynamical system (also referred to as “the industrial system 103”) is denoted by equation (1):
y
0
:
T
=
M
T
(
θ
) where
y
0
:
T
represents output of a model
M
T
(
θ
) of the dynamical system, the constant parameters of the model are described by
θ
∈
Θ
⊂
R
n
θ
. Further, assume that the admissible set of parameters Θ is known. For instance, Θ could denote a set of upper and lower bounds on parameters obtained from archived data or domain knowledge. The model
M
T
(
θ
) is a like a black-box, where a user may not be able to tune parameters of the model
M
T
(
θ
). Therefore, range of Θ of the parameters may be purely a guess (i.e. undefined parameter range).“ Chakrabarty [P.0055])
the similar problem being identified based on creating a mapping between parameter sets of at least some of the initial conditions of the modeling problem and associated error values (“trained for mapping between various combinations of different values of the parameters of the model of the industrial system and their corresponding calibration errors.” Chakrabarty [P.0013]),
and searching the database for mappings associated with the prior calibrated models similar to the created mapping (Referring to Fig. 1B (see below), “The simulation model obtains values θj of different combination of parameters for each simulation cycle from a database 113, where the database 113 is configured to store operational data Dj comprising different values of each parameter in their admissible range. Each simulation cycle includes execution of the simulation model 115 with different combinations of values θj of the parameters selected within the admissible range of values of the parameters.” Chakrabarty [P.0068]. The parameters stored in the database (113) are interpreted to have been searched prior to simulation (115) because “parameters θ are selected from the admissible parameter search domain Θ (i.e., θ∈Θ)” Chakrabarty [P.0082])
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599
782
media_image1.png
Greyscale
the associated error values representing sensitivities of parameter values against an output of the modeling problem; (“for a given a combination of different values of the different parameters of the model of thermal dynamics, the probabilistic parameter-to-cost regressor provides not only a calibration error but also a confidence range (i.e. sensitivities) around the calibration error.” Chakrabarty [P.0068]. The confidence range is interpreted as representing sensitivities due to Applicant’s disclosure “sensitivity indices quantify how much of the total variance of the model output each uncertain parameter (or input) is responsible for.” Spec. [P.0028].)
calibrating the modeling problem using information of the identified similar problem; (“The calibration system 101 further comprises an acquisition function module 107d that uses an acquisition function to choose parameters such that the failure region ΘF is avoided [ ] The acquisition function is executed to identify a combination of parameters (i.e. similar problem information) having the maximum likelihood of minimizing a calibration error according to the probabilistic parameter-to-cost mapping.” Chakrabarty [P.0076].)
,
and wherein the calibrating changes parameters for the modeling problem according to the identified similar problem (“the calibration system 101 comprises a simulation model (MT(θ)) 115 which is used to simulate an operation of the industrial system 103 multiple times [ ] the simulation model 115 is simulated multiple times for different combinations of parameters, where values of parameters may be different during each simulation cycle.” Chakrabarty [P.0068]);
monitoring the accuracy of calibrated modeling problem; (“for a given a combination of different values of the different parameters of the model of thermal dynamics, the probabilistic parameter-to-cost regressor provides not only a calibration error but also a confidence range around the calibration error. In response, the convergence rate of Bayesian optimization increases and the computational burden of the calibration system reduces.” Chakrabarty [P.0068]. The convergence rate disclosed by Chakrabarty is interpreted to mean “monitoring the accuracy” due to Applicant’s disclosure “The processor 202 may monitor the calibration process for convergence” Spec. [P.0024] )
recalibrating the modeling problem until a performance criterion is met; (“The calibration system further comprises a probabilistic parameter-to-cost regressor [ ] the parameter-to-cost regressor is trained iteratively until a termination condition (i.e. performance criterion) is met” Chakrabarty [P.0013-14])
storing the recalibrated modeling problem in the database; (“the database 113 is configured to store operational data Dj comprising different values of each parameter in their admissible range. Each simulation cycle includes execution of the simulation model 115 with different combinations of values θj of the parameters selected within the admissible range of values of the parameters” Chakrabarty [P.0068]. Reference Fig. 1B above.)
and based on an output of the recalibrated modeling problem, automatically triggering an air filtration system to circulate air in a space (“the industrial system 103 may include a controller that determines control inputs for actuators of the industrial system 103, based on the optimal combination of the different parameters received from the calibration system 101” Chakrabarty [P.0053]. Actuators of the industrial system are interpreted to control an air filtration system because “the HVAC system includes actuators including an indoor fan, an outdoor fan, an expansion valve actuator, and the like. The actuators may be controlled according to corresponding control inputs, e.g., a speed of the indoor fan, a speed of the outdoor fan, a position of the expansion valve, a speed of a compressor, and the like.” Chakrabarty [P.0049]
Chakrabarty fails to specifically disclose the modeling problem including a disease transmission modeling with initial conditions that include at least numbers of susceptible, exposed, infectious, recovered and not-survived entities in a dynamic physical environment; searching a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem.
However, the analogous art of Viguerie discloses, the modeling problem including a disease transmission modeling with initial conditions that include at least numbers of susceptible, exposed, infectious, recovered and not-survived entities in a dynamic physical environment (“Initial conditions for the subpopulations s, e, i, r, and d in the model are defined by means of Gaussian circular functions” Viguerie [Pg.3 Sec.3 P.2]. Note, “s, e, i, r, and d” represent susceptible (s), exposed (e), infected (i), recovered (r), and deceased (d).)
Chakrabarty and Viguerie are analogous art as they both relate to physical system modelling. Chakrabarty [Abstract] discloses “A calibration system and method for calibrating a model of dynamics of an industrial system” and Viguerie [Abstract] discloses “an early version of a Susceptible–Exposed–Infected–Recovered– Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatiotemporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features.”
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant’s effective filling date of the claimed invention to have modified the model calibration method, as taught by Chakrabarty, to incorporate a SEIRD model, as Viguerie discloses, for the benefit of informing “health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources” Viguerie [Abstract].
Like Chakrabarty, Viguerie also fails to specifically disclose searching a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem.
