DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Amendments
This Office Action is in response to the amendment filed on April 13, 2026.
Claims 1, 9, and 15 have been amended.
No claims have been cancelled.
No new claim has been added.
The objections and rejections from the prior correspondence that are not restated herein are withdrawn.
Response to Arguments
Applicant's arguments filed on April 13, 2026 have been fully considered.
Applicant’s arguments regarding the 35 U.S.C. § 101 rejections have been fully considered but are not persuasive. Applicant argues:
“the latest rejection does not accurately evaluate the claims as a whole. The Examiner characterizes the independent claims as reciting only mathematical concepts and mental processes, pointing to operations such as generating a frequency spectrum, normalizing that spectrum, classifying a corruption utilizing a Fourier transform, updating classifier weights, and updating batch-normalization statistics. But that analysis improperly dissects the claims into isolated computational substeps and ignores the technological arrangement that those steps collectively define. The pending claims are not directed to mathematics in the abstract. Rather, they recite a specific machine-implemented corruption-robustness pipeline that receives sensor-derived input data, transforms that data into a normalized frequency-domain representation, uses a hyper model to identify a current corruption condition, and then dynamically adapts the operative classifier parameters so that the classifier produces improved classification results under the detected corruption.”
Examiner respectfully disagrees. Applicant’s argument confirms that the objective of the recited pipeline is to dynamically adapt a classifier so that the classifier produces improved classification results. However, that objective is fulfilled by the recited mathematical processing itself, including generating and normalizing a frequency spectrum, classifying a corruption using a Fourier transform, updating classifier weights, and updating batch normalization (BN) statistics. An improvement in the result of that processing is not, by itself, an improvement to technology. As stated in the previous Office Action, the improvement must come from additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The additional elements recited in claim 1 receive sensor data, send the normalized frequency spectrum to the hyper model, use the normalized frequency spectrum as input to the model, and output a classification. These limitations do not apply the classification in a concrete technological action such as to control a vehicle, control an actuator, control a robot, avoid collision, or trigger an automatic action. Therefore, even when the claims are considered as an ordered sequence of operations, the alleged improvement is an improved classification result produced by the judicial exception. At Step 2B, the claims also do not amount to significantly more than the judicial exception because the claims recite generic computer components, receiving data, and outputting data at a high level of generality. Moreover, it should be noted that the independent claims do not recite mental process and are only rejected for reciting mathematical concepts, as indicated in the 101 rejections below for claims 1, 9, and 15.
Applicant further argues:
“The latest rejection also asserts that the Specification and claims do not provide a clear description of how classifying corruptions and updating BN statistics or weights amount to a technological improvement. Respectfully, the Specification says exactly that. Paragraph 22 explains that the disclosed unified framework combines corruption detection with BN-statistics updating and "may allow for an accuracy improvement of about 8% and 4% on CIF ARI 0-C and ImageNet-C, respectively," and further states that the framework can improve the accuracy of already robust models. Paragraph 24 explains the mechanism of that improvement: corruption type is detected in the Fourier domain, the corresponding BN statistics are selected from a lookup table, and the pre-trained network is updated accordingly before classification. Paragraph 25 further explains that the disclosed framework improves robustness for off-the-shelf pre-trained models and is "insensitive to the rate at which corruption changes," thereby addressing a concrete technical shortcoming of prior adaptation approaches. Paragraph 46 and FIG. 3 then describe the same architecture at the system level, including real-time selection or generation of corresponding BN statistics, parameters, or even architecture changes to make the main model insensitive to the detected corruption. Those disclosures are not aspirational; they describe a specific technical mechanism and its technical effect.
Accordingly, the claims integrate any alleged mathematical operation into a practical application. The claimed transforms and normalization are not ends in themselves. They are used in a particular processing chain to identify corruption from sensor-originated data and to dynamically adapt the classifier to that corruption so that the classifier performs more robustly in changing real-world conditions. The specification explains that the corruption-detection task is difficult in the image domain but easier in the Fourier domain, and therefore the claimed use of the normalized frequency spectrum is part of a concrete engineering solution rather than a generalized mathematical exercise. See Specification 11 22, 24-25, 46; FIG. 4. Likewise, the update of BN statistics or model weights is not a mere abstract calculation. In the claimed context, that update is the operative mechanism by which the system changes classifier behavior in response to a detected corruption so as to improve robustness and classification performance.
The Examiner also treats the sensor input and classification output as mere data gathering or extra-solution activity. That is inconsistent with the disclosure. The claimed pipeline begins with real sensor-derived input, including image, radar, sonar, or sound information. See Claim 1; Claim 9; Claim 15; Specification 1 51. The specification further ties the classifier output to concrete control applications. Paragraphs 63-64 and FIG. 6 explain that the control system may be used in an at least partially autonomous vehicle or robot, where the classifier output characterizes nearby objects and actuator control commands may be determined from that output to avoid collisions. Thus, the claims are not directed to analysis divorced from technology. They are directed to improving how machine-learning classification operates on corrupted sensor inputs in technological systems that rely on such classification. Nor is the Examiner correct to suggest that the claimed corruption classification can practically be performed mentally. The claims require tangible steps, such as receiving sensor data, performing a frequency-domain transformation, applying a specific normalization, inputting the normalized spectrum to a hyper model, classifying the corruption, dynamically updating classifier parameters or BN statistics based on that corruption, and then outputting the resulting classification. The Specification teaches that this framework uses a corruption-detection DNN, a trained main model, and a lookup structure containing corruption-specific BN statistics, and may further extend to generated parameters or architecture changes. See Specification 11 24-25, 46. That is a concrete computational architecture for adapting a machine-learning classifier in real time to changing corruption conditions. It is not something that can be performed in the human mind or with pencil and paper in any practical sense.
For the same reasons, the claims are not directed merely to "improving the abstract idea itself" The improvement identified in the present application is an improvement in the operation of a machine-learning classification system that processes corrupted sensor data. The claimed arrangement improves corruption robustness, improves classification accuracy under corrupted conditions, and does so without retraining the main model from scratch. See Specification 11 22, 25, 46. That is a technological improvement in the functioning of the claimed ML-based classification pipeline, not an attempt to monopolize mathematics as such.
