DETAILED ACTION
This final office action is responsive to the amendment filed on July 7, 2026. Claims 1-11, 13, and 16-19 are pending. Claim 1 is independent. Claims 12, and 14-15 are canceled. Claims 17-19 are added.
Objection of claim 9 is withdrawn in light of applicant’s amendment.
Claim rejections under 35 USC §103 are maintained for claims 1-11, 13, and 16. Rejection of claims 17-19 has been added in light of applicant’s amendment. See sections Claim Rejections – 35 USC §103 and Response to Arguments below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 11, 13, rejected under 35 U.S.C. 103 as being unpatentable over Bauer et al. (DE102006054425), hereinafter Bauer, in view of Junginger et al. (US20200236005), hereinafter Junginger.
Regarding claim 1, Bauer teaches:
A method for determining an impermissible deviation of a system behavior of a technical device from a normal value range using an artificial neural network comprising: (Bauer, paragraph 0020: “A further development of the method and the device provides that deviations of the estimated value of the model parameter from a given initial value are determined using the artificial neural network.” And paragraph 0030: “Furthermore, a method for influencing the driving state of a motor vehicle is provided, in which the driving state is influenced depending on a deviation between an actual value of a first driving state variable and a reference value of the first driving state variable.”)
in a learning phase supplying the artificial neural network with (i) first input data that was also provided to the technical device, and (ii) first output data generated by the technical device based on the first input data; (Bauer, paragraph 0007: “For this purpose, the artificial neural network is adapted using a learning procedure so that the estimated value of the model parameter approaches the actual value of the parameter in repeated calculations.” And paragraphs 0013-0014: “Further development of the method and the device includes the fact that the learning method is a supervised learning method. Supervised learning has the advantage that the training of the artificial neural network is very targeted and realistic estimates for the model parameters are obtained very quickly.” – Paragraph 0051 additionally discusses the input signals of the neural network being driving condition variables either measured or input by the driver of the vehicle, thus the input data is being provided to the technical device, as well as the output signals being parameters of the vehicle model of the vehicle dynamics control system which was generated by the technical device by the first input data. The neural network being a supervised learning method means that during the learning procedure it was first fed as input data and an output data which corresponds to the first input data and first output data.)
in a prediction phase following the learning phase (i) feeding only second input data to the artificial neural network, and (ii) calculating output reference data using the artificial neural network based on the second input data; (Bauer, paragraph 0045: “The calculation of the reference value Yref of the controlled variable Y is carried out in the reference value calculation device 104 on the basis of a reference model of the vehicle 101 using quantities E that specify the driving state of the vehicle 101 desired by the driver.” – Since this occurs after the training phase, calculating the reference value Y would be a prediction phase where the input data is being fed into the artificial neural network to be calculated.)
generating second output data using the technical device based on the second input data; (Bauer, paragraph 0044: “The current actual value Yist of the controlled variable Y is either measured directly using a sensor of the vehicle 101 or derived from the measured values of one or more sensors… The control deviation
∆
Y represents the input variable of a control device 102, which calculates an output signal depending on the control deviation.” – The current actual value of Y would correspond to the second output data that is generated using the technical device.)
identifying the impermissible deviation when a difference between the second output data and the output reference data is outside the normal value range; and (Bauer, paragraph 0044: “The control deviation
∆
Y = Yref – Yist is calculated from the difference between the actual value Yist and a reference value Yref of the controlled variable Y… The control device 102 is usually activated when the control deviation
∆
Y and, if applicable, other variables exceed predefined control entry thresholds.” – The predefined thresholds would indicate the normal value range, thus the variables exceeding this would indicate an impermissible deviation between the second output data and the output reference data.)
taking a countermeasure in connection with the technical device when the impermissible deviation is identified, (Bauer, paragraph 0044: “The control device 102 is usually activated when the control deviation
∆
Y and, if applicable, other variables exceed predefined control entry thresholds. The output signals of the control device 102 correspond to a position request, according to which at least one actuator 103 is controlled, with which the driving behavior of the vehicle 101 can be influenced.” – Activating the control device and influencing the driving behavior of the vehicle would be countermeasures taken in connection with the technical device.)
Bauer does not explicitly teach:
wherein the countermeasure includes at least one of (i) deactivating partial functions of the technical device, and (ii) using an alternative technical device.