However, the analogous art of Bhaskaran discloses searching a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, (“The hazard simulator 102 is configured to access data (e.g. calibrated models) from a plurality of different sources (i.e. different domains) and to use those data to generate information representing the hazard situation. The generated information may be a model (e.g. calibrated model) of how the hazard situation is predicted to develop in the physical domain over a period of time [ ] The hazard simulator 102 analyses the data from the different sources in order to generate the information, this process may also be referred to as exploration (i.e. searching). The hazard simulator 102 analyses (i.e. identifies) the data from the plurality of data sources using data analytics tools. For example, in a flood emergency situation, the hazard simulator 102 may employ a geographical information system (e.g. domain) (GIS) to map potential inundation areas using a source of flood forecasting information (e.g. different domain) combined with a digital elevation model (e.g. different domain). Combining this inundation information with the demographic data and local infrastructure information using the GIS tool, information is generated which provides insight into agent behavior, and thus provides a basis for rule generation” Bhaskaran [Col.7 Ln.55 – Col.8 Ln.13]. The hazard simulator 102 generated information is is analogous to calibrated models given Applicant’s disclosure “the calibrated problem 110 can be a disease modeling problem which may predict a disease transmission or progression rate or the like.” Spec. [P.0024]. The hazard simulator 102 is also interpreted to comprise a database because “the hazard simulator comprises a federated database server configured to retrieve data from the plurality of data sources, and a data exploration engine configured to analyze the retrieved data in order to generate the information representing the hazard situation.” Bhaskaran [Col.3 Ln.7-11]),
and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem (Bhaskaran [Col.7 Ln.55 – Col.8 Ln.13] discloses identifying a modeling problem applicable to unrelated domains (e.g. geographical information, flood forecasting information, etc.).)
Bhaskaran is analogous art as it relates to disaster risk reduction and response, specifically to a decision support system that assimilates data from multiple sources including real-time data, models agent behavior in hazard situations using agent-based simulations, and determines optimal intervention actions to reduce disaster risks and manage hazard situations effectively. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie combination to incorporate data from multiple different sources/domains, as Bhaskaran discloses, in order to effectively “identify and evaluate risk” Bhaskaran [Col.1 Ln.35] associated with hazard scenarios, such as the spread of infectious disease.
Regarding independent (amended) claim 8, Chakrabarty teaches, A system comprising: at least one hardware processor; and a memory device coupled with said at least one hardware processor; said at least one hardware processor configured to at least: (“a calibration system and a calibration method that learns from simulation failures and incorporate this information to increase the probability of selecting sets of parameters that lead to successful simulations while avoiding the expenditure of significant time and resources.” Chakrabarty [P.0007], “The calibration system comprises: at least one processor; and memory having instructions stored thereon that, when executed by the at least one processor, cause the calibration system to:” Chakrabarty [P.0023])
receive a modeling problem having undefined parameter range to calibrate, (“a general model of a dynamical system (also referred to as “the industrial system 103”) is denoted by equation (1):
y
0
:
T
=
M
T
(
θ
) where
y
0
:
T
represents output of a model
M
T
(
θ
) of the dynamical system, the constant parameters of the model are described by
θ
∈
Θ
⊂
R
n
θ
. Further, assume that the admissible set of parameters Θ is known. For instance, Θ could denote a set of upper and lower bounds on parameters obtained from archived data or domain knowledge. The model
M
T
(
θ
) is a like a black-box, where a user may not be able to tune parameters of the model
M
T
(
θ
). Therefore, range of Θ of the parameters may be purely a guess (i.e. undefined parameter range).“ Chakrabarty [P.0055])
the similar problem being identified based on creating a mapping between parameter sets of at least some of the initial conditions of the modeling problem and associated error values (“trained for mapping between various combinations of different values of the parameters of the model of the industrial system and their corresponding calibration errors.” Chakrabarty [P.0013]),
and searching the database for mappings associated with the prior calibrated models similar to the created mapping (Referring to Fig. 1B (see below), “The simulation model obtains values θj of different combination of parameters for each simulation cycle from a database 113, where the database 113 is configured to store operational data Dj comprising different values of each parameter in their admissible range. Each simulation cycle includes execution of the simulation model 115 with different combinations of values θj of the parameters selected within the admissible range of values of the parameters.” Chakrabarty [P.0068]. The parameters stored in the database (113) are interpreted to have been searched prior to simulation (115) because “parameters θ are selected from the admissible parameter search domain Θ (i.e., θ∈Θ)” Chakrabarty [P.0082])
PNG
media_image1.png
599
782
media_image1.png
Greyscale
the associated error values representing sensitivities of parameter values against an output of the modeling problem; (“for a given a combination of different values of the different parameters of the model of thermal dynamics, the probabilistic parameter-to-cost regressor provides not only a calibration error but also a confidence range (i.e. sensitivities) around the calibration error.” Chakrabarty [P.0068]. The confidence range is interpreted as representing sensitivities due to Applicant’s disclosure “sensitivity indices quantify how much of the total variance of the model output each uncertain parameter (or input) is responsible for.” Spec. [P.0028].)
calibrate the modeling problem using information of the identified similar problem, (“The calibration system 101 further comprises an acquisition function module 107d that uses an acquisition function to choose parameters such that the failure region ΘF is avoided [ ] The acquisition function is executed to identify a combination of parameters (i.e. similar problem information) having the maximum likelihood of minimizing a calibration error according to the probabilistic parameter-to-cost mapping.” Chakrabarty [P.0076].)