The claims do not merely invoke a generic computer to execute a result. They recite a particular sequence of operations using a normalized frequency-domain representation, a hyper model configured to classify corruption, and corruption-specific adaptation of the classifier through (batch norm) BN-statistics or weight updates. See Claims 1, 8-10, 15-17, 20-21; Specification 1124-25, 46. That combination is what produces the disclosed technical benefit of improved robustness against multiple corruptions, including changing corruptions, in real-world sensor environments. This is a specific technological solution for a technical problem, and is exactly what the Federal Circuit cautioned against in cases like Enfish.”
Examiner respectfully disagrees. The previous Office Action does not reject any independent claim as being a mental process, but rather mathematical concepts. The claims, as currently drafted, still do not provide a clear improvement to technology. Even when considering paragraph [0046], which describes updating BN statistics at inference time and dynamically choosing or generating corresponding BN statistics in real time, the described result is the classification output after the classifier has been updated. The claims, as currently drafted, do not recite or reflect controlling an actuator, operating a vehicle, causing a vehicle to respond to the updated classification, controlling a robot, avoiding collision, braking or reducing vehicle speed, or triggering any automatic action in response to the classification. Additionally, applicant’s reliance on Enfish is not persuasive because Enfish involved claims directed to a specific improvement in computer functionality, specifically a self-referential table that improved how a computer stored and retrieved data. The present claims do not recite a comparable data structure or comparable improvement to computer memory, storage, retrieval, or processor operation. Instead, the alleged improvement comes from the recited mathematical concept, not from additional elements.
Paragraphs [0063] and [0064] describe example downstream control applications that could support integration into a practical application if those features were recited in the independent claims. However, the independent claims do not recite controlling an actuator, operating a vehicle, controlling a robot, avoiding collision, braking or reducing vehicle speed, or triggering an automatic action based on the output classification. Therefore, the unclaimed examples cannot integrated the recited mathematical concepts into a practical application as currently claimed.
Applicant’s arguments regarding the 35 U.S.C. § 103 rejections have been fully considered but are not persuasive. Applicant argues:
“the combination of SHEN, BUNAZAWA, and BENZ fails to establish a prima facie case of obviousness because it does not teach or suggest one or more claim limitations, including at least: (1) utilizing the normalized frequency spectrum as input to a hyper model in order to classify a corruption associated with the input data; and (2) updating the classifier based on that corruption, including, in claim 1, updating one or more weights associated with the classifier based on the corruption associated with the input data before outputting the claimed classification. At most, the cited references disclose frequency-domain processing, anomaly-related analysis, and model adaptation in isolation, but they do not teach the claimed interaction in which the normalized frequency spectrum is provided to the hyper model to classify a corruption associated with the input data, and that classified corruption in turn forms the basis for updating the classifier used to generate the output classification. Accordingly, the cited combination does not disclose or render obvious the claims as presently drafted.
However, BUNAZAWA at most discloses generating normalized frequency-domain features using a Fourier transform and providing such features to a model for determining a condition of a mechanical system, such as gear anomaly detection. That is not the same as the presently claimed arrangement, in which the normalized frequency spectrum is used as input to a hyper model to classify a corruption associated with the input data itself Thus, even if BUNAZAWA is viewed as disclosing a neural-network-based "map," it still does not teach or suggest the claimed corruption-classification role attributed to the hyper model in the present claims.
Examiner respectfully disagrees. The primary reference SHEN provides the input data from sensors and generates a Fourier transform for the input data. BUNAZAWA is introduced for teaching normalizing a frequency spectrum for subsequent processing by a fully-connected neural network (e.g., map data DM) that utilizes a support vector machine classifier to classify an anomaly associated with sensor input variables, such as sound captured by a sensor (BUNAZAWA [0040], [0130], [0138], [0145-0146]). Additionally, SHEN and BUNAZAWA both concern sensor data and determining corruption or anomaly conditions for that data. Therefore, one would be motivated to combine BUNAZAWA and implement the Fourier transform normalization and map data DM that includes a support vector machine classifier in order to improve the accuracy of determination as to whether there is an anomaly in the data (see BUNAZAWA [0010]), such as the data provided by SHEN. Furthermore, under BRI, BENZ [pg. 1, Figure 1] teaches updating the weights associated with the classifier, under BRI in view of specification paragraphs [0031] and [0046], to output a prediction using the updated BN statistics. Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, and BENZ before them, to include BENZ' estimating and adapting BN statistics in SHEN/BUNAZAWA's training method against image corruption. One would have been motivated to make such a combination in order to improve model robustness under corruptions (BENZ [pg. 4, section 3.3. Motivation for rectifying batch normalization]). For this reason, the combination of SHEN, BUNAZAWA, and BENZ teaches claim 1.
Applicant further argues:
“While BENZ may disclose adaptation of a model, BENZ does not disclose or suggest the claimed arrangement in which a corruption associated with the input data is first classified via the hyper model using the normalized frequency spectrum, and that classified corruption then forms the basis for the claimed classifier update. Instead, the Examiner's reliance on BENZ at most shows adaptation in a more general sense, not the particular corruption-driven update sequence required by the claims.”
Examiner respectfully disagrees. BENZ is not relied upon for teaching the hyper model corruption classification step because BUNAZAWA is used for teaching this limitation. BENZ teaches rectifying BN statistics for correcting the outputs of the classifier, which matches the scope of the instant disclosure. Specifically, paragraph [0031] states that model adaptation includes updating the model parameters and that the system updates BN statistics to adapt the model to update weights based on the corruption. Specification paragraph [0046] states that the system updates classifier 309 with corresponding BN statistics and then outputs classification 311. Therefore, under BRI, BENZ teaches the relied upon classifier adaptation step.
