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 .
Claim Objections
Claims 3, 8 and 13 are objected to because of the following informalities: claims 3, 8 and 13 each describe a list of classifier types, but they do not state whether the classifier types are alternatives separated by “or,” or whether they are inclusive and separated by “and.” Examiner has interpreted these claims as teaching either a mathematical time-series classifier, a frequency-based classifier, or a machine learned classifier. Appropriate correction to claims 3, 8 and 13 is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method of using a processor (i.e., a process). Claim 6 is directed to an input/output interface (i.e., a machine). Claim 11 is directed to a non-transitory machine-readable information storage medium (i.e., a machine). Therefore, claims 1-15 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A processor-implemented method comprising:
receiving, via an input/output interface, a continuous past residue of an iterative numerical simulation, wherein a fixed size window is chosen to select the continuous past residue;
determining, via one or more hardware processors, a status of the received continuous past residue of the iterative numerical simulation using a classifier, wherein the status includes one of a stable or an unstable simulation [mental process/step];
predicting, via the one or more hardware processors, an output for a control parameters using a control logic [mental process/step]; and
integrating, via the one or more hardware processors, the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining…” in the context of this claim encompasses a person (driver) looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A processor-implemented method comprising:
receiving, via an input/output interface, a continuous past residue of an iterative numerical simulation, wherein a fixed size window is chosen to select the continuous past residue [pre-solution activity (data gathering) using generic sensors];
determining, via one or more hardware processors, a status of the received continuous past residue of the iterative numerical simulation using a classifier, wherein the status includes one of a stable or an unstable simulation [mental process/step];
predicting, via the one or more hardware processors, an output for a control parameters using a control logic [mental process/step]; and
integrating, via the one or more hardware processors, the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation [post-solution activity (calculation steps)].
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “receiving, via an input/output interface, a continuous past residue…,” and “integrating, via the one or more hardware processors, the predicted output with the iterative numerical simulation…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer and a set of sensors to perform the process. In particular, the receiving step from the input/output interface is recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The integrating step is also recited at a high level of generality (i.e. as a general means of combining data sets).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a vehicle controller to perform the evaluating… amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “receiving, via an input/output interface, a continuous past residue…,” and “integrating, via the one or more hardware processors, the predicted output with the iterative numerical simulation…,” the examiner submits that these limitations are insignificant extra-solution activities. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claims 2-5, 7-10 and 12-15 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application [provide concise explanation]. Therefore, dependent claims 2-5, 7-10 and 12-15 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-15 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hoyer, et al., WO 2022/159074 A1, in view of Dalli.
As per Claim 1, Hoyer, et al., WO 2022/159074 A1 teaches a processor-implemented method (¶¶ 27-28) comprising:
receiving, via an input/output interface, a continuous past residue of an iterative numerical simulation, wherein a fixed size window is chosen to select the continuous past residue (¶¶ 88-90; as a “residual stress term”); and
determining, via one or more hardware processors, a status of the received continuous past residue of the iterative numerical simulation using a classifier, wherein the status includes one of a stable or an unstable simulation (¶ 66; based on “error function” calculations).
Hoyer does not expressly teach: predicting, via the one or more hardware processors, an output for a control parameters using a control logic; and integrating, via the one or more hardware processors, the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation. Dalli, et al., US 2022/0114417 A1 teaches:
predicting, via the one or more hardware processors, an output for a control parameters using a control logic (¶¶ 44, 48; with “[P]redictive INNs” and “predictive XNNs”); and
integrating, via the one or more hardware processors, the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation (¶¶ 54, 90).
At the time of the invention, a person of skill in the art would have thought it obvious to combine the processing method of Hoyer with the prediction steps of Dalli, in order to reduce the likelihood of errors in future iterations of a simulation.
As per Claim 6, Hoyer teaches a system (¶¶ 36-38) comprising:
an input/output interface to receive a continuous past residue of an iterative numerical simulation, wherein a fixed size window is chosen to select the continuous past residue (¶¶ 88-90; as a “residual stress term”); and
a memory in communication with the one or more hardware processors (¶ 32), wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory to; determine a status of the received continuous past residue of the iterative numerical simulation using a classifier, wherein the status includes a stable and an unstable simulation (¶ 66; based on “error function” calculations).
Hoyer does not expressly teach: predicting an output for control parameters using a control logic; and integrate the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation. Dalli teaches:
predicting an output for control parameters using a control logic (¶¶ 44, 48; with “[P]redictive INNs” and “predictive XNNs”); and
integrating the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation (¶¶ 54, 90).
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 11, Hoyer teaches one or more non-transitory machine-readable information storage mediums (¶¶ 53-54; data storage 208 of Figure 2) comprising one or more instructions (¶ 55) which when executed by one or more hardware processors cause:
receiving, via an input/output interface, a continuous past residue of an iterative numerical simulation, wherein a fixed size window is chosen to select the continuous past residue (¶¶ 88-90; as a “residual stress term”); and
determining a status of the received continuous past residue of the iterative numerical simulation using a classifier, wherein the status includes a stable and an unstable simulation (¶ 66; based on “error function” calculations).
Hoyer does not expressly teach: predicting an output for an under-relaxation factor using a control logic; and integrating the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation. Dalli teaches:
predicting an output for an under-relaxation factor using a control logic (¶¶ 44, 48; with “[P]redictive INNs” and “predictive XNNs”); and
integrating the predicted output with the iterative numerical simulation to stabilize and accelerate iterative numerical simulation (¶¶ 54, 90).
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claims 2, 7 and 12, Hoyer teaches that a self-learning of the classifier comprises:
receiving, via the one or more hardware processors, continuous past residue, and outcome of the simulation (¶ 88; via a “residual stress term”); and
self-labelling, via the one or more hardware processors, the continuous past residue based on the outcome of the simulation (¶ 44); and updating, via the one or more hardware processors, the classifier based on the self-labelled past residue (¶ 8; “the solution to the differential equation(s) on the points of the mesh may be updated to more accurately model the physical system”; or ¶ 88, for “a revised Navier-Stokes equation”).
As per Claims 3, 8 and 13, Hoyer teaches that the classifier comprises at least one of a mathematical time-series classifier, frequency-based classifier (¶¶ 36-37), machine learned classifiers such as Reinforcement Learning (RL) (¶¶ 44-45), Long Short-term Memory (LSTM), Spiking Neural Network (SNN).
As per Claims 4, 9 and 14, Hoyer does not expressly teach that the control logic includes an if-else, a fuzzy logic, and a mathematical logic. Dalli teaches that the control logic includes an if-else, a fuzzy logic, and a mathematical logic (¶¶ 164-165). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claims 5, 10 and 15, Hoyer teaches that the SNN based classifier and the control logic work together without interfering with the iterative numerical simulation until the simulation ends (¶¶ 46-47; until “Training phase 102 has been completed” as shown in Figure 1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATUL TRIVEDI whose telephone number is (313)446-4908. The examiner can normally be reached Mon-Fri; 9:00 AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ATUL TRIVEDI
Primary Examiner
Art Unit 3661
/ATUL TRIVEDI/Primary Examiner, Art Unit 3661
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