and wherein the calibrating changes parameters for the modeling problem according to the identified similar problem (“the calibration system 101 comprises a simulation model (MT(θ)) 115 which is used to simulate an operation of the industrial system 103 multiple times [ ] the simulation model 115 is simulated multiple times for different combinations of parameters, where values of parameters may be different during each simulation cycle.” Chakrabarty [P.0068]);
monitor the accuracy of calibrated modeling problem; (“for a given a combination of different values of the different parameters of the model of thermal dynamics, the probabilistic parameter-to-cost regressor provides not only a calibration error but also a confidence range around the calibration error. In response, the convergence rate of Bayesian optimization increases and the computational burden of the calibration system reduces.” Chakrabarty [P.0068]. The convergence rate disclosed by Chakrabarty is interpreted to mean “monitoring the accuracy” due to Applicant’s disclosure “The processor 202 may monitor the calibration process for convergence” Spec. [P.0024] )
recalibrate the modeling problem until a performance criterion is met; (“The calibration system further comprises a probabilistic parameter-to-cost regressor [ ] the parameter-to-cost regressor is trained iteratively until a termination condition (i.e. performance criterion) is met” Chakrabarty [P.0013-14])
store the recalibrated modeling problem in the database (“the database 113 is configured to store operational data Dj comprising different values of each parameter in their admissible range. Each simulation cycle includes execution of the simulation model 115 with different combinations of values θj of the parameters selected within the admissible range of values of the parameters” Chakrabarty [P.0068]. Reference Fig. 1B above.);
and based on an output of the recalibrated modeling problem, automatically triggering an air filtration system to circulate air in a space. (“the industrial system 103 may include a controller that determines control inputs for actuators of the industrial system 103, based on the optimal combination of the different parameters received from the calibration system 101” Chakrabarty [P.0053]. Actuators of the industrial system are interpreted to control an air filtration system because “the HVAC system includes actuators including an indoor fan, an outdoor fan, an expansion valve actuator, and the like. The actuators may be controlled according to corresponding control inputs, e.g., a speed of the indoor fan, a speed of the outdoor fan, a position of the expansion valve, a speed of a compressor, and the like.” Chakrabarty [P.0049]
Chakrabarty fails to specifically disclose the modeling problem including a disease transmission modeling with initial conditions that include at least numbers of susceptible, exposed, infectious, recovered and not-survived entities in a dynamic physical environment; search a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem.
However, the analogous art of Viguerie discloses, the modeling problem including a disease transmission modeling with initial conditions that include at least numbers of susceptible, exposed, infectious, recovered and not-survived entities in a dynamic physical environment (“Initial conditions for the subpopulations s, e, i, r, and d in the model are defined by means of Gaussian circular functions” Viguerie [Pg.3 Sec.3 P.2]. Note, “s, e, i, r, and d” represent susceptible (s), exposed (e), infected (i), recovered (r), and deceased (d).)
Chakrabarty and Viguerie are analogous art as they both relate to physical system modelling. Chakrabarty [Abstract] discloses “A calibration system and method for calibrating a model of dynamics of an industrial system” and Viguerie [Abstract] discloses “an early version of a Susceptible–Exposed–Infected–Recovered– Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatiotemporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features.”
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant’s effective filling date of the claimed invention to have modified the model calibration method, as taught by Chakrabarty, to incorporate a SEIRD model, as Viguerie discloses, for the benefit of informing “health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources” Viguerie [Abstract].
Like Chakrabarty, Viguerie also fails to specifically disclose searching a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem.
However, the analogous art of Bhaskaran discloses search a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, (“The hazard simulator 102 is configured to access data (e.g. calibrated models) from a plurality of different sources (i.e. different domains) and to use those data to generate information representing the hazard situation. The generated information may be a model (e.g. calibrated model) of how the hazard situation is predicted to develop in the physical domain over a period of time [ ] The hazard simulator 102 analyses the data from the different sources in order to generate the information, this process may also be referred to as exploration (i.e. searching). The hazard simulator 102 analyses (i.e. identifies) the data from the plurality of data sources using data analytics tools. For example, in a flood emergency situation, the hazard simulator 102 may employ a geographical information system (e.g. domain) (GIS) to map potential inundation areas using a source of flood forecasting information (e.g. different domain) combined with a digital elevation model (e.g. different domain). Combining this inundation information with the demographic data and local infrastructure information using the GIS tool, information is generated which provides insight into agent behavior, and thus provides a basis for rule generation” Bhaskaran [Col.7 Ln.55 – Col.8 Ln.13]. The hazard simulator 102 generated information is is analogous to calibrated models given Applicant’s disclosure “the calibrated problem 110 can be a disease modeling problem which may predict a disease transmission or progression rate or the like.” Spec. [P.0024]. The hazard simulator 102 is also interpreted to comprise a database because “the hazard simulator comprises a federated database server configured to retrieve data from the plurality of data sources, and a data exploration engine configured to analyze the retrieved data in order to generate the information representing the hazard situation.” Bhaskaran [Col.3 Ln.7-11]),
and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem (Bhaskaran [Col.7 Ln.55 – Col.8 Ln.13] discloses identifying a modeling problem applicable to unrelated domains (e.g. geographical information, flood forecasting information, etc.).)
Bhaskaran is analogous art as it relates to disaster risk reduction and response, specifically to a decision support system that assimilates data from multiple sources including real-time data, models agent behavior in hazard situations using agent-based simulations, and determines optimal intervention actions to reduce disaster risks and manage hazard situations effectively. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie combination to incorporate data from multiple different sources/domains, as Bhaskaran discloses, in order to effectively “identify and evaluate risk” Bhaskaran [Col.1 Ln.35] associated with hazard scenarios, such as the spread of infectious disease.
