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 .
This action is in response to communications filed on 01/23/2026.
Claims 1-8 are canceled.
Claims 9-17 are pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/23/2026 has been entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 15 is objected to because of the following informalities:
As per claim 15, the term “and” should be inserted after “…third difference signal;” in line 15 and a semicolon should be inserted after “wherein the monitoring comprises” in line 17.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 9 is amended to recite “feeding the real input data stream, in parallel with the generated output signals, to the discriminator to determine one or more distances that are used for evaluating the real input data stream for controlling driving functions”. However, the specification does not support the above features. The original specification (13 pages) recites “based on the real input data stream, the trained Al module generates output signals suitable for the driving function” and “in parallel with this, the real input data stream is fed to the discriminator of the trained monitoring module, and the discriminator determines distances therefrom that are used for evaluating the input data stream” (e.g. in page 3). This merely describes that the input signal is fed to the AI module to generate output signals to control driving and, at the same time (i.e. in parallel), the input signal is fed to the discriminator to generate distances. This can also be seen in figure 2 described by page 9. This is different than the amended limitations, however, which imply that the generated output signals are also fed into the discriminator to determine distance and control driving (note also applicant’s remarks that state “the discriminator receiving the real input data stream in parallel with generated output signals of the AI module as recited in claims 9 and 12… the discriminator determining one or more distance values from such a parallel feed that are used for evaluating the real input data stream for controlling driving functions”, which is not supported). As such, the claim lacks written description. Independent claim 12 also recites similar limitations and therefore have the same problem. Due at least to their dependency upon claims 9 and 12, dependent claims 10-11 and 13-14 also fail to comply with the written description requirement.
Claim 12 is also amended to recite “the generator being configured to, in a training phase, train the monitoring module using real training data and false training data generated by the generator”. However, the specification does not support the above features. The original specification (13 pages) recites “first monitoring module for monitoring the input data stream of the Al module is trained by real training data and false training data” (e.g. in page 4) and “said training losses are used to train the generators and discriminators of the respective monitoring module” (e.g. in page 5). In other words, the generator does not train but is trained and is merely part of the monitoring module, which is different from what is claimed. As such, the claim lacks written description. Due at least to their dependency upon claim 12, dependent claims 13-14 also fail to comply with the written description requirement.
Response to Arguments
Previous claim interpretations under 112(f) have been withdrawn in view of amendments.
Previous rejections under 35 USC 112(b) have been withdrawn in view of amendments.
Applicant’s arguments with respect to the amended claims have been considered but are moot in view of new grounds of rejection. See rejections including Cricri et al (US 20190122072 A1) below. However, it is noted that, in response to applicant's arguments against the references individually (e.g. Schiegg allegedly not teaching an inference phase, distances, and ground truth data, etc.), one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). For example, an inference phase and distances are at least taught by newly cited Cricri (see below). Ground truth data and false ground truth data are taught by Lim (e.g. in paragraphs 16, 45 and 50, “generating a fake image of the first domain by decoding the latent variable in the first domain, generating a fake image of the second domain by decoding the latent variable in the second domain… ground truth information (GT) of the synthetic image [i.e. false ground truth] is also referred to as ground truth 1, and ground truth information of the real image is also referred to as ground truth 2… learning apparatus trains the detector 130 with the loss function 140 based on … the ground truth 1 … the ground truth 2”, and figure 1). It is noted that Cricri also teaches such features (e.g. in figure 2a). As such, the combination teaches the claimed features.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Notz et al (EP 3654246) in view of Cricri et al (US 20190122072 A1) and Schiegg et al. (US 20200331473 A1).