Applicant further argues:
“At most, the Examiner's proposed combination places alongside one another BUNAZAWA's frequency-domain processing and BENZ's classifier adaptation, but it still does not show or suggest the claimed use of the normalized frequency spectrum as input to the hyper model to classify the corruption that, in turn, forms the basis for updating the classifier and then generating the claimed classification output. As such, the combination fails to establish a prima facie case of obviousness.”
Examiner respectfully disagrees. The rejection combines references according to their functions in one processing chain. SHEN provides the input data that contains the image corruption and applies a Fourier transformation to the input data. BUNAZAWA teaches normalizing a frequency spectrum to generate a normalized frequency spectrum to be used as input to a map DM model for classifying anomalies in the input data. BENZ teaches adapting BN statistics for corrupted inputs before outputting a correct classification. The combined references, under BRI, establish a prima facie case of obviousness for the claimed steps, as shown in the 103 rejections below.
Applicant further argues:
“The Examiner's rationale for combining the references, namely to improve robustness, is insufficient. A general desire to improve robustness does not provide a reasoned basis to modify the cited references to arrive at the claimed invention, particularly where such modification would require integrating a corruption-classification mechanism with a BN update process that is specifically conditioned on the classification outcome, which is what the Examiner proposes with combining the various references. Such a modification would constitute a non-trivial redesign of the cited systems and is not a predictable use of prior art elements according to their established functions.
Accordingly, the cited references fail to teach or suggest the claimed limitations, either individually or in combination, and the rejection under 35 U.S.C. §103 should be withdrawn.”
Examiner respectfully disagrees. The rationale of improving robustness is for combining BENZ, which is introduced after BUNAZAWA has already been combined with SHEN. The motivation to combine BUNAZAWA is to improve the accuracy of determination as to whether there is an anomaly (i.e., corruption) based on various input variables such as vibration or sounds from sensors (i.e., input data) (BUNAZAWA [0010], [0031], and [0033]). Therefore, the motivation for combining SHEN, BUNAZAWA, and BENZ is more than simply improving robustness. Additionally, BENZ explicitly teaches a method for improving corruption robustness using batch normalization (BENZ [page 1, Title and Abstract]). Accordingly, the references provide sufficient reasoned basis for one of ordinary skill in the art to combine to arrive at the claimed invention.
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 1-17 and 19-21 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 and 21 are directed to a process. Claims 9-17 and 19-20 are directed to a machine or an article of manufacture.
With respect to claims 1, 9, and 15:
2A Prong 1: The claim recites an abstract idea. Specifically:
generating/generate a frequency spectrum associated with the input data, wherein the generating includes creating the frequency spectrum by applying a frequency domain transformation on the input data; (Mathematical concept – generating a frequency spectrum by applying a frequency domain transformation on the input data involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
normalizing/normalize the frequency spectrum to generate a normalized frequency spectrum; (Mathematical concept – normalizing the frequency spectrum involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
(Claims 1 and 9) […] classify corruptions utilizing at least a Fourier transform of the input data; (Mathematical concepts – Classifying corruptions utilizing a Fourier transform involves mathematical calculations (see paragraph [0049]) – see MPEP § 2106.04(a)(2)(I))
(Claim 15) classify, utilizing at least a Fourier transform of the input data, a corruption associated with the input data based on an output of the hyper model; (Mathematical concepts – Classifying a corruption utilizing a Fourier transform involves mathematical calculations (see paragraph [0049]) – see MPEP § 2106.04(a)(2)(I))
updating/update one or more weights associated with the classifier based on the corruption associated with the input data; (Mathematical concept – updating weights involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
(Claims 1 and 9) in response to the corruption and corresponding batch norm (BN) statistic, updating one or more network BN statistics associated with the machine-learning network; (Mathematical concepts – updating BN statistics in response to the corruption and corresponding BN statistic involves mathematical calculations (see paragraphs [0024] and [0049]) – see MPEP § 2106.04(a)(2)(I))
If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process, but for the recitation of generic computer components, then the claim limitations fall within the mathematical or mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
(Claim 1) receiving an input data from a sensor, wherein the input data is indicative of image information, radar information, sonar information, or sound information; (Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).)
(Claim 9) an input interface configured to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claims 9 and 15) receive input data from a sensor, wherein the sensor includes a camera, a radar, a sonar, or a microphone; (Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).)
(Claim 9) a processor in communication with the input interface, (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I))
(Claim 1) sending […]
(Claim 9) send […]
[…] the normalized frequency spectrum to a hyper model configured to […] (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
(Claim 15) inputting the normalized frequency spectrum to a hyper model utilizing a classifier (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
utilizing the normalized frequency spectrum as input to the hyper model […] (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
(Claims 1 and 9) wherein the hyper model includes a classifier; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
outputting/output a classification associated with the input data […] (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).)
[…] utilizing the classifier with updated weights. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
(Claim 1) receiving an input data from a sensor, wherein the input data is indicative of image information, radar information, sonar information, or sound information; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
(Claim 9) an input interface configured to […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
(Claims 9 and 15) receive input data from a sensor, wherein the sensor includes a camera, a radar, a sonar, or a microphone; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
(Claim 9) a processor in communication with the input interface, (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I))
(Claim 1) sending […]
(Claim 9) send […]
[…] the normalized frequency spectrum to a hyper model configured to […] (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
(Claim 15) inputting the normalized frequency spectrum to a hyper model utilizing a classifier; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
utilizing the normalized frequency spectrum as input to the hyper model […] (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
(Claims 1 and 9) wherein the hyper model includes a classifier; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
outputting/output a classification associated with the input data […] (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).)