Regarding independent (amended) claim 15, Chakrabarty teaches, A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to: (“the program code or code segments to perform the necessary tasks may be stored in a machine readable medium. A processor(s) may perform the necessary tasks.” Chakrabarty [P.0167]),
receive a modeling problem having undefined parameter range to calibrate, (“a general model of a dynamical system (also referred to as “the industrial system 103”) is denoted by equation (1):
y
0
:
T
=
M
T
(
θ
) where
y
0
:
T
represents output of a model
M
T
(
θ
) of the dynamical system, the constant parameters of the model are described by
θ
∈
Θ
⊂
R
n
θ
. Further, assume that the admissible set of parameters Θ is known. For instance, Θ could denote a set of upper and lower bounds on parameters obtained from archived data or domain knowledge. The model
M
T
(
θ
) is a like a black-box, where a user may not be able to tune parameters of the model
M
T
(
θ
). Therefore, range of Θ of the parameters may be purely a guess (i.e. undefined parameter range).“ Chakrabarty [P.0055])
the similar problem being identified based on creating a mapping between parameter sets of at least some of the initial conditions of the modeling problem and associated error values (“trained for mapping between various combinations of different values of the parameters of the model of the industrial system and their corresponding calibration errors.” Chakrabarty [P.0013]),
and searching the database for mappings associated with the prior calibrated models similar to the created mapping (Referring to Fig. 1B (see below), “The simulation model obtains values θj of different combination of parameters for each simulation cycle from a database 113, where the database 113 is configured to store operational data Dj comprising different values of each parameter in their admissible range. Each simulation cycle includes execution of the simulation model 115 with different combinations of values θj of the parameters selected within the admissible range of values of the parameters.” Chakrabarty [P.0068]. The parameters stored in the database (113) are interpreted to have been searched prior to simulation (115) because “parameters θ are selected from the admissible parameter search domain Θ (i.e., θ∈Θ)” Chakrabarty [P.0082])
PNG
media_image1.png
599
782
media_image1.png
Greyscale
the associated error values representing sensitivities of parameter values against an output of the modeling problem; (“for a given a combination of different values of the different parameters of the model of thermal dynamics, the probabilistic parameter-to-cost regressor provides not only a calibration error but also a confidence range (i.e. sensitivities) around the calibration error.” Chakrabarty [P.0068]. The confidence range is interpreted as representing sensitivities due to Applicant’s disclosure “sensitivity indices quantify how much of the total variance of the model output each uncertain parameter (or input) is responsible for.” Spec. [P.0028].)
calibrate the modeling problem using information of the identified similar problem, (“The calibration system 101 further comprises an acquisition function module 107d that uses an acquisition function to choose parameters such that the failure region ΘF is avoided [ ] The acquisition function is executed to identify a combination of parameters (i.e. similar problem information) having the maximum likelihood of minimizing a calibration error according to the probabilistic parameter-to-cost mapping.” Chakrabarty [P.0076].)
and wherein the calibrating changes parameters for the modeling problem according to the identified similar problem (“the calibration system 101 comprises a simulation model (MT(θ)) 115 which is used to simulate an operation of the industrial system 103 multiple times [ ] the simulation model 115 is simulated multiple times for different combinations of parameters, where values of parameters may be different during each simulation cycle.” Chakrabarty [P.0068]);
monitor the accuracy of calibrated modeling problem; (“for a given a combination of different values of the different parameters of the model of thermal dynamics, the probabilistic parameter-to-cost regressor provides not only a calibration error but also a confidence range around the calibration error. In response, the convergence rate of Bayesian optimization increases and the computational burden of the calibration system reduces.” Chakrabarty [P.0068]. The convergence rate disclosed by Chakrabarty is interpreted to mean “monitoring the accuracy” due to Applicant’s disclosure “The processor 202 may monitor the calibration process for convergence” Spec. [P.0024] )
recalibrate the modeling problem until a performance criterion is met; (“The calibration system further comprises a probabilistic parameter-to-cost regressor [ ] the parameter-to-cost regressor is trained iteratively until a termination condition (i.e. performance criterion) is met” Chakrabarty [P.0013-14])
store the recalibrated modeling problem in the database (“the database 113 is configured to store operational data Dj comprising different values of each parameter in their admissible range. Each simulation cycle includes execution of the simulation model 115 with different combinations of values θj of the parameters selected within the admissible range of values of the parameters” Chakrabarty [P.0068]. Reference Fig. 1B above.);
and based on an output of the recalibrated modeling problem, automatically triggering an air filtration system to circulate air in a space. (“the industrial system 103 may include a controller that determines control inputs for actuators of the industrial system 103, based on the optimal combination of the different parameters received from the calibration system 101” Chakrabarty [P.0053]. Actuators of the industrial system are interpreted to control an air filtration system because “the HVAC system includes actuators including an indoor fan, an outdoor fan, an expansion valve actuator, and the like. The actuators may be controlled according to corresponding control inputs, e.g., a speed of the indoor fan, a speed of the outdoor fan, a position of the expansion valve, a speed of a compressor, and the like.” Chakrabarty [P.0049]
Chakrabarty fails to specifically disclose the modeling problem including a disease transmission modeling with initial conditions that include at least numbers of susceptible, exposed, infectious, recovered and not-survived entities in a dynamic physical environment; search a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem.
However, the analogous art of Viguerie discloses, the modeling problem including a disease transmission modeling with initial conditions that include at least numbers of susceptible, exposed, infectious, recovered and not-survived entities in a dynamic physical environment (“Initial conditions for the subpopulations s, e, i, r, and d in the model are defined by means of Gaussian circular functions” Viguerie [Pg.3 Sec.3 P.2]. Note, “s, e, i, r, and d” represent susceptible (s), exposed (e), infected (i), recovered (r), and deceased (d).)
Chakrabarty and Viguerie are analogous art as they both relate to physical system modelling. Chakrabarty [Abstract] discloses “A calibration system and method for calibrating a model of dynamics of an industrial system” and Viguerie [Abstract] discloses “an early version of a Susceptible–Exposed–Infected–Recovered– Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatiotemporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features.”
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant’s effective filling date of the claimed invention to have modified the model calibration method, as taught by Chakrabarty, to incorporate a SEIRD model, as Viguerie discloses, for the benefit of informing “health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources” Viguerie [Abstract].
Like Chakrabarty, Viguerie also fails to specifically disclose searching a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem.