However, Junginger teaches:
wherein the countermeasure includes at least one of (i) deactivating partial functions of the technical device, and (Junginger, paragraph 0058: “However, if this is the case (2500), it is decided that there is an anomaly, and error variable F is set to value “1,” which results in countermeasures being initiated (2600), for example, by motor vehicle 100 being switched to a safe mode. The method thus ends.” – Switching the vehicle to a safe mode indicates that some of the functions of the device have been deactivated which is in response to detecting an anomaly, i.e., an impermissible deviation.) (ii) using an alternative technical device. (Junginger, paragraph 0045: “If error signal F already contains specific information about the cause of the anomaly, control variable A may initiate a specific countermeasure, such as cutting off one of communication nodes 110, 120, 130 from the data communications traffic over the CAN bus.” – Page 7, lines 3-8 of the specification discusses the technical device in the vehicle. The communication nodes are in the vehicle, see e.g. Fig. 1, and therefore cutting off one of the communication nodes would switch communication to another node and is therefore analogous to using an alternative technical device.)
Junginger is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Bauer, which already teaches a method for determining an impermissible deviation of a system behavior and then taking a countermeasure but does not explicitly teach that the countermeasure includes deactivating partial functions of the device or using an alternative device, to include the teachings of Junginger which does teach that the countermeasure includes deactivating partial functions of the device or using an alternative device in order to prevent external attacks. (Junginger, paragraphs 0007-0010)
Regarding claim 11, Bauer and Junginger teach the method of claim 1, as cited above.
Bauer further teaches:
wherein a control unit in a vehicle is configured to carry out the method. (Bauer, paragraph 0046: “These parameters can be determined on a prototype of the vehicle 101 and stored in a non-volatile memory 105 of the vehicle dynamics control system, which is connected to the reference value calculation device 104.”)
Regarding claim 13, Bauer and Junginger teach the method of claim 1, as cited above.
Bauer further teaches:
wherein a non-transitory machine- readable storage medium is configured to store a computer program product that includes program code configured to carry out the method. (Bauer paragraph 0046: “These parameters can be determined on a prototype of the vehicle 101 and stored in a non-volatile memory 105 of the vehicle dynamics control system, which is connected to the reference value calculation device 104.”)
Regarding claim 16, Bauer and Junginger teach the method of claim 1, as cited above.
Bauer further teaches:
wherein the technical device is an electronic stability program ("ESP") module for a braking system of a vehicle. (Bauer, paragraph 0002: “Vehicle dynamics control systems, such as the well-known ESP (ESP: Electronic Stability Program), stabilize the vehicle by compensating for a control deviation between an actual value of a vehicle state variable, measured by a vehicle sensor, and a reference value of the driving state variable by influencing the driving behavior by means of an actuator.”)
Regarding claim 18, Bauer and Junginger teach the method of claim 1, as cited above.
Bauer further teaches:
the technical device generates the second output data as an output signal in response to an input signal comprising the second input data, and (Bauer, paragraph 0044: “The control deviation ΔY represents the input variable of a control device 102, which calculates an output signal depending on the control deviation.”)
the output reference data from the artificial neural network comprises a predicted value of the output signal of the technical device. (Bauer, paragraph 0007: “Advantageously, an artificial neural network is used to estimate the parameters of a vehicle reference model, which is trained to determine the parameters as realistically as possible.”)
Regarding claim 19, Bauer and Junginger teach the method of claim 16, as cited above.
Bauer further teaches:
the second input data comprises an electrical input current to a pump of the ESP module, (Bauer, paragraph 0002: “Vehicle dynamics control systems, such as the well-known ESP system (ESP: Electronic Stability Program), stabilize the vehicle by compensating for a control deviation between an actual value of a vehicle state variable, measured by a vehicle sensor, and a reference value of the driving state variable by influencing the driving behavior by means of an actuator. The reference value of the state variable is usually determined using a vehicle model. The vehicle models that can be used usually contain several parameters that need to be adapted to a specific vehicle type so that the model correctly reproduces the reference behavior of a specific vehicle.”)
the second output data comprises a hydraulic brake pressure generated by the pump in response to the electrical input current, and (Bauer, paragraph 0044: “The actuator 103 could, for example, be a brake actuator known to experts, with which wheel-specific brake pressures can be built up in the wheel brakes of the vehicle 101.”)
the output reference data calculated by the artificial neural network comprises a predicted hydraulic brake pressure corresponding to the electrical input current. (Bauer, paragraph 0046: “The reference vehicle model describes the behavior of vehicle 101 based on parameters that must be adapted to this vehicle 101.”)
Claims 2, 3, and 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over Bauer in view of Junginger in view of Wen et al. (Time Series Anomaly Detection Using Convolutional Neural networks), hereinafter Wen.