As per independent claim 9, Notz teaches a method for monitoring a real input data stream of an artificial intelligence (Al) module that forms part of a processing chain of a partially automated or automated driving function of a vehicle (e.g. in paragraphs 5, 7, 42-43, 51, and 59, “generating a signal indicating anomalous vehicle scenario using an evaluation machine comprising a discriminator network, a generator network, and an encoder network… wherein an input signal to the encoder network includes data from vehicle sensors… distinguish between driving scenarios that the vehicle is able to handle autonomous, from driving scenarios that the vehicle potentially will not be able to handle autonomous in a safe manner… sensors 120 generating signals 140 indicating current vehicle scenario… evaluation machine 150 generating signal 160 indicating anomalous scenario… signal may indicate to the driver that additional precaution is needed, such as that driver's hands are put on the steering wheel and the driver has readiness to brake… may enter a safe mode, such as including stopping the vehicle in a controlled manner”), comprising:
in a training phase, training a monitoring module using real training data and generated false training data generated by a generator (e.g. in paragraphs 17, 50 and 53, “x comes from real data and z is generated from the encoder network with input x… Calibration (or training) is depicted in Fig. 2… Fig. 2 depicts a structure of the evaluation machine during calibration of the discriminator D, generator G, and encoder E” and figure 2);
feeding the real training data and the generated false training data to a discriminator (e.g. in paragraphs 17, 50 and 53, “x comes from real data and z is generated from the encoder network with input x… Calibration (or training) is depicted in Fig. 2… Fig. 2 depicts a structure of the evaluation machine during calibration of the discriminator D, generator G, and encoder E” and figure 2 showing real training data and false (generated) training data fed to discriminator network D);
generating a calculation between the real training data and the generated false training data (e.g. in paragraphs 7, 26-27 and 53-54, “The reconstruction loss may be given by the function: LGx=‖x−G(E(x))‖1” showing difference between real and false; note: x is real, while G(E(x)) is false/generated, and figure 2);
in an inference phase, evaluating the Al module via the monitoring module (e.g. in paragraphs 7 and 51, “generating an anomaly score from an output signal of the encoder network, an output signal of the generator network, and/or an output signal of the discriminator network, wherein an input signal to the encoder network includes data from vehicle sensors… evaluation machine 150 generating signal 160 indicating anomalous scenario” and paragraphs 28-29, “For anomalous sensor signals x, the reconstruction loss may be large, since neither G nor E have been trained on anomalous sensor signals. Using the reconstruction loss…further improves the accuracy of indicating anomalous vehicle scenario”);
generating, via the Al module, output signals configured for a driving function based on the real input data stream (e.g. in paragraphs 51 and 59, “sensors 120 generating signals 140 indicating current vehicle scenario… evaluation machine 150 generating signal 160 indicating anomalous scenario… signal may indicate to the driver that additional precaution is needed, such as that driver's hands are put on the steering wheel and the driver has readiness to brake… may enter a safe mode, such as including stopping the vehicle in a controlled manner”); and
feeding the real input data stream to the discriminator to determine one or more calculations that are used for evaluating the real input data stream for controlling driving functions (e.g. in paragraphs 5, 7, 15, 26-29, 42-43, 51, and 59, “generating a signal indicating anomalous vehicle scenario using an evaluation machine comprising a discriminator network, a generator network, and an encoder network… Generate the anomaly score may include generating a reconstruction loss value… The reconstruction loss may be given by the function: LGx=‖x−G(E(x))‖1 [showing difference between real and false for signals 140] … wherein an input signal to the encoder network includes data from vehicle sensors… distinguish between driving scenarios that the vehicle is able to handle autonomous, from driving scenarios that the vehicle potentially will not be able to handle autonomous in a safe manner… sensors 120 generating signals 140 indicating current vehicle scenario… evaluation machine 150 generating signal 160 indicating anomalous scenario… signal may indicate to the driver that additional precaution is needed, such as that driver's hands are put on the steering wheel and the driver has readiness to brake… may enter a safe mode, such as including stopping the vehicle in a controlled manner” and figures 1-2),
but does not specifically teach generating, via the discriminator, the one or more calculation/calculations including one or more distance/distances and feeding in parallel with the generated output signals.
However, Cricri teaches generating, via a discriminator, one or more calculation/calculations including one or more distance/distances (e.g. in paragraphs 22, 31, and 47, “discriminator distinguishes between generated and real content… the discriminator neural network is considered a critic, which…provides an estimate of the distance between the probability distributions of the real training data and the generated data… online…where models continuously receive new streams of data and keep on learning, the whole system comprising the generator G 220, discriminator D1 240, discriminator D2 250 and the model which extracts key features may be part of the deployed system”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Notz to include the teachings of Cricri because one of ordinary skill in the art would have recognized the benefit of distinguishing between generated and real content and/or facilitating continuous learning.
but does not specifically teach feeding in parallel with the generated output signals.
However, Schiegg teaches feeding a real input data stream in parallel with generated output signals (e.g. in paragraphs 47 and 92, “optimization of an objective function 5, in dependence upon which, parameters of generator 41 and of discriminator 42 are adjusted” and figure 1 showing generated output signals 52 fed in parallel with input signals 23/24). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Schiegg because one of ordinary skill in the art would have recognized the benefit of allowing components to be appropriately adjusted and/or optimized.
As per claim 10, the rejection of claim 9 is incorporated and the combination further teaches wherein the generated false training data is generated by a background data source that are fed to the discriminator (e.g. Notz, in paragraph 17, “x comes from real data and z is generated from the encoder network with input x” and figure 2 showing feeding to discriminator; Schiegg, in paragraphs 60-61, “a random vector is ascertained in block 3 and transmitted to generator 4l”).