However, the analogous art of Bhaskaran discloses search a database of prior calibrated models of different domains to identify a similar problem having features similar to the received modeling problem, (“The hazard simulator 102 is configured to access data (e.g. calibrated models) from a plurality of different sources (i.e. different domains) and to use those data to generate information representing the hazard situation. The generated information may be a model (e.g. calibrated model) of how the hazard situation is predicted to develop in the physical domain over a period of time [ ] The hazard simulator 102 analyses the data from the different sources in order to generate the information, this process may also be referred to as exploration (i.e. searching). The hazard simulator 102 analyses (i.e. identifies) the data from the plurality of data sources using data analytics tools. For example, in a flood emergency situation, the hazard simulator 102 may employ a geographical information system (e.g. domain) (GIS) to map potential inundation areas using a source of flood forecasting information (e.g. different domain) combined with a digital elevation model (e.g. different domain). Combining this inundation information with the demographic data and local infrastructure information using the GIS tool, information is generated which provides insight into agent behavior, and thus provides a basis for rule generation” Bhaskaran [Col.7 Ln.55 – Col.8 Ln.13]. The hazard simulator 102 generated information is is analogous to calibrated models given Applicant’s disclosure “the calibrated problem 110 can be a disease modeling problem which may predict a disease transmission or progression rate or the like.” Spec. [P.0024]. The hazard simulator 102 is also interpreted to comprise a database because “the hazard simulator comprises a federated database server configured to retrieve data from the plurality of data sources, and a data exploration engine configured to analyze the retrieved data in order to generate the information representing the hazard situation.” Bhaskaran [Col.3 Ln.7-11]),
and wherein the identified similar problem is applicable to a domain unrelated to the modeling problem (Bhaskaran [Col.7 Ln.55 – Col.8 Ln.13] discloses identifying a modeling problem applicable to unrelated domains (e.g. geographical information, flood forecasting information, etc.).)
Bhaskaran is analogous art as it relates to disaster risk reduction and response, specifically to a decision support system that assimilates data from multiple sources including real-time data, models agent behavior in hazard situations using agent-based simulations, and determines optimal intervention actions to reduce disaster risks and manage hazard situations effectively. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie combination to incorporate data from multiple different sources/domains, as Bhaskaran discloses, in order to effectively “identify and evaluate risk” Bhaskaran [Col.1 Ln.35] associated with hazard scenarios, such as the spread of infectious disease.
Claims 2, 4-6, 9, 11-14, 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chakrabarty et al. US Pub. No. 2023/0106530 A1 (hereinafter referred to as “Chakrabarty”), in view of Viguerie, Alex, et al. "Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion." Applied mathematics letters 111 (2021): 106617 (hereinafter referred to as “Viguerie”), in further view of Bhaskaran US Patent No. 10268963 B2 (hereinafter referred to as “Bhaskaran”), and in further view of White et al. U.S. Pat No. 11966670 B2 (hereinafter referred to as “White”).
Regarding claim 2, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 1 but fail to specifically disclose wherein the information includes statistical description of initial conditions and calibrated model results with parameter distributions.
However, White discloses, wherein the information includes statistical description of initial conditions and calibrated model results with parameter distributions. (“A first step 210 includes obtaining observational data, wherein the observational data includes at least one source of physical data sensed, including one or more sensors sensing the physical data.... The observational data includes measurement data... A second step 220 includes obtaining numeric simulation data... the simulation data includes data generated by running numerical simulation models, whereby certain a-priori specified processes are explicitly modeled (for example, via dynamic or statistical equations), computations are performed that calculate the evolution of these previously-specified processes over space and time” White [Col.6 Ln.8-30], “the numeric simulation data additionally includes reanalysis data obtained from previous data assimilation work” White [Col.7 Ln.40-42]. Examiner interprets “simulation data” to include “statistical description of initial conditions”, “reanalysis data obtained...” to include “calibrated model results”, “physical data” to mean “parameter”, and “one or more sensors sensing the physical data” to mean “parameter distributions”.)
White is analogous art as it relates to physical system modeling. Specifically, White teaches “systems, methods and apparatuses for predicting wildfire hazard and spread at multiple time scales” White [Col.1 Ln.24]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran combination to include information related to statistical description of initial conditions and model results with parameter distribution, as White discloses, in order “for predicting wildfire spread (i.e. analogous to claimed invention) and spread at multiple time scales” White [Col.1 Ln.45].
Regarding claim 4, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 1 but fail to specifically disclose wherein said calibrating includes performing parameter reduction technique.
However, White discloses, wherein said calibrating includes performing parameter reduction technique. (“For at least in some embodiments, this is achieved through eliminating model parameters or aspects of model architecture that have minimal impact of model performance.” White [Col.9 Ln.29- 32])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran combination to include parameter reduction, as White discloses, in order to generate “emulator models that are compressed versions of initial emulator models, yet retaining the performance of the initial models” White [Col.9 Ln.27].
Regarding claim 5, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 1 but fail to specifically disclose wherein said monitoring includes monitoring for convergence to an error threshold.
However, White discloses, wherein said monitoring includes monitoring for convergence to an error threshold. (“training the emulator model includes 1) retrieving an untrained model, 2) predicting physical model output comprising applying a current model to the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data, 3) testing and adapting the current model by updating model parameters, and 4) iterating on this process until a measure of convergence is achieved.” White [Col.8 Ln.24-31]. Examiner interprets “testing and adapting” to include “an error threshold” because “testing the current model comprises 1) measuring a fit of the predicted physical model output and 2) computing an error function by comparing the measured fit with physical knowledge.” White [Col.8 Ln.32-35].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran combination to include monitoring for convergence to an error threshold, as White discloses, in order to eliminate “ data that is suspected of being detrimental to the end goal of the system” White [Col.4 Ln.56].
Regarding claim 6, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 1 but fail to specifically disclose further including testing with historical results and tracking loss functions to determine performance of the calibrated modeling problem.
However, White discloses, further including testing with historical results and tracking loss functions to determine performance of the calibrated modeling problem. (“incorporating prior knowledge of the physical system into the emulator model includes utilizing statistical or domain inspired constraints in the training process of the emulator model (“soft” constraints). For an embodiment, this is implemented by incorporating terms into the model optimization loss (fitness) function penalizing the departure of model output from physical properties expected for the system studied.” White [Col.8 Ln.1-8]. Examiner interprets “prior knowledge” to mean “historical results”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran combination to include incorporation of historical results and tracking loss functions, as White discloses, in order “to enhance training stability” White [Col.13 Ln.28].