Regarding claim 2, Bauer and Junginger teach the method of claim 1, as cited above.
Bauer further teaches:
the artificial neural network is divided into a base network and a head network, (Bauer, paragraph 0050: “The topology of the KNN 500 provides for several layers of neurons 201, in particular an input layer 501 and an output layer 502.” – The output layer is analogous to the head network while the input and hidden layers are analogous to the base network.)
…trained on a first technical device, and (Bauer, paragraph 0010 and 0011: “Since all parameters of the further vehicle model must be determined using the artificial neural network in order to carry out the learning process, it is advantageous if the further vehicle model has as little parameter excess as possible compared to the reference vehicle model. Therefore, a further embodiment of the method and the device provides that the reference vehicle model and other vehicle model are identical.” – Training on a reference vehicle and a further vehicle indicates that the method is capable of training on a first vehicle therefore the training is being done on a first technical device.)
…trained on a second technical device which is identical to the first technical device. (Bauer, paragraphs 0010 and 0011: “Since all parameters of the further vehicle model must be determined using the artificial neural network in order to carry out the learning process, it is advantageous if the further vehicle model has as little parameter excess as possible compared to the reference vehicle model. Therefore, a further embodiment of the method and the device provides that the reference vehicle model and other vehicle model are identical.” – Training on a reference vehicle and a further vehicle indicates that the method is capable of training on a second vehicle therefore the training is being done on a second technical device which is stated to be identical to the first.)
Bauer and Junginger do not explicitly teach:
in a first section of the learning phase both the base network and the head network are trained… (Wen, section 4, paragraph 1: “The transfer learning approach uses the weights from a base model pretrained on an available large-scale data set and fine-tunes the model weights with a small-scale data set related to the task of interest.” And section 4.1: “We found two fine-tuning strategies with good performance in our tests. The first one is to set up different learning rate multipliers in 10 sections (5 encoding sections, 4 decoding sections, and the output section) as 0.01, 0.04, 0.09, …, 0.81, 1.0)” – The pretraining is analogous to the first section of a learning phase while the neural network is being divided up into sections is analogous to the base network and head network with the encoding sections being the base and the other sections being the head.)
in a second section of the learning phase only the head network is trained… (Wen, section 4.2, paragraph 3: “During fine-tuning, we freeze the first four encoding sections and tune the weights of the remaining layers.” – The four encoding layers is analogous to the base network, as well as the input layer of Bauer, while the “remaining layers” is analogous to the output layer of Bauer and the head network.)
Wen is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Bauer and Junginger, which already teaches a method of training and using an artificial neural network to detect deviations of system behaviors that has a base network and a head network but does not explicitly teach that the training is done in two sections where the first section trains both the base network and head network while the second section trains only the head network, to include the teachings of Wen which does teach that the training is done in two sections where the first section trains both the base network and head network while the second section trains only the head network in order to "resolve the data disparity issue." (Wen, section 1, paragraph 4)
Regarding claim 3, Bauer, Junginger, and Wen teach the method of claim 2, as cited above.
Bauer further teaches:
wherein in the prediction phase both the base network and the head network are used to determine the impermissible deviation of the second technical device. (Bauer, paragraph 0011-0012: “Therefore, a further embodiment of the method and the device provides that the reference vehicle model and the other vehicle model are identical. This avoids the need to determine parameters of the further vehicle model with the artificial neural network that are not included in the reference model, thus minimizing the number of parameters to be determined by the artificial neural network.” – The vehicles being identical and the further vehicle model being determined by the same artificial neural network indicates that the prediction phase of both the base network and head network, as taught by Bauer above, are used to determine the impermissible deviation of the second technical device.)
Regarding claim 5, Bauer, Junginger, and Wen teach the method of claim 2, as cited above.
Bauer further teaches:
wherein an output of the base network is used as an input for the head network. (Bauer, paragraph 0050: “The neurons 201 of input layer 501 receives the input signals I of the KNN 500 and forward them to output layer 502.” – As noted above, the input layer is analogous to the base network and the output layer is analogous to the head network. Thus, the input layer forwarding signals to the output layer is analogous to the output of the base network being used as input for the head network.)
Regarding claim 6, Bauer, Junginger, and Wen teach the method of claim 2, as cited above.