[…] utilizing the classifier with updated weights. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 2:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein generating the frequency spectrum is only associated with a first channel of the input data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein generating the frequency spectrum is only associated with a first channel of the input data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 3:
2A Prong 1: The claim recites an abstract idea. Specifically:
wherein the frequency domain transformation on the input data includes utilizing a wavelength transform (Mathematical concept – utilizing a wavelength transform to perform the frequency domain transformation on the input data involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
Additionally, the claim does not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 4:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the corruption includes Gaussian noise, shot noise, motion blur, zoom blur, compression, or brightness changes (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the corruption includes Gaussian noise, shot noise, motion blur, zoom blur, compression, or brightness changes (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 5:
2A Prong 1: The claim recites an abstract idea. Specifically:
wherein the frequency domain transformation on the input data utilizes a Fourier transform (Mathematical concept – utilizing a Fourier transform for the frequency domain transformation involves mathematical calculations– see MPEP § 2106.04(a)(2)(I))
Additionally, the claim does not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 6:
2A Prong 1: The claim recites an abstract idea. Specifically:
[…] classify a clean image (Mental process – classifying a clean image can be practically performed in the human mind, or by a human using a pen and paper as a physical aid – see MPEP § 2106.04(a)(2)(III))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the hyper model is configured to (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the hyper model is configured to (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 7:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the classifier is a pre-trained classifier (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the classifier is a pre-trained classifier (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 8:
2A Prong 1: The claim recites an abstract idea. Specifically:
updating the one or more weights (Mathematical concept – updating weights involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
in response to utilizing a look-up table defining BN statistics associated with the corruption (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
in response to utilizing a look-up table defining BN statistics associated with the corruption (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 10:
2A Prong 1: The claim recites an abstract idea. Specifically:
update the one or more weights associated with the classifier […] (Mathematical concept – updating weights involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
[…] or directly updating the one or more weights (Mathematical concept – updating weights involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
utilizing a look-up table (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
utilizing a look-up table (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 11:
2A Prong 1: The claim recites an abstract idea. Specifically:
wherein the frequency spectrum includes a Fourier transform of the input data (Mathematical concept – the claim involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
Additionally, the claim does not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 12:
2A Prong 1: The claim recites an abstract idea. Specifically:
wherein the frequency spectrum includes a wavelength transform of the input data (Mathematical concept – the claim involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
Additionally, the claim does not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 13:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the hyper model is a three-layer fully connected neural network. (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the hyper model is a three-layer fully connected neural network. (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 14:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the three fully connected layers include a size of 1024 neurons, 512 neurons, and 16 neurons. (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the three fully connected layers include a size of 1024 neurons, 512 neurons, and 16 neurons. (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 16:
2A Prong 1: The claim recites an abstract idea. Specifically:
update one or more weights associated with the classifier based on a lookup table identifying information associated with the corruption (Mathematical concept – updating weights involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
Additionally, the claim does not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 17:
2A Prong 1: The claim recites an abstract idea. Specifically:
update one or more weights associated with the classifier (Mathematical concept – updating weights involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
Additionally, the claim does not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 19:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the hyper model includes three layers (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the hyper model includes three layers (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 20:
2A Prong 1: The claim recites an abstract idea. Specifically:
update one or more weights of the classifier (Mathematical concept – updating weights involves mathematical calculations – see MPEP § 2106.04(a)(2)(I))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
utilizing a look-up table defining batch norm statistics associated with the corruption. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
utilizing a look-up table defining batch norm statistics associated with the corruption. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
With respect to claim 21:
2A Prong 2: The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the hypermodel is pre-trained utilizing natural samples without any corruption. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
2B: The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the hypermodel is pre-trained utilizing natural samples without any corruption. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible.
Claim 15-17 and 19-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to non-statutory subject matter.
With respect to claim 15:
The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of A computer-program product storing instructions encompasses software per se.
A claim whose BRI covers non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. See MPEP 2106.03(II). Computer program product is not being embodied on a medium for its functionality to be realized. Therefore, the claim is directed to a software per se. Accordingly, Claim 15 fails to recite statutory subject matter under 35 U.S.C. 101.
It is suggested that claim 15 be amended to recite A non-transitory computer readable storage medium storing computer-program product… to overcome this rejection.
With respect to claims 16-17 and 19-20:
The dependent claims inherent the deficiencies of their respective parent claims and are likewise rejected.
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.
Claims 1, 3-7, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over SHEN (“Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering”) in view of BUNAZAWA (US 20220044502 A1) and BENZ (“Revisiting Batch Normalization for Improving Corruption Robustness”), hereafter SHEN, BUNAZAWA, and BENZ respectively.
Regarding Claim 1:
SHEN teaches:
A computer-implemented method for training a machine-learning network, comprising:
receiving an input data from a sensor, wherein the input data is indicative of image information, radar information, sonar information, or sound information; (SHEN [pg. 3, section 3 Background and Setup] teaches: "given a single image as input (e.g. captured by a front-facing camera on a self-driving car)," Examiner's note: SHEN explicitly teaches receiving input data from a sensor (i.e., front-facing camera), which is indicative of image information.)
generating a frequency spectrum associated with the input data, wherein the generating includes creating the frequency spectrum by applying a frequency domain transformation on the input data; (SHEN [pg. 2, Figure 1] teaches: "A frequency-space branch is added to the backbone when frequency-related perturbations (e.g., blur, noise) need to be handled." SHEN [pg. 19, A.5] teaches: "Formally, we do a standard 2-D Fourier Transform, and using the absolute value of each complex number and form another channel of the image,")
SHEN is not relied upon for teaching:
normalizing the frequency spectrum to generate a normalized frequency spectrum;
sending the normalized frequency spectrum to a hyper model configured to classify corruptions utilizing at least a Fourier transform of the input data;
utilizing the normalized frequency spectrum as input to the hyper model in order to classify a corruption associated with the input data;
updating one or more weights associated with the classifier based on the corruption associated with the input data; and
in response to the corruption and corresponding batch norm (BN) statistic, updating one or more network BN statistics associated with the machine-learning network; and
outputting a classification associated with the input data utilizing the classifier with updated weights.