Regarding claim 9, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 8 but fail to specifically disclose wherein the information includes statistical description of initial conditions and calibrated model results with parameter distributions.
However, White discloses, wherein the information includes statistical description of initial conditions and calibrated model results with parameter distributions. (“A first step 210 includes obtaining observational data, wherein the observational data includes at least one source of physical data sensed, including one or more sensors sensing the physical data.... The observational data includes measurement data... A second step 220 includes obtaining numeric simulation data... the simulation data includes data generated by running numerical simulation models, whereby certain a-priori specified processes are explicitly modeled (for example, via dynamic or statistical equations), computations are performed that calculate the evolution of these previously-specified processes over space and time” White [Col.6 Ln.8-30], “the numeric simulation data additionally includes reanalysis data obtained from previous data assimilation work” White [Col.7 Ln.40-42]. Examiner interprets “simulation data” to include “statistical description of initial conditions”, “reanalysis data obtained...” to include “calibrated model results”, “physical data” to mean “parameter”, and “one or more sensors sensing the physical data” to mean “parameter distributions”.)
White is analogous art as it relates to physical system modeling. Specifically, White teaches “systems, methods and apparatuses for predicting wildfire hazard and spread at multiple time scales” White [Col.1 Ln.24]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran combination to include information related to statistical description of initial conditions and model results with parameter distribution, as White discloses, in order “for predicting wildfire spread (i.e. analogous to claimed invention) and spread at multiple time scales” White [Col.1 Ln.45].
Regarding claim 11, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 8 but fail to specifically disclose wherein said at least one hardware processor is configured to perform parameter reduction technique to calibrate the modeling problem.
However, White discloses, wherein said at least one hardware processor is configured to perform parameter reduction technique to calibrate the modeling problem. (“The system includes one or more sensors and one or more computing devices... Memory includes instructions that, when executed by the one or more computing devices, enables the system to...” White [Col.2 Ln.18- 23], “For at least in some embodiments, this is achieved through eliminating model parameters or aspects of model architecture that have minimal impact of model performance.” White [Col.9 Ln.29-32]. Examiner interprets “computing devices” to include “memory device coupled with said at least one hardware processor”.)
White teaches the additional limitations of Claim 11 and maintains the same rationale for combination with Chakrabarty-Viguerie-Bhaskaran as Claim 4.
Regarding claim 12, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 8 but fail to specifically disclose wherein said at least one hardware processor is configured to monitor calibrating of the modeling problem for convergence to an error threshold.
However, White discloses, wherein said at least one hardware processor is configured to monitor calibrating of the modeling problem for convergence to an error threshold. (“The system includes one or more sensors and one or more computing devices... Memory includes instructions that, when executed by the one or more computing devices, enables the system to...” White [Col.2 Ln.18-23], “training the emulator model includes 1) retrieving an untrained model, 2) predicting physical model output comprising applying a current model to the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data, 3) testing and adapting the current model by updating model parameters, and 4) iterating on this process until a measure of convergence is achieved.” White [Col.8 Ln.24-31]. Examiner interprets “testing and adapting” to include “an error threshold” because “testing the current model comprises 1) measuring a fit of the predicted physical model output and 2) computing an error function by comparing the measured fit with physical knowledge.” White [Col.8 Ln.32-35]. Examiner interprets “computing devices” to include “memory device coupled with said at least one hardware processor”.)
White teaches the additional limitations of Claim 12 and maintains the same rationale for combination with Chakrabarty-Viguerie-Bhaskaran as Claim 5.
Regarding claim 13, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 8 but fail to specifically disclose wherein said at least one hardware processor is configured to test the calibrated modeling problem with historical results and track loss functions to determine performance of the calibrated modeling problem.
However, White discloses, wherein said at least one hardware processor is configured to test the calibrated modeling problem with historical results and track loss functions to determine performance of the calibrated modeling problem. (“The system includes one or more sensors and one or more computing devices... Memory includes instructions that, when executed by the one or more computing devices, enables the system to...” White [Col.2 Ln.18-23], “incorporating prior knowledge of the physical system into the emulator model includes utilizing statistical or domain-inspired constraints in the training process of the emulator model (“soft” constraints). For an embodiment, this is implemented by incorporating terms into the model optimization loss (fitness) function penalizing the departure of model output from physical properties expected for the system studied.” White [Col.8 Ln.1-8]. Examiner interprets “prior knowledge” to mean “historical results” and “computing devices” to include “memory device coupled with said at least one hardware processor”.)
White teaches the additional limitations of Claim 13 and maintains the same rationale for combination with Chakrabarty-Viguerie-Bhaskaran as Claim 6.
Regarding claim 14, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 8 but fail to specifically disclose wherein an output of the calibrated modeling problem automatically triggers a physical barrier to open or close.
However, White discloses, wherein an output of the calibrated modeling problem automatically triggers a physical barrier to open or close. (“For an embodiment, physical structure adjustments 1343 include switching or adjusting a physical structure. For an embodiment, this includes automatically controlling a setting of a switch based on the generating gridded wildfire prediction data... this includes adjusting an outflow of a dam based on the generated gridded wildfire prediction data. Any of these actions can be automatically controlled through a switch or adjustable switch.” White [Col.24 Ln.39-50])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran combination to include automatically triggering a switch, as White discloses, in order to mitigate the spread of a physical system, such as wildfire (i.e. automatically triggering a dam) or disease (i.e. automatically triggering an air filtration system).
Regarding claim 16, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 15 but fail to specifically disclose wherein the information includes statistical description of initial conditions and calibrated model results with parameter distributions.
However, White discloses, wherein the information includes statistical description of initial conditions and calibrated model results with parameter distributions. (“A first step 210 includes obtaining observational data, wherein the observational data includes at least one source of physical data sensed, including one or more sensors sensing the physical data.... The observational data includes measurement data... A second step 220 includes obtaining numeric simulation data... the simulation data includes data generated by running numerical simulation models, whereby certain a-priori specified processes are explicitly modeled (for example, via dynamic or statistical equations), computations are performed that calculate the evolution of these previously-specified processes over space and time” White [Col.6 Ln.8-30], “the numeric simulation data additionally includes reanalysis data obtained from previous data assimilation work” White [Col.7 Ln.40-42]. Examiner interprets “simulation data” to include “statistical description of initial conditions”, “reanalysis data obtained...” to include “calibrated model results”, “physical data” to mean “parameter”, and “one or more sensors sensing the physical data” to mean “parameter distributions”.)