Bauer further teaches:
wherein measured values of the second technical device are fed to the head network as an input. (Bauer, paragraph 0011-0012: “Therefore, a further embodiment of the method and the device provides that the reference vehicle model and the other vehicle model are identical. This avoids the need to determine parameters of the further vehicle model with the artificial neural network that are not included in the reference model, thus minimizing the number of parameters to be determined by the artificial neural network.” And paragraph 0051: “The input signals I of the KNN 500, which is used to determine the estimated values of the model parameters, are driving condition variables that are measured using sensors of the vehicle 101 or determined from the measured values of vehicle sensors, and/or one or more of the quantities E that are set by the driver of the vehicle 101.” – Since this could be used with a second vehicle, this is analogous to the second technical device. The driving condition variables which are used in the neural network come from the vehicle. Thus, this is analogous to the measured values of the second device being fed into the head network as an input.)
Regarding claim 7, Bauer, Junginger, and Wen teach the method of claim 2, as cited above.
Bauer does not explicitly teach:
wherein information about a type or a class of the second input data is fed to the head network as an input.
However, Junginger further teaches:
wherein information about a type or a class of the second input data is fed to the head network as an input. (Junginger, paragraph 0064: “Thus, by selecting switch position A, B, input variable x is labeled depending on whether input variable x is a normal datum n or an artificial datum f. Therefore, it is self-evidently also possible to feed mixed batches, which contain both normal data n as well as artificial variables f, to the discriminator.” – The input variable being labeled depending on whether it is a normal datum or artificial datum is analogous to information about a type or a class of the second input data.)
Regarding claim 8, Bauer, Junginger, and Wen teach the method of claim 2, as cited above.
Bauer and Junginger do not explicitly teach:
wherein the base network is included in a plurality of base networks to which different first input data are fed.
However, Wen further teaches:
wherein the base network is included in a plurality of base networks to which different first input data are fed. (Wen, section 4.2, paragraph 2: “However, the C channels of input series are separated by a slicing layer first, and then every channel has its own univariate encoding section, like the first four encoding sections in U-Net.” – This is analogous to a plurality of base networks, each with an input series being analogous to the plurality of base networks to which different input data are fed.)
Regarding claim 9, Bauer, Junginger, and Wen teach the method of claim 8, as cited above.
Bauer and Junginger do not explicitly teach:
wherein an output of each base network of the plurality of base networks is fed to a common head network.
However, Wen further teaches:
wherein an output of each base network of the plurality of base networks is fed to a common head network. (Wen, section 4.2, paragraph 2: “The outputs from the fourth encoding section over all channels will be concatenated before the integrated fifth encoding section, followed by four decoding sections and a final output section, the same as U-Net. We have nicknamed this architecture MU-Net (multivariate U-Net).” – The fifth encoding section is analogous to the head network which receives the output from the previous channels.)
Claims 4 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Bauer in view of Junginger in view of Wen in view of Lee et al. (Training Deep Spiking Neural Networks Using Backpropagation), hereinafter Lee.
Regarding claim 4, Bauer, Junginger, and Wen teach the method of claim 2, as cited above.
Bauer, Junginger, and Wen do not explicitly teach:
wherein a number of neurons of the head network is smaller than a number of neurons of the base network by at least a factor of five or ten.
However, Lee teaches:
wherein a number of neurons of the head network is smaller than a number of neurons of the base network by at least a factor of five or ten. (Lee, section 3.2: “The convolutional layers produce 20 and 50 feature maps, respectively, with kernels of size 5 x 5. The output of the second pooling layer is connected to a fully connected hidden layer with 200 neurons followed by the output layer with 10 class neurons.” – The convolutional and hidden layers are analogous to the base network while the output layer is analogous to the head network and 10 neurons is more than 10 times smaller than 200 neurons.)
Lee is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Bauer, Junginger, and Wen, which already teaches training a neural network that is divided into a base network and a head network but does not explicitly teach that the number of neurons of the head network is smaller than the number of neurons in the base network by at least a factor of five or ten, to include the teachings of Lee which does teach that the number of neurons of the head network is smaller than the number of neurons in the base network by at least a factor of five or ten in order to “achieve very competitive levels of accuracy.” (Lee, section 3.2)
Regarding claim 10, Bauer, Junginger, and Wen teach the method of claim 2, as cited above.
Bauer, Junginger, and Wen do not explicitly teach:
wherein the second input data is subjected to pre-processing before the calculating takes place in the artificial neural network.
However, Lee teaches:
wherein the second input data is subjected to pre-processing before the calculating takes place in the artificial neural network. (Lee, page 9, Table 3 – The last row represents the CNN presented in this paper and indicates that there was preprocessing done on the input data prior to using in the neural network.)