As per claim 11, the rejection of claim 9 is incorporated and the combination further teaches wherein a training loss with respect to the real training data is determined, based on the distance, and wherein the training loss is used for training the generator and the discriminator of the monitoring module (e.g. Cricri, in paragraphs 22, 43, and 46, “discriminator distinguishes between generated and real content… the discriminator neural network is considered a critic, which…provides an estimate of the distance between the probability distributions of the real training data and the generated data… loss computed from an output of the discriminator D1 240… loss computed through the discriminator D1 240 and applied to train the discriminator… loss computed through the discriminator D1 240 and applied to train the generator G 220”; Schiegg, in paragraphs 47 and 92, “optimization of an objective function 5, in dependence upon which, parameters of generator 41 and of discriminator 42 are adjusted” and figure 1).
Claims 12-14 are the apparatus method claims corresponding to method claims 9-11, and are rejected under the same reasons set forth, and the combination further teaches one or more processors and a memory, operatively coupled to the one or more processors, the memory storing instructions executable by the one or more processors (e.g. Notz, in paragraphs 40-41 and 51, and claim 11, “storage and processing requirements… storage for vehicle training… Controller 130… Computer readable storage medium containing instructions which, when executed by at least one processor, cause the processor to carry out the steps”; Cricri, in paragraph 57, “computer program code in a memory, and a processor that, when running the computer program code, causes the device to carry out the features of an embodiment”); and a monitoring module comprising a generator and a discriminator, wherein the one or more processors are configured to implement the generator and the discriminator (e.g. Notz, in paragraphs 5, 40-41 and 51, and claim 11, “an evaluation machine comprising a discriminator network, a generator network, and an encoder network… storage and processing requirements… storage for vehicle training… Controller 130… Computer readable storage medium containing instructions which, when executed by at least one processor, cause the processor to carry out the steps”; Cricri, in paragraphs 22, 47, and 57, “GAN includes at least two neural networks: a generator and a discriminator… continuously receive new streams of data and keep on learning, the whole system comprising the generator G 220, discriminator D1 240, discriminator D2 250 and the model which extracts key features… computer program code in a memory, and a processor that, when running the computer program code, causes the device to carry out the features of an embodiment”), the generator being configured to, in a training phase, train the monitoring module using real training data and false training data generated by the generator (e.g. Notz, in paragraphs 17, 28-29, 50 and 53, “x comes from real data and z is generated from the encoder network with input x… Calibration (or training) is depicted in Fig. 2… Fig. 2 depicts a structure of the evaluation machine during calibration of the discriminator D, generator G, and encoder E”), wherein the discriminator is configured to output one or more distance values representing a distance between the real training data and the false training data generated by the generator (e.g. Cricri, in paragraphs 22, 31, and 47, “discriminator distinguishes between generated and real content… the discriminator neural network is considered a critic, which…provides an estimate of the distance between the probability distributions of the real training data and the generated data”).
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Notz et al (EP 3654246) in view of Cricri et al (US 20190122072 A1), Schiegg et al. (US 20200331473 A1), and Lim et al (US 20200065635 A1).