White is analogous art as it relates to physical system modeling. Specifically, White teaches “systems, methods and apparatuses for predicting wildfire hazard and spread at multiple time scales” White [Col.1 Ln.24]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran combination to include information related to statistical description of initial conditions and model results with parameter distribution, as White discloses, in order “for predicting wildfire spread (i.e. analogous to claimed invention) and spread at multiple time scales” White [Col.1 Ln.45].
Regarding claim 18, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 15 but fail to specifically disclose wherein the device is further caused to perform parameter reduction technique to calibrate the modeling problem.
However, White discloses, wherein the device is further caused to perform parameter reduction technique to calibrate the modeling problem. (“For at least in some embodiments, this is achieved through eliminating model parameters or aspects of model architecture that have minimal impact of model performance.” [Col.9 Ln.29-32])
White teaches the additional limitations of Claim 18 and maintains the same rationale for combination with Chakrabarty-Viguerie-Bhaskaran as Claim 4.
Regarding claim 19, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 15 but fail to specifically disclose wherein the device is further caused to monitor calibrating of the modeling problem for convergence to an error threshold.
However, White discloses, wherein the device is further caused to monitor calibrating of the modeling problem for convergence to an error threshold. (“iterating until a measure of convergence is achieved, e.g., a validation-set performance as measured by the metrics implemented in Metric Computation and Validation Module 526” [Col.17 Ln.56-59]. Examiner interprets “metrics” to include “an error threshold” because “A validation module 526 implements computation of spatial and temporal performance metrics as appropriate for the application at hand.” [Col.16 Ln.8-10].)
White teaches the additional limitations of Claim 19 and maintains the same rationale for combination with Chakrabarty-Viguerie-Bhaskaran as Claim 5.
Regarding claim 20, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 15 but fail to specifically disclose wherein the device is further caused to test the calibrated modeling problem with historical results and track loss functions to determine performance of the calibrated modeling problem.
However, White discloses, wherein the device is further caused to test the calibrated modeling problem with historical results and track loss functions to determine performance of the calibrated modeling problem. (“incorporating prior knowledge of the physical system into the emulator model includes utilizing statistical or domain-inspired constraints in the training process of the emulator model (“soft” constraints). For an embodiment, this is implemented by incorporating terms into the model optimization loss (fitness) function penalizing the departure of model output from physical properties expected for the system studied.” [Col.8 Ln.1-8])
White teaches the additional limitations of Claim 20 and maintains the same rationale for combination with Chakrabarty-Viguerie-Bhaskaran as Claim 6.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chakrabarty et al. US Pub. No. 2023/0106530 A1 (hereinafter referred to as “Chakrabarty”), in view of Viguerie, Alex, et al. "Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion." Applied mathematics letters 111 (2021): 106617 (hereinafter referred to as “Viguerie”), in further view of Bhaskaran US Patent No. 10268963 B2 (hereinafter referred to as “Bhaskaran”), and in further view of White et al. U.S. Pat No. 11966670 B2 (hereinafter referred to as “White”)., and in further view of Sadilek et al. US Pub No. 20190148023 A1 (hereinafter referred to as “Sadilek”).
Regarding claim 3, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 1 but fail to specifically disclose further including generating the database of the prior calibrated models, the database storing statistical description of initial conditions and calibrated model results with parameter distributions associated with each of the prior calibrated models.
However, White discloses,, the database storing statistical description of initial conditions and calibrated model results with parameter distributions associated with each of the prior calibrated models. (“A first step 210 includes obtaining observational data, wherein the observational data includes at least one source of physical data sensed, including one or more sensors sensing the physical data....The observational data includes measurement data... A second step 220 includes obtaining numeric simulation data... the simulation data includes data generated by running numerical simulation models, whereby certain a-priori specified processes are explicitly modeled (for example, via dynamic or statistical equations), computations are performed that calculate the evolution of these previously-specified processes over space and time” White [Col.6 Ln.8-30], “the numeric simulation data additionally includes reanalysis data obtained from previous data assimilation work” White [Col.7 Ln.40-42]. Examiner interprets “simulation data” to include “statistical description of initial conditions”, “reanalysis data obtained...” to include “calibrated model results”, “physical data” to mean “parameter”, and “one or more sensors sensing the physical data” to mean “parameter distributions”.)
However, the Chakrabarty-Viguerie-Bhaskaran-White combination fails to specifically disclose further including generating the database of the prior calibrated models, the database storing.
On the other hand, the analogous art of Sadilek discloses, further including generating the database of the prior calibrated models, the database storing (“In some implementations, the user computing device 102 can store or include one or more machine-learned models 120. For example, the machine-learned models 120 can be or can otherwise include various machine-learned models such as support vector machines, log linear models, neural networks (e.g., deep neural networks), and/or other types of machine-learned models, including non-linear models and/or linear models.” Sadilek [P.0066]. Examiner interprets “computing device 102 can store” to mean “database”.)
Sadilek is analogous art as it relates to machine learned prediction modeling systems and methods, in particular, models based on environmental factors/conditions. Sadilek teaches “as an output of the machine-learned disease detection model, identification of a second plurality of users predicted to have the disease.” Sadilek [P.0008].
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant’s effective filling date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran-White combination to incorporate the computing device, as disclosed in Sadilek, in order to store one or more machine-learned models which “can be received from the server computing system 130 over network 180, stored in the user computing device memory 114, and then used or otherwise implemented by the one or more processors 112.” Sadilek [P.0067].
Regarding claim 10, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 8 but fail to specifically disclose wherein said at least one hardware processor is further configured to generate the database of the prior calibrated models, the database storing statistical description of initial conditions and calibrated model results with parameter distributions associated with each of the prior calibrated models.