However, BUNAZAWA teaches: normalizing the frequency spectrum to generate a normalized frequency spectrum; (BUNAZAWA [0130] teaches: "The distribution of a frequency component obtained by subjecting the time-series data of the gear rotation speed Ngear to fast Fourier transform may be normalized, and the normalized feature quantity may be used as input variables fed to the map.")
sending the normalized frequency spectrum to a hyper model configured to classify corruptions utilizing at least a Fourier transform of the input data; (BUNAZAWA [0130] teaches: "The distribution of a frequency component obtained by subjecting the time-series data of the gear rotation speed Ngear to fast Fourier transform may be normalized, and the normalized feature quantity may be used as input (i.e., sending) variables fed to the map. […] the normalized feature quantity may be used as input variables fed to the map." BUNAZAWA [0138] teaches: "The input variables fed to the map defined by the map data DM may include the sound NZ." BUNAZAWA [0086] teaches: "Specifically, a fully-connected feed-forward neural network having a single middle layer is used as the map." BUNAZAWA [0145] teaches: "The neural network is not limited to a fully-connected feed-forward neural network. For example, a one-dimensional convolutional neural network may be used. […] For example, the state of the gear may be identified using classification by a support vector machine.” BUNAZAWA [0146] teaches: "In the process of S105, the number of middle layers in the neural network is one. However, the number of middle layers may be two or more.” BUNAWAZA [0040] teaches: “The above-described configuration is capable of determining whether the gear is in a damaged state. It is thus possible to detect anomalies (i.e., to classify corruptions) of causes in different categories.” Examiner's note: Paragraph [0025] of the specification states that “a shallow 3-layer fully connected neural network can identify 16 corruption types” and paragraph [0026] states “The hyper model305 may identify the type of input corruption, e.g., motion blur or Gaussian noise”, and thus the hyper model can be defined as a 3-layer fully connected network, per the specification. Under broadest reasonable interpretation “a hyper model” can be interpreted as BUNAZAWA's map data DM model, which is a fully-connected feed-forward neural network that can have a single middle layer, or two or more middle layers. Furthermore, BUNAZAWA [0130] teaches that the distribution of a frequency component is obtained by subjecting the time-series data to a fast Fourier transform to normalize the time-series data (i.e., normalized frequency spectrum). Then, the normalized data by the fast Fourier transform is used as input (i.e., sending) for the map data DM model. The map data DM model is capable of determining gear state anomalies using classification (i.e., configured to classify corruptions) by a support vector machine by using the time-series data as input after being subjected to normalization by the fast Fourier transform.)
utilizing the normalized frequency spectrum as input to the hyper model in order to classify a corruption associated with the input data, wherein the hyper model includes a classifier; (Examiner's note: As taught above by BUNAZAWA [0040], [0086], [0130], [0138], [0145] and [0146], under broadest reasonable interpretation, "to classify a corruption" can be interpreted as the map data DM model (i.e., hyper model) being capable of determining gear state anomalies using classification (i.e., configured to classify corruptions) by a support vector machine (i.e., hyper model includes a classifier) by using the time-series data as input after being subjected to normalization by the fast Fourier transform.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN and BUNAZAWA before them, to include BUNAZAWA's normalized Fourier transform and map in SHEN's training method against image corruption. One would have been motivated to make such a combination in order to improve the accuracy of determination as to whether there is an anomaly (i.e., corruption) in the transmission based on various input variables such as vibration or sounds from sensors (i.e., input data) (BUNAZAWA [0010], [0031], and [0033]).
SHEN in view of BUNAZAWA is not relied upon for teaching:
updating one or more weights associated with the classifier based on the corruption associated with the input data; and
in response to the corruption and corresponding batch norm (BN) statistic, updating one or more network BN statistics associated with the machine-learning network; and
outputting a classification associated with the input data utilizing the classifier with updated weights.
However, BENZ teaches: updating one or more weights associated with the classifier based on the corruption associated with the input data; (BENZ [pg. 2, section 1 Introduction] teaches: "As indicated in Figure 1, we investigate and find that such influence on the model performance can be at least partially mitigated by estimating and adapting the statistics with a few representation samples from the corruption domain." (BENZ [pg. 2, section 1 Introduction] teaches: "As indicated in Figure 1, we investigate and find that such influence on the model performance can be at least partially mitigated by estimating and adapting the statistics with a few representation samples from the corruption domain." BENZ [pg. 1, Abstract] teaches: "We find that simply estimating and adapting the BN statistics on a few (32 for instance) representation samples, without retraining the model, improves the corruption robustness by a large margin on several benchmark datasets with a wide range of model architectures." BENZ [pg. 1, Figure 1] teaches: "An image under corruption changes the prediction from “German Shepherd” to “Beaver”. After rectifying the BN statistics, the corrupted image is classified correctly." BENZ [pg. 6, section 6.2. Impact of mean and variance] teaches: "Rectifying the BN statistics involves the manipulation of two parameters, namely the mean
μ
and variance
σ
2
." Examiner's note: paragraph [0046] of the instant application states: "The system may update the classifier 309 with the corresponding BN statistics. Upon updating the model of the classifier 309, the classifier 309 may output the corresponding classification 311." Additionally, paragraph [0031] of the instant application states: "Model adaptation may include updating model's parameters, or even architecture of the model. The system may update BN statistics to adapt the model to update weights based on the corruption." Therefore, under BRI in light of the specification, "updating the one or more weights" can be interpreted as rectifying the BN statistics of BENZ's classifier.”)
in response to the corruption and corresponding batch norm (BN) statistic, updating one or more network BN statistics associated with the machine-learning network; (BENZ [pg. 1, Abstract] teaches: "We find that simply estimating and adapting the BN statistics on a few (32 for instance) representation samples, without retraining the model, improves the corruption robustness by a large margin on several benchmark datasets with a wide range of model architectures. For example, on ImageNet-C, statistics adaptation improves the top1 accuracy of ResNet50 (i.e., machine-learning network) from 39.2% to 48.7%." BENZ [pg. 1, Figure 1] teaches: "An image under corruption changes the prediction from “German Shepherd” to “Beaver”. After rectifying the BN statistics, the corrupted image is classified correctly." Examiner’s note: Under BRI, “in response to the corruption updating one or more network BN statistics” can be interpreted as Figure 1, which describes that when the model outputs an incorrect classification such as predicting that a “German Shepherd” is a “Beaver”, the BN statistics are rectified (i.e., updating) in order to output a corrected classification.)
outputting a classification associated with the input data utilizing the classifier with updated weights. (BENZ [pg. 1, Figure 1] teaches: "An image under corruption changes the prediction from “German Shepherd” to “Beaver”. After rectifying the BN statistics, the corrupted image is classified correctly.")
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, and BENZ before them, to include BENZ' estimating and adapting BN statistics in SHEN/BUNAZAWA's training method against image corruption. One would have been motivated to make such a combination in order to improve model robustness under corruptions (BENZ [pg. 4, section 3.3. Motivation for rectifying batch normalization]).
Regarding Claim 3:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. SHEN further teaches:
wherein the frequency domain transformation on the input data includes utilizing a wavelength transform. (SHEN [pg. 19, A.5] teaches: "Formally, we do a standard 2-D Fourier Transform, and using the absolute value of each complex number and form another channel of the image," Examiner's note: a "wavelength transformation" can be considered a Fourier transform or any other frequency domain transformation.)
Regarding Claim 4:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. SHEN further teaches:
wherein the corruption includes Gaussian noise, shot noise, motion blur, zoom blur, compression, or brightness changes. (SHEN [pg. 2, Figure 1] teaches: "A frequency-space branch is added to the backbone when frequency-related perturbations (e.g., blur, noise) need to be handled.")
Regarding Claim 5:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. SHEN further teaches:
wherein the frequency domain transformation on the input data utilizes a Fourier transform. (SHEN [pg. 19, A.5] teaches: "Formally, we do a standard 2-D Fourier Transform, and using the absolute value of each complex number and form another channel of the image,")
Regarding Claim 6:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. SHEN further teaches:
wherein the hyper model is configured to classify a clean image. (SHEN [pg. 2, section I. Introduction] teaches: "Finally, we propose a comprehensive robustness evaluation standard under four different scenarios: clean data, single-perturbation data, multi-perturbation data, and previously unseen data." Examiner's note: under BRI, the "hyper model" can be interpreted as the map data DM model from BUNAZAWA, which determines the states of input data of interest (i.e., normal or damaged). Therefore, under BRI, "the hyper model is configured to classify a clean image" can be interpreted as the combination of the map data DM (i.e., hyper model) that makes an evaluation using SHEN's clean data.)
Regarding Claim 7:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. BENZ further teaches:
wherein the classifier is a pre-trained classifier. (BENZ [pg. 4, Figure 2] teaches: "[…] ResNet50 pretrained on ImageNet." Examiner’s note: ResNet50 is a classifier model.)
Regarding Claim 21:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. BENZ further teaches:
wherein the hypermodel is pre-trained utilizing natural samples without any corruption. (BENZ [pg. 1, Abstract] teaches: "The performance of DNNs trained on clean images has been shown to decrease when the test images have common corruptions." BENZ [pg. 1, Figure 1] teaches a clean image. BENZ [pg. 4, section 4. Experimental Setup] teaches: "We evaluate the performance of rectifying the BN statistics on various models trained on the corresponding clean dataset (i.e., utilizing natural samples without any corruption)." Examiner’s note: BUNAZAWA’s DM model is also a pre-trained model, and a person of ordinary skill in the art could apply BENZ’s training using a clean dataset in BUNAZAWA’s model.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over SHEN in view of BUNAZAWA, and BENZ as applied to claim 1 above, and further in view of YAMAKAJI (US 20220335276 A1), hereafter YAMAKAJI.
Regarding Claim 2:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. SHEN in view of BUNAZAWA and BENZ are not relied upon for teaching, but YAMAKAJI teaches:
wherein generating the frequency spectrum is only associated with a first channel of the input data. (YAMAKAJI [0121] teaches: "[…] a method of performing Fourier transform on each channel and then making conversion to one channel in a fully connected layer, or a method of merely weighting each channel in advance so that the input signal 20 to be inputted to the input layer 11 has one channel, can be used." Examiner’s note: under BRI, “a first channel of the input data” can be interpreted as inputting the input signal as one channel, the input signal being the result of a Fourier transform.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, BENZ, and YAMAKAJI before them, to include YAMAKAJI channel input layer processing in SHEN/BUNAZAWA/BENZ' training method against image corruption. One would have been motivated to make such a combination in order to reduce calculation amount and increase calculation speed based on input size (YAMAKAJI [0147]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over SHEN in view of BUNAZAWA and BENZ, as applied to claim 1 above, and further in view of SEGU (US 20230122207 A1), hereafter SEGU.
Regarding Claim 8:
SHEN in view of BUNAZAWA and BENZ teaches the elements of claim 1 as outlined above. BENZ further teaches:
wherein updating the one or more weights is in response to utilizing […] BN statistics associated with the corruption. (BENZ [pg. 1, Abstract] teaches: "We find that simply estimating and adapting the BN statistics on a few (32 for instance) representation samples, without retraining the model, improves the corruption robustness by a large margin on several benchmark datasets with a wide range of model architectures." BENZ [pg. 1, Figure 1] teaches: "An image under corruption changes the prediction from “German Shepherd” to “Beaver”. After rectifying the BN statistics, the corrupted image is classified correctly." BENZ [pg. 6, section 6.2. Impact of mean and variance] teaches: "Rectifying the BN statistics involves the manipulation of two parameters, namely the mean
μ
and variance
σ
2
." Examiner's note: paragraph [0031] of the instant application recites "Model adaptation may include updating model's parameters, or even architecture of the model. The system may update BN statistics to adapt the model to update weights based on the corruption." Therefore, "updating the one or more weights" can be interpreted as rectifying the parameters of the classifier.)
SHEN in view of BUNAZAWA and BENZ is not relied upon for teaching, but SEGU teaches: […] a look-up table defining BN statistics associated with the corruption. (SEGU [0008] teaches: "The computing system includes one or more processors and one or more non-transitory computer-readable media that collectively store [...] plurality of different batch normalization layers respectively associated with a plurality of source domains;” SEGU [0040] teaches: “At training time, the multi-source batch normalization layer can collect and apply domain-specific batch statistics
(
μ
d
b
,
σ
d
b
2
)
,while accordingly updating the domain population statistics as moving average of the statistics for every batch b.” SEGU [0022] teaches: “During inference, a computing system can determine a target set of batch normalization statistics for a target sample associated with a target domain.” SEGU [0045] teaches: "[…] separate batch normalization statistics are kept for each domain, […]". Examiner's note: SEGU teaches maintaining a stored collection of batch normalization statistics for each domain, and each domain represents any possible scenario from collected samples as discusses in SEGU [0003]. During inference, the computing system determines (i.e., looks up) a target set of batch normalization statistics for a target sample (i.e., image) associated with a target domain. Under BRI, “a look-up table defining batch norm statistics associated with the corruption” can be interpreted as the multi-source batch normalization layer that collects and applies domain-specific batch statistics.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, BENZ, and SEGU before them, to include SEGU’s multi-source batch normalization layer that collects and applies domain-specific batch statistics in SHEN/BUNAZAWA/BENZ' training method against image corruption. One would have been motivated to conserve data collection resources, thereby “reducing the consumption of computing resources such as processor usage, memory usage, and/or network bandwidth.” (SEGU [0030]).
Claims 9, 11-13, 15-17, and 19 rejected under 35 U.S.C. 103 as being unpatentable over SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI.
Regarding Claim 9:
The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. However, the combination of SHEN, BUNAZAWA, and BENZ is not relied upon for teaching, but YAMAKAJI teaches:
an input interface configured to receive input data from a sensor […] (YAMAKAJI [0039] teaches: "[0039] The input unit 37 is formed by a keyboard, a mouse, a microphone, a camera, or the like.")
a processor in communication with the input interface, wherein the processor is programmed to: (YAMAKAJI [0037] teaches: "[0037] As shown in FIG. 1, the hardware 100 includes a central processing unit (CPU) 30, and an input/output interface 35 is connected to the CPU".)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, BENZ, and YAMAKAJI before them, to include YAMAKAJI input interface and CPU in SHEN/BUNAZAWA/BENZ' training method against image corruption. One would have been motivated to make such a combination in order to receive and process different types of input data such as images from a camera or sound from a microphone.
Regarding Claim 11:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 9 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding Claim 12:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 9 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Claim 13:
SHEN in view of BUNAZAWA, BENZ and YAMAKAJI teaches the elements of claim 9 as outlined above. BUNAZAWA further teaches:
wherein the hyper model is a three-layer fully connected neural network (BUNAZAWA [0086] teaches: “In the present embodiment, the map is a function approximator. Specifically, a fully-connected feed-forward neural network having a single middle layer is used as the map (i.e., fully connected neural network hyper model).” BUNAZAWA [0146] teaches: "In the process of S105, the number of middle layers in the neural network is one. However, the number of middle layers may be two or more (i.e., three-layer).”
Regarding Claim 15:
The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. However, the combination of SHEN, BUNAZAWA, and BENZ is not relied upon for teaching, but YAMAKAJI teaches:
A computer-program product storing instructions which, when executed by a computer, cause the computer to: (YAMAKAJI [0037] teaches: "the CPU 30 loads a program stored in a hard disk drive (HDD) 33 or a solid state drive (SSD, not shown) onto a random access memory (RAM) 32, and executes the program while performing reading and writing as necessary. Thus, the CPU 30 performs various processes to cause the hardware 100 to operate as a device having a predetermined function.")
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, BENZ, and YAMAKAJI before them, to include YAMAKAJI stored program and CPU in SHEN/BUNAZAWA/BENZ' training method against image corruption. One would have been motivated to make such a combination in order to execute the program and operate the device having a predetermined function (i.e., training method against image corruption).
Regarding Claim 16:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Regarding Claim 17:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 15 as outlined above. BENZ further teaches:
wherein the instructions cause the computer to update one or more weights associated with the classifier. (BENZ [pg. 2, section 1 Introduction] teaches: "As indicated in Figure 1, we investigate and find that such influence on the model performance can be at least partially mitigated by estimating and adapting the statistics with a few representation samples from the corruption domain." Examiner’s note: the computer-program product with instructions that cause the computer to execute a predetermined function (i.e., update one or more weights) is taught by the combination of SHEN/BUNAZAWA/BENZ/YAMAKAJI above in claim 15.)
Regarding Claim 19:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 15 as outlined above. BUNAZAWA further teaches:
wherein the hyper model includes three layers. (BUNAZAWA [0086] teaches: “In the present embodiment, the map is a function approximator. Specifically, a fully-connected feed-forward neural network having a single middle layer is used as the map (i.e., fully connected neural network hyper model).” BUNAZAWA [0146] teaches: "In the process of S105, the number of middle layers in the neural network is one. However, the number of middle layers may be two or more (i.e., three-layer).”)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI as applied to claim 9 above, and further in view of SEGU.
Regarding Claim 10:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 9 as outlined above. BENZ further teaches:
wherein the processor is programmed to update the one or more weights associated with the classifier […] (BENZ [pg. 1, Abstract] teaches: "We find that simply estimating and adapting the BN statistics on a few (32 for instance) representation samples, without retraining the model, improves the corruption robustness by a large margin on several benchmark datasets with a wide range of model architectures." BENZ [pg. 1, Figure 1] teaches: "An image under corruption changes the prediction from “German Shepherd” to “Beaver”. After rectifying the BN statistics, the corrupted image is classified correctly." BENZ [pg. 6, section 6.2. Impact of mean and variance] teaches: "Rectifying the BN statistics involves the manipulation of two parameters, namely the mean
μ
and variance
σ
2
." Examiner's note: paragraph [0031] of the instant application recites "Model adaptation may include updating model's parameters, or even architecture of the model. The system may update BN statistics to adapt the model to update weights based on the corruption." Therefore, "to update the one or more weights associated with the classifier" can be interpreted as rectifying the parameters of the classifier. Furthermore, the processor is taught by YAMAKAJI [0039] as outlined above in claim 9.)
or directly updating the one or more weights. (BENZ [pg. 3, section 3.1. Revisiting classical batch normalization] discusses directly updating the BN statistics of the model (i.e., updating the weights) by using a moving average: "population statistics
μ
p
and
σ
p
2
are estimated over the whole training dataset through moving average.)
However, SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI is not relied upon for teaching, but SEGU teaches: […] utilizing a look-up table (SEGU [0008] teaches: "The computing system includes one or more processors and one or more non-transitory computer-readable media that collectively store [...] plurality of different batch normalization layers respectively associated with a plurality of source domains;” SEGU [0040] teaches: “At training time, the multi-source batch normalization layer can collect and apply domain-specific batch statistics
(
μ
d
b
,
σ
d
b
2
)
,while accordingly updating the domain population statistics as moving average of the statistics for every batch b.” SEGU [0022] teaches: “During inference, a computing system can determine a target set of batch normalization statistics for a target sample associated with a target domain.” SEGU [0045] teaches: "[…] separate batch normalization statistics are kept for each domain, […]". Examiner's note: SEGU teaches maintaining a stored collection of batch normalization statistics for each domain, and each domain represents any possible scenario from collected samples as discusses in SEGU [0003]. During inference, the computing system determines (i.e., looks up) a target set of batch normalization statistics for a target sample (i.e., image) associated with a target domain. Under BRI, “utilizing a look-up table” can be interpreted as the multi-source batch normalization layer that collects and applies domain-specific batch statistics.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, BENZ, YAMAKAJI, and SEGU before them, to include SEGU’s multi-source batch normalization layer that collects and applies domain-specific batch statistics in SHEN/BUNAZAWA/YAMAKAJIBENZ' training method against image corruption. One would have been motivated to conserve data collection resources, thereby “reducing the consumption of computing resources such as processor usage, memory usage, and/or network bandwidth.” (SEGU [0030]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI, as applied to claim 13, and further in view of CHEN (US 20160293167 A1) hereafter CHEN.
Regarding Claim 14:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 13 as outlined above. However, SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI is not relied upon for teaching, but CHEN teaches:
wherein the three fully connected layers include a size of 1024 neurons, 512 neurons, and 16 neurons. (CHEN [0063] teaches: "Table 1 shows baseline results for various configurations of fully-connected networks: with variable number of layers (top), with variable context sizes (middle) and with variable number of nodes (bottom.)" CHEN [0127] teaches: "[0127] Alternatively, a different number of layers (e.g., 2, 3, 5, 8, etc.) or a different number of nodes per layer (e.g., 16, 32, 64, 128, 512, 1024, etc.) may be used.")
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, BENZ, YAMAKAJI, and CHEN before them, to include CHEN's fully-connected layer configuration in SHEN/BUNAZAWA/BENZ/YAMAKAJI's training method against image corruption. One would have been motivated to make such a combination in order to in order to use full-connected model to increase accuracy and significantly decrease error rates (CHEN [0045]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI, as applied to claim 15 above, and further in view of SEGU.
Regarding Claim 20:
SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI teaches the elements of claim 15 as outlined above. BENZ further teaches:
wherein the instructions cause the computer to update one or more weights of the classifier utilizing […] batch norm statics associated with the corruption. (BENZ [pg. 1, Abstract] teaches: "We find that simply estimating and adapting the BN statistics on a few (32 for instance) representation samples, without retraining the model, improves the corruption robustness by a large margin on several benchmark datasets with a wide range of model architectures." BENZ [pg. 1, Figure 1] teaches: "An image under corruption changes the prediction from “German Shepherd” to “Beaver”. After rectifying the BN statistics, the corrupted image is classified correctly." BENZ [pg. 6, section 6.2. Impact of mean and variance] teaches: "Rectifying the BN statistics involves the manipulation of two parameters, namely the mean
μ
and variance
σ
2
." Examiner's note: paragraph [0031] of the instant application recites "Model adaptation may include updating model's parameters, or even architecture of the model. The system may update BN statistics to adapt the model to update weights based on the corruption." Therefore, "updating the one or more weights" can be interpreted as rectifying the parameters of the classifier.)
However, SHEN in view of BUNAZAWA, BENZ, and YAMAKAJI, are not relied upon for teaching, but SEGU teaches: […] a look-up table defining batch norm statics associated with the corruption. (SEGU [0008] teaches: "The computing system includes one or more processors and one or more non-transitory computer-readable media that collectively store [...] plurality of different batch normalization layers respectively associated with a plurality of source domains;” SEGU [0040] teaches: “At training time, the multi-source batch normalization layer can collect and apply domain-specific batch statistics
(
μ
d
b
,
σ
d
b
2
)
,while accordingly updating the domain population statistics as moving average of the statistics for every batch b.” SEGU [0022] teaches: “During inference, a computing system can determine a target set of batch normalization statistics for a target sample associated with a target domain.” SEGU [0045] teaches: "[…] separate batch normalization statistics are kept for each domain, […]". Examiner's note: SEGU teaches maintaining a stored collection of batch normalization statistics for each domain, and each domain represents any possible scenario from collected samples as discusses in SEGU [0003]. During inference, the computing system determines (i.e., looks up) a target set of batch normalization statistics for a target sample (i.e., image) associated with a target domain. Under BRI, “a look-up table defining batch norm statistics associated with the corruption” can be interpreted as the multi-source batch normalization layer that collects and applies domain-specific batch statistics.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHEN, BUNAZAWA, BENZ, and SEGU before them, to include SEGU’s multi-source batch normalization layer that collects and applies domain-specific batch statistics in SHEN/BUNAZAWA/BENZ' training method against image corruption. One would have been motivated to conserve data collection resources, thereby “reducing the consumption of computing resources such as processor usage, memory usage, and/or network bandwidth.” (SEGU [0030]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.S.L./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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