Lee is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Bauer, Junginger, and Wen, which already teaches training a neural network to identify an impermissible deviation in a technical device using input data but does not explicitly teach that the input data is pre-processed before being fed into the neural network, to include the teachings of Lee which does teach that the input data is pre-processed before being fed into the neural network in order to "further improve the accuracy." (Lee, page 9, column 1)
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Bauer in view of Junginger in view of Yun et al. (US20120245791), hereinafter Yun.
Regarding claim 17, Bauer and Junginger teach the method of claim 1, as cited above.
Bauer further teaches:
the output reference data calculated by the artificial neural network in the prediction phase comprises a predicted value of an output of the technical device for the second input data; and (Bauer, paragraph 0051: “The output signals O of the KNN 500 comprise the estimated values of the model parameters of the reference vehicle model, which is used within the reference value calculation device 104 of the vehicle dynamics control system of the vehicle 101 to determine the reference values Yref of the controlled variable Y.”)
Bauer and Junginger do not explicitly teach:
the impermissible deviation is indicative of a deterioration or a failure of the technical device.
However, Yun teaches:
the impermissible deviation is indicative of a deterioration or a failure of the technical device. (Yun, paragraph 0042: “The data normalization unit 110 determines that a state in question is normal if corresponding vehicle network data falls between a minimum threshold value and a maximum threshold value during normalization transformation, and determines that the state is an abnormal state (problematic state) if the value does not fall within the threshold range.”)
Yun is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning and vehicle control. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Bauer and Junginger, which already teaches identifying an impermissible deviation of a technical device and taking a countermeasure but does not explicitly teach that the impermissible deviation is indicative of a deterioration or failure of the technical device, to include the teachings of Yun which does teach that the impermissible deviation is indicative of a deterioration or failure of the technical device in order to improve the safety of the vehicle. (Yun, paragraph 0007)
Response to Arguments
Applicant's arguments filed July 7, 2026 have been fully considered but they are not persuasive. On page 9 applicant argues that Bauer does not disclose a comparing the output of the ANN against any output of the technical devices. Examiner disagrees. Paragraph 0007, as cited above, states that “an artificial neural network is used to estimate the parameters of a vehicle reference model, which is trained to determine the parameters as realistically as possible.” Further paragraph 0030 states “in which the driving state is influenced depending on a deviation between an actual value of a first driving state variable and a reference value of the first driving state variable.” Thus, the reference value, which is the output of the ANN is compared with the actual value, which is the output of the technical device.
On page 10 applicant states that “the Office action cited paragraphs [0007] and [0051] of Bauer and alleged that the driver-specified quantities E and the measured values xi supplied to the ANN 500 correspond to the first input data and the first output data, respectively.” However, this is not what was stated in the office action. Paragraph 0007 states ““For this purpose, the artificial neural network is adapted using a learning procedure so that the estimated value of the model parameter approaches the actual value of the parameter in repeated calculations.” And paragraph 0051 states “The input signals I of the KNN 500, which is used to determine the estimated values of the model parameters, are driving condition variables that are measured using sensors of the vehicle 101 or determined from the measured values of vehicle sensors, and/or one or more of the quantities E that are set by the driver of the vehicle 101.” Thus, the input data of the neural network is either the measure data or driver inputs and would correspond to the first input data being provided by the technical device (the driver input to the vehicle would also be provided from the device to the neural network). The output then being the model parameter, which is the vehicle model parameter and is thus the output data generated by the technical device.
On page 11 applicant argues that reference value of Bauer does not correspond with the reference data of the prediction phase of the claim. Examiner disagrees. Applicant further states that device 104 of Bauer is not an artificial neural network and further cites the beginning of paragraph 0051 as support. However, that sentence of paragraph 0051 fully states “The output signals O of the KNN 500 comprise the estimated values of the model parameters of the reference vehicle model, which is used within the reference value calculation device 104 of the vehicle dynamics control system of the vehicle 101 to determine the reference values Yref of the controlled variable Y.” Thus, the cited reference for the prediction phase of Bauer is using a neural network to determine the reference value output.
Regarding applicants arguments on page 12 that Bauer does not teach impermissible deviation, examiner disagrees. As noted above, Yref is produced by the ANN and therefore represents the reference data while Yist is the output data. Thus, the data is of the same type and the difference is able to be formed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bojarski et al. (Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car)
Pfieffer et al. (A Fleet-Based Machine Learning Approach for Automatic Detection of Deviations between Measurements and Reality)
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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/J.C.M./Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