However, White discloses, wherein said at least one hardware processor is further configured to , the database storing statistical description of initial conditions and calibrated model results with parameter distributions associated with each of the prior calibrated models. (“The system includes one or more sensors and one or more computing devices... Memory includes instructions that, when executed by the one or more computing devices, enables the system to...” White [Col.2 Ln.18- 23]. Examiner interprets “computing devices” to include “at least one hardware processor is further configured...”. “A first step 210 includes obtaining observational data, wherein the observational data includes at least one source of physical data sensed, including one or more sensors sensing the physical data....The observational data includes measurement data... A second step 220 includes obtaining numeric simulation data... the simulation data includes data generated by running numerical simulation models, whereby certain a-priori specified processes are explicitly modeled (for example, via dynamic or statistical equations), computations are performed that calculate the evolution of these previously-specified processes over space and time” White [Col.6 Ln.8-30], “the numeric simulation data additionally includes reanalysis data obtained from previous data assimilation work” White [Col.7 Ln.40-42]. Examiner interprets “simulation data” to include “statistical description of initial conditions”, “reanalysis data obtained...” to include “calibrated model results”, “physical data” to mean “parameter”, and “one or more sensors sensing the physical data” to mean “parameter distributions”.)
However, the Chakrabarty-Viguerie-Bhaskaran-White combination fails to specifically disclose generate the database of the prior calibrated models.
On the other hand, Sadilek does disclose generate the database of the prior calibrated models (“the user computing device 102 can store or include one or more machine-learned models 120. For example, the machine-learned models 120 can be or can otherwise include various machine-learned models such as support vector machines, log linear models, neural networks (e.g., deep neural networks), and/or other types of machine-learned models, including non-linear models and/or linear models.” Sadilek [P.0066]. Examiner interprets “computing device 102 can store” to mean “database”.)
Sadilek is analogous art as it relates to machine-learned prediction modeling systems and methods, in particular, models based on environmental factors/conditions. Sadilek teaches “as an output of the machine-learned disease detection model, identification of a second plurality of users predicted to have the disease.” Sadilek [P.0008].
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant’s effective filling date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran-White combination to incorporate the computing device, as disclosed in Sadilek, in order to store one or more machine-learned models which “can be received from the server computing system 130 over network 180, stored in the user computing device memory 114, and then used or otherwise implemented by the one or more processors 112.” Sadilek [P.0067].
Regarding claim 17, Chakrabarty-Viguerie-Bhaskaran disclose the limitations of claim 15 but fail to specifically disclose wherein the device is further caused to generate the database of the prior calibrated models, the database storing statistical description of initial conditions and calibrated model results with parameter distributions associated with each of the prior calibrated models.
However, White discloses, the database storing statistical description of initial conditions and calibrated model results with parameter distributions associated with each of the prior calibrated models. (“A first step 210 includes obtaining observational data, wherein the observational data includes at least one source of physical data sensed, including one or more sensors sensing the physical data....The observational data includes measurement data... A second step 220 includes obtaining numeric simulation data... the simulation data includes data generated by running numerical simulation models, whereby certain a-priori specified processes are explicitly modeled (for example, via dynamic or statistical equations), computations are performed that calculate the evolution of these previously-specified processes over space and time” White [Col.6 Ln.8-30], “the numeric simulation data additionally includes reanalysis data obtained from previous data assimilation work” White [Col.7 Ln.40-42]. Examiner interprets “simulation data” to include “statistical description of initial conditions”, “reanalysis data obtained...” to include “calibrated model results”, “physical data” to mean “parameter”, and “one or more sensors sensing the physical data” to mean “parameter distributions”.)
However, the Chakrabarty-Viguerie-Bhaskaran-White combination fails to specifically disclose wherein the device is further caused to generate the database of the prior calibrated models.
However, the analogous art of Sadilek discloses wherein the device is further caused to generate the database of the prior calibrated models (“In some implementations, the user computing device 102 can store or include one or more machine-learned models 120. For example, the machine-learned models 120 can be or can otherwise include various machine-learned models such as support vector machines, log linear models, neural networks (e.g., deep neural networks), and/or other types of machine-learned models, including non-linear models and/or linear models.” Sadilek [P.0066]. Examiner interprets “computing device 102 can store” to mean “database”.)
Sadilek is analogous art as it relates to machine-learned prediction modeling systems and methods, in particular, models based on environmental factors/conditions. Sadilek teaches “as an output of the machine-learned disease detection model, identification of a second plurality of users predicted to have the disease.” Sadilek [P.0008].
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant’s effective filling date of the claimed invention to have modified the Chakrabarty-Viguerie-Bhaskaran-White combination to incorporate model storage means, as disclosed in Sadilek, in order to store one or more machine-learned models which “can be received from the server computing system 130 over network 180, stored in the user computing device memory 114, and then used or otherwise implemented by the one or more processors 112.” Sadilek [P.0067].
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is
considered pertinent to applicant's disclosure:
Parolini, Nicola, et al. "SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy." Proceedings of the Royal Society A 477.2253 (2021): 20210027. “a novel mathematical epidemiological model [ ] a one-to-one calibration strategy between the model compartments and the data” [Abstract]
Hoffman et al. (Use Of Data From Field Trials In Crop Protection For Calibrating And Optimising Prediction Models – US Pub. No 2020/0250360 A1). “a method, a computer system and a computer program product that make data obtained in field trials amenable to the calibration and optimization of forecasting models” [Abstract]
Laxmanan et al. (AUTOMATICALLY RECALIBRATING RISK MODELS – US Pub. No 2011/0246385 A1). “Methods, computer readable media, and apparatuses for automatically recalibrating risk models” [Abstract]
Williams (Predicting Quantitative Structure-activity Relationships – US Patent No. 11210438 B2). “accurately capture and model inherent nonlinearities in cases where sufficient knowledge does not exist to a priori build a model and its parameters; and, (iv) provide one-to-one relationships between model parameters and model outputs” [Abstract]
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/ANTHONY CHAVEZ/ Examiner, Art Unit 2187
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186