As per independent claim 15, Notz teaches a method for monitoring a real input data stream and an output data stream of an artificial intelligence (Al) module that forms part of a processing chain of a partially automated or automated driving function of a vehicle (e.g. in paragraphs 5, 7, 42-43, 51, and 59, “generating a signal indicating anomalous vehicle scenario using an evaluation machine comprising a discriminator network, a generator network, and an encoder network… wherein an input signal to the encoder network includes data from vehicle sensors… distinguish between driving scenarios that the vehicle is able to handle autonomous, from driving scenarios that the vehicle potentially will not be able to handle autonomous in a safe manner… sensors 120 generating signals 140 indicating current vehicle scenario… evaluation machine 150 generating signal 160 indicating anomalous scenario… signal may indicate to the driver that additional precaution is needed, such as that driver's hands are put on the steering wheel and the driver has readiness to brake… may enter a safe mode, such as including stopping the vehicle in a controlled manner”), comprising:
training the Al module using real training data, and generating output data (e.g. in paragraphs 13-14, “output signal of the discriminator network… Updating the discriminator network may include affecting (increasing or decreasing) a networks loss value in a first direction (such as increasing and/or maximizing) by calibrating the discriminator network… x comes from real data”);
training a first monitoring module for monitoring the real input data stream of the Al module using the real training data and false training data, wherein the false training data are generated by a generator of the first monitoring module (e.g. in paragraphs 17, 50 and 53, “x comes from real data and z is generated from the encoder network with input x… Calibration (or training) is depicted in Fig. 2… Fig. 2 depicts a structure of the evaluation machine during calibration of the discriminator D, generator G, and encoder E” and figure 2 showing real training data and false (generated) training data fed to discriminator network D), and wherein the first monitoring module generates a first calculation signal (e.g. in paragraphs 27 and 53-54, “The reconstruction loss may be given by the function: LGx=‖x−G(E(x))‖1” showing difference between real and false for signals 140, and figures 1-2); and generating a second calculation signal and generating a third calculation signal (e.g. in paragraphs 27, 51, and 53-54, “The reconstruction loss may be given by the function: LGx=‖x−G(E(x))‖1” showing difference between real and false for signals 140, and figures 1-2);
monitoring, in an inference phase, both the real input data stream and the output data stream from the Al module (e.g. in paragraphs 28-29, “For anomalous sensor signals x, the reconstruction loss may be large, since neither G nor E have been trained on anomalous sensor signals. Using the reconstruction loss…further improves the accuracy of indicating anomalous vehicle scenario”), wherein the monitoring comprises (i) feeding the real input data stream to a discriminator of the trained first monitoring module and determining one or more calculations therefrom that are used for evaluating the real input data stream of the Al module (e.g. in paragraphs 5, 7, 15, 26-27, 42-43, 51, and 59, “generating a signal indicating anomalous vehicle scenario using an evaluation machine comprising a discriminator network, a generator network, and an encoder network… Generate the anomaly score may include generating a reconstruction loss value… The reconstruction loss may be given by the function: LGx=‖x−G(E(x))‖1 [showing calculations for signals 140] … wherein an input signal to the encoder network includes data from vehicle sensors… distinguish between driving scenarios that the vehicle is able to handle autonomous, from driving scenarios that the vehicle potentially will not be able to handle autonomous in a safe manner… sensors 120 generating signals 140 indicating current vehicle scenario… evaluation machine 150 generating signal 160 indicating anomalous scenario… signal may indicate to the driver that additional precaution is needed, such as that driver's hands are put on the steering wheel and the driver has readiness to brake… may enter a safe mode, such as including stopping the vehicle in a controlled manner” and figures 1-2),
but does not specifically teach training the AI module using real training data and ground truth data, the calculation/calculations include distance/distances and training a second monitoring module using the output data of the Al module and false output data, wherein the false output data are generated by a generator of the second monitoring module; training a third monitoring module using ground truth data and false ground truth data, wherein the false ground truth data are generated by a generator of the third monitoring module, determining, via the discriminator, one more distances, and (ii) feeding the output data stream to a discriminator of the trained second monitoring module, and determining, via the discriminator, one or more distances therefrom that are used for evaluating the output data stream of the Al module, and/or (iii) feeding the output data stream to a discriminator of the trained third monitoring module, and determining, via the discriminator, one or more distances therefrom that are used for evaluating the output data stream of the Al module.
However, Cricri teaches determining, via a discriminator, one or more calculation/calculations including distance/distances (e.g. in paragraphs 22, 31, and 47, “discriminator distinguishes between generated and real content… the discriminator neural network is considered a critic, which…provides an estimate of the distance between the probability distributions of the real training data and the generated data… online…where models continuously receive new streams of data and keep on learning, the whole system comprising the generator G 220, discriminator D1 240, discriminator D2 250 and the model which extracts key features may be part of the deployed system”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Notz to include the teachings of Cricri because one of ordinary skill in the art would have recognized the benefit of distinguishing between generated and real content and/or facilitating continuous learning.
but does not specifically teach training the AI module using real training data and ground truth data, training a second monitoring module using the output data of the Al module and false output data, wherein the false output data are generated by a generator of the second monitoring module; training a third monitoring module using ground truth data and false ground truth data, wherein the false ground truth data are generated by a generator of the third monitoring module, and (ii) feeding the output data stream to a discriminator of the trained second monitoring module, and determining, via the discriminator, one or more distances therefrom that are used for evaluating the output data stream of the Al module, and/or (iii) feeding the output data stream to a discriminator of the trained third monitoring module, and determining, via the discriminator, one or more distances therefrom that are used for evaluating the output data stream of the Al module.
However, the combination teaches determining, via a discriminator, one or more distances therefrom that are used for evaluating the output data stream of the Al module (e.g. Cricri, in paragraphs 20, 22, 31, and 47, “Artificial neural networks may be used for first extracting features… discriminator distinguishes between generated and real content… the discriminator neural network is considered a critic, which…provides an estimate of the distance between the probability distributions of the real training data and the generated data… online…where models continuously receive new streams of data and keep on learning, the whole system comprising the generator G 220, discriminator D1 240, discriminator D2 250 and the model which extracts key features may be part of the deployed system”) and Schiegg teaches training a second monitoring module using output data of an Al module and false output data, wherein the false output data are generated by a generator of the second monitoring module and feeding an output data stream to a discriminator of the trained second monitoring module and/or feeding the output data stream to a discriminator of a trained third monitoring module (e.g. in paragraphs 47 and 92, “optimization of an objective function 5, in dependence upon which, parameters of generator 41 and of discriminator 42 are adjusted” and figure 1 showing generated output signals 52 fed with false output data 43 generated by generator 41). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Schiegg because one of ordinary skill in the art would have recognized the benefit of allowing components to be appropriately adjusted and/or optimized,
but the combination does not specifically teach training the AI module using real training data and ground truth data and training a third monitoring module using ground truth data and false ground truth data, wherein the false ground truth data are generated by a generator of the third monitoring module.
However, Lim teaches training an AI module using real training data and ground truth data (e.g. in paragraph 46, “learning apparatus may train the detector 130 by a loss function 140 based on a similarity level between the real image and ground truth 2 of the real image”) and training a third monitoring module using ground truth data and false ground truth data, wherein the false ground truth data are generated by a generator of the third monitoring module (e.g. in paragraphs 16, 45 and 50, “generating a fake image of the first domain by decoding the latent variable in the first domain, generating a fake image of the second domain by decoding the latent variable in the second domain… ground truth information (GT) of the synthetic image [i.e. false ground truth] is also referred to as ground truth 1, and ground truth information of the real image is also referred to as ground truth 2… learning apparatus trains the detector 130 with the loss function 140 based on … the ground truth 1 … the ground truth 2”, and figure 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Lim because one of ordinary skill in the art would have recognized the benefit of facilitating updating of parameters for learning.
As per claim 16, the rejection of claim 15 is incorporated and the combination further teaches wherein generating false training data comprises using a respective background data source that is which are fed to any of the discriminators of the respective monitoring modules (e.g. Notz, in paragraph 17, “x comes from real data and z is generated from the encoder network with input x” and figure 2 showing feeding to discriminator; Schiegg, in paragraphs 60-61, “a random vector is ascertained in block 3 and transmitted to generator 4l”).
As per claim 17, the rejection of claim 15 is incorporated and the combination further teaches wherein during the training phase, training losses with respect to the respective training data are determined from each of the distances, and the training losses are used for training the generators and discriminators of the respective monitoring modules (e.g. Cricri, in paragraphs 22, 43, and 46, “discriminator distinguishes between generated and real content… the discriminator neural network is considered a critic, which…provides an estimate of the distance between the probability distributions of the real training data and the generated data… loss computed from an output of the discriminator D1 240… loss computed through the discriminator D1 240 and applied to train the discriminator… loss computed through the discriminator D1 240 and applied to train the generator G 220”; Schiegg, in paragraphs 47 and 92, “optimization of an objective function 5, in dependence upon which, parameters of generator 41 and of discriminator 42 are adjusted” and figure 1; Lim, in paragraphs 100-101 and 105, “learning apparatus 400 acquires the first loss 422… the first loss 422 may be an L1 distance between the synthetic image (image 1) 418 and the fake image (image 1′) 419 of the first domain… gradient of the first loss 422 to the fake image (image 1′) 419 of the first domain is back-propagated relative to a path through which the synthetic image (image 1) is propagated in FIG. 4, and used to update parameters”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Cheng et al (US 20190130212 A1) teaches “inference is encapsulated in a generative adversarial training process… discriminator aims to distinguish the real data distribution custom-character.sub.data(x) from the fake sample distribution custom-character.sub.g(z), where N(0, custom-character). An Earth-Mover distance metric can be used, such that the problem is expressed as” a distance equation between real data and false data (e.g. in paragraphs 18 and 25).
Rezagholizadeh et al. (US 20180336471 A1) teaches “outputting, from a generator network, generated samples derived from a random noise vector (block 302); inputting, to a discriminator network, the generated samples, a plurality of labelled training samples, and a plurality of unlabelled training samples (block 304); outputting, from the discriminator network, for at least the generated samples, a predicted continuous label for a generated sample and a predicted probability that the sample is a real sample or a fake sample” (e.g. in paragraph 84).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/W.W/Examiner, Art Unit 2144 04/03/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights