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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE 10 2021 206 943.6, filed on July 1st 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/03/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Pg., 7 of the Specification, lines 10 to 15 contain hyperlinks with respect to the stated simulation tools; these hyperlinks must be deleted.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
the context detecting apparatus is configured to in claim 19;
the reconfiguration apparatus is configured to in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 11-13 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 11 and 19 recite the following claim limitations:
adapting the configuration in accordance with the estimated configuration
context detecting apparatus; and a reconfiguration apparatus
wherein the context detecting apparatus is configured to detect and/or to obtain at least one item of contextual information of a current context in which the vehicle is operated;
and to adapt the configuration in accordance with the estimated configuration.
These above claim limitations as drafted comply with step 1 due to them being either process claims or articles of manufacture, that under the BRI can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the independent claims recite a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong Two because they recite the following claim limitations:
a plurality of application entities and a plurality of computing nodes, wherein the application entities are executed in a distributed manner across the computing nodes in accordance with a configuration, wherein sensor data detected by at least one sensor is supplied to at least some of the application entities. This claim limitation is recited at a high-level of generality using generic computer components (i.e., using a generic node, and sensors to perform generic computer functions such as executing applications) such that it does not amount to a particular machine.
the application entities generate and provide control signals for controlling the vehicle. This claim limitations recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to how the application generates the control signals for controlling the vehicle.
obtaining at least one item of contextual information of a current context, in which context the vehicle is operated. This claim limitation amounts to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., inputting contextual data of the vehicle).
supplying the at least one item of contextual information to a trained machine learning method. This claim limitation amounts to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., outputting contextual data of the vehicle into a trained machine learning method).
estimating, by the trained machine learning method, a configuration based on the at least one item of contextual information. This claim limitation recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model and/or configuration used and the training and/or finetuning steps used by the model on the data to generate an estimated configuration.
and wherein the reconfiguration apparatus is configured to provide a trained machine learning method, to supply the at least one item of contextual information that is detected and/or obtained to the trained machine learning method and to have the trained machine learning method estimate a configuration based on the at least one item of contextual information. This claim limitation recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model and/or configuration used and the training and/or finetuning steps used by the model on the data to generate an estimated configuration.
The claims do not include additional elements that are sufficient to amount to significantly more under Step 2B than the judicial exception because as discussed above:
a plurality of application entities and a plurality of computing nodes, wherein the application entities are executed in a distributed manner across the computing nodes in accordance with a configuration, wherein sensor data detected by at least one sensor is supplied to at least some of the application entities. This claim limitation is recited at a high-level of generality using generic computer components (i.e., using a generic node, and sensors to perform generic computer functions such as executing applications) such that it does not amount to a particular machine.
the application entities generate and provide control signals for controlling the vehicle. This claim limitation recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
obtaining at least one item of contextual information of a current context, in which context the vehicle is operated. This claim limitation is well-understood, routine, conventional activity that court decisions, such as Symantec and buySAFE cited in MPEP 2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network using a generic computer are well-understood, routine, and conventional functions when claimed in a merely generic manner (as it is here).
supplying the at least one item of contextual information to a trained machine learning method. This claim limitation is well-understood, routine, conventional activity that court decisions, such as Symantec and buySAFE cited in MPEP 2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network using a generic computer are well-understood, routine, and conventional functions when claimed in a merely generic manner (as it is here).
estimating, by the trained machine learning method, a configuration based on the at least one item of contextual information. This claim limitation recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
and wherein the reconfiguration apparatus is configured to provide a trained machine learning method, to supply the at least one item of contextual information that is detected and/or obtained to the trained machine learning method and to have the trained machine learning method estimate a configuration based on the at least one item of contextual information. This claim limitation recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Dependent claims 12-13 and 20 are directed to statutory subject matter under Step 1, but when viewed under the Broadest Reasonable Interpretation, do not contain additional claim limitations that transform the judicial exception into a practical application under Step 2A, Prong Two because the additional claim limitations of wherein the training data set generated in the field is transmitted to a central server amounts to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data; and a vehicle is recited at a high-level of generality. Furthermore, these additional elements do not amount to significantly more than the judicial exception under Step 2B because the additional claim elements of wherein the training data set generated in the field is transmitted to a central server are well-understood, routine, conventional activity that court decisions, such as Symantec and buySAFE cited in MPEP 2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network using a generic computer are well-understood, routine, and conventional functions when claimed in a merely generic manner (as it is here) and a vehicle is recited at a high-level of generality not amounting to a particular machine.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 11-26 are rejected under 35 U.S.C. 103 as being unpatentable over Kain et al., "Towards a reliable and context-based system architecture for autonomous vehicles." 2nd International Workshop on Autonomous Systems Design (ASD 2020) 2020 April (“Kain”) in view of Wang et al., A digital twin paradigm: Vehicle-to-cloud based advanced driver assistance systems. In 2020 IEEE 91st vehicular technology conference (VTC2020-Spring) 2020 May 25(“Wang”)
Regarding claim 11, Kain teaches a method for reconfiguring a system architecture of an autonomous vehicle, wherein the system architecture comprises
a plurality of application entities and a plurality of computing nodes, wherein the application entities are executed in a distributed manner across the computing nodes in accordance with a configuration (Kain, pgs., 2-3, see also fig. 1, “The reconfiguration measures determined by the reconfiguration layer are then executed by the architecture layer. This layer is responsible for distributing application instances among the available computing nodes as instructed by the layer situated above, whereby a minimum level of safety has to be preserved.”),
wherein sensor data detected by at least one sensor is supplied to at least some of the application entities, and wherein at least some of the application entities generate and provide control signals for controlling the vehicle(Kain, pgs., 3-4, see also fig. 2, “From these context observations, a set of required software applications can be implied. This set may, for example, include applications for detecting pedestrians, planning trajectories taking the rough weather conditions into account, as well as entertainment applications that are included in the ride... the context observations, we can imply the safety-criticality of the respective applications...[t]he former task, extracting context observations, necessitates perceiving environment parameters. These parameters are, for example, determined by sensors the car is equipped with, communicating with backend services, and interacting with the passengers.”)
Kain does not teach: the method comprises: obtaining at least one item of contextual information of a current context, in which context the vehicle is operated; supplying the at least one item of contextual information to a trained machine learning method; estimating, by the trained machine learning method, a configuration based on the at least one item of contextual information; and adapting the configuration in accordance with the estimated configuration.
However, Wang teaches:
the method comprises: obtaining at least one item of contextual information of a current context, in which context the vehicle is operated(Wang, pgs., 3-5, see also figs., 2-3, “Human behavior model: This module predicts the speed tracking error generated by the driver, and compensates for the raw advisory speed in real time. The output of this model is the advisory speed sent to the physical world, which already considers the speed error generated by the driver.”);
supplying the at least one item of contextual information to a trained machine learning method(Wang, pgs., 3-5, see also figs., 2-3, “Due to the presence of speed tracking error generated by the human driver at each time step, the human behavior model trains a nonlinear autoregressive (NAR) neural network to predict the error at the next time step.”);
estimating, by the trained machine learning method, a configuration based on the at least one item of contextual information; and adapting the configuration in accordance with the estimated configuration(Wang, pgs., 3-5, see also figs., 2-3, “Due to the presence of speed tracking error generated by the human driver at each time step, the human behavior model trains a nonlinear autoregressive (NAR) neural network to predict the error at the next time step... [t]he raw advisory speed can then be compensated by this predicted error...where
v
^
i
(
t
+
δ
t
)
is the advisory speed being sent to the physical world and displayed on the DVI device.”).
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 Kain with the teachings of Wang the motivation to do so would be to create a digital-twin environment to simulate the random events of the real world to develop an advanced driver assistance system for a car(Wang, pg., 1, “A digital twin is a digital replica in cyber world of an entity in physical world. Digital twin technology opens the way to real-time monitoring and synchronization of the real-world activities with the virtual counterparts thanks to the physical-cyber connection and the Cyber-Physical Systems (CPS) elements... we propose a digital twin framework for connected vehicles using vehicle-to-cloud (V2C) communication. A paradigm of digital twin is developed for an advanced driver assistance system (ADAS), where the advisory speed calculated by the cloud server is shown on the vehicle on-board driver vehicle interface (DVI) device, so the driver can control the vehicle in a more intelligent manner.”).
Regarding claim 12, Kain in view of Wang teaches the method of claim 11,
wherein at least one key performance indicator is determined during the application of the adapted configuration and is stored associated with the adapted configuration(Wang, pgs., 3-4, see also fig. 2, “DVI device: This module shows the advisory information (via V2C communication) to the driver to conduct cooperative/intelligent maneuvers. The information displayed on the DVI (an example is shown in Fig. 2) may include current speed (the left number), advisory speed (the right number)... [t]he performance evaluation module analyzes the data in real time. The speed, acceleration, energy consumption, criteria pollutant emissions and many other performance indices of interest can be analyzed on the cloud server, and their results can be...displayed on the DVI[wherein at least one key performance indicator is determined during the application of the adapted configuration and is stored associated with the adapted configuration].”),
wherein training data is generated from the at least one item of contextual information that is detected and/or obtained, the adapted configuration and the determined at least one key performance indicator, and wherein a training data set compiled from such training data is provided as the training data set generated in the field(Wang, pgs., 3-4, see also figs. 2 and 3, “[T]he ego vehicle i retrieves information of its (virtual) leading vehicle j (j = i − 1) from the cloud server[wherein training data is generated from], including its length
l
j
, longitudinal speed
v
j
and distance to merge
r
j
. Then, the proposed consensus algorithm takes those inputs as well as the vehicle dynamics data from the ego vehicle i, and computes a reference acceleration
a
r
e
f
for the ego vehicle i by ...[algorithm (1)]... [s]pecifically, the value of the control gain
γ
i
is set based on a lookup table, which is built offline based on safety, efficiency, and comfort constraints[and the determined at least one key performance indicator,]... the human behavior model trains a nonlinear autoregressive (NAR) neural network to predict the error at the next time step[the at least one item of contextual information that is detected and/or obtained]... [t]he raw advisory speed can then be compensated by this predicted error as: [equation 4][ the adapted configuration] where
v
^
i
(
t
+
δ
t
)
is the advisory speed being sent to the physical world and displayed on the DVI device[[and wherein a training data set compiled from such training data is provided as the training data set generated in the field].”).1
Regarding claim 13, Kain in view of Wang teaches the method of claim 12, wherein the training data set generated in the field is transmitted to a central server(Wang, pgs., 3-4, see also figs. 2 and 3, “For the map matching module, a pre-built map of the testing field is available on the cloud server... [f]or position synchronization, vehicles’ coordinates (i.e., longitude, latitude, and altitude) received from the GNSS[wherein the training data set generated in the field] can be matched to the pre-built map by the proposed map matching algorithm to update their current position in the cyber world[is transmitted to a central server]....”).2
Regarding claim 14, Kain in view of Wang teaches a method for training a machine learning method for application in the method of claim 11, comprising:
generating training data based on a simulation in which the vehicle and the vehicle surroundings are realistically simulated(Wang, pgs., 1-4, see also figs.1, 2 and 3, “Located at the heart of the cyber world, the knowledge base is built on top of historical information and keeps updated as new information flows in. The knowledge can be drawn to perform predictive analyses (with the combination of modeling/simulation tools) and find optimal strategies to support decision making processes[generating training data based on a simulation in which the vehicle and the vehicle surroundings are realistically simulated].”);
generating contextual information to this end, wherein a configuration is generated in each case for the generated contextual information(Wang, pgs., 1-4, see also figs.1, 2 and 3, “Human behavior model: This module predicts the speed tracking error generated by the driver, and compensates for the raw advisory speed in real time. The output of this model is the advisory speed sent to the physical world, which already considers the speed error generated by the driver.”);
determining, within the framework of the simulation in which the generated configuration is used, at least one key performance indicator for assessing the generated configuration(Wang, pgs., 1-4, see also figs.1, 2 and 3, “It receives advisory speed from the human behavior model, and also the position and vehicle speed from the map matching module...[p]erformance evaluation: The performance evaluation module analyzes the data in real time. The speed, acceleration,energy consumption, criteria pollutant emissions and many other performance indices of interest can be analyzed on the cloud server, and their results can be sent back to the physical world or displayed on the DVI.”);
wherein the contextual information is used as input data of the machine learning method for training the machine learning method; wherein the associated generated configuration and the associated determined at least one key performance indicator are used as the basic truth during the training; wherein a training data set is generated from such training data; and wherein the machine learning method is trained with the generated training data set(Wang, pgs., 3-4, see also figs. 2 and 3, “[T]he ego vehicle i retrieves information of its (virtual) leading vehicle j (j = i − 1) from the cloud server, including its length
l
j
, longitudinal speed
v
j
and distance to merge
r
j
. Then, the proposed consensus algorithm takes those inputs as well as the vehicle dynamics data from the ego vehicle i, and computes a reference acceleration
a
r
e
f
for the ego vehicle i by ...[algorithm (1)]... [s]pecifically, the value of the control gain
γ
i
is set based on a lookup table, which is built offline based on safety, efficiency, and comfort constraints... the human behavior model trains a nonlinear autoregressive (NAR) neural network to predict the error at the next time step... [t]he raw advisory speed can then be compensated by this predicted error as: [equation 4] where
v
^
i
(
t
+
δ
t
)
is the advisory speed being sent to the physical world and displayed on the DVI device.”).3
Regarding claim 15, Kain in view of Wang teaches the method of claim 14, wherein the trained machine learning method is downloaded into a memory of a reconfiguration apparatus of at least one vehicle(Wang, pgs., 3-4, see also figs. 2 and 3, “[T]he human behavior model trains a nonlinear autoregressive (NAR) neural network to predict the error at the next time step... [t]he raw advisory speed can then be compensated by this predicted error as: [equation 4] where
v
^
i
(
t
+
δ
t
)
is the advisory speed being sent to the physical world and displayed on the DVI device[wherein the trained machine learning method is downloaded into a memory of a reconfiguration apparatus of at least one vehicle].”).
Regarding claim 16, Kain in view of Wang teaches the method of claim 14, wherein at least one training data set generated in the field is obtained and is added to the training data set(Wang, pgs., 3-4, see also figs. 2 and 3, “Fig. 4 shows the visualization of the digital twin in the cyber world, where digital replicas of all three vehicles are moving in the virtual map with real-time position updates from the physical world. The advisory speeds calculated in the cyber world are also shown here, and they are sent to all three vehicles in the physical world through V2C communication”).
Regarding claim 17, Kain in view of Wang teaches the method of claim 14, wherein the training data set is only generated from such training data and/or only such training data is added to the training data set, which training data has at least one key performance indicator that meets at least one predefined selection criterion(Wang, pgs., 4-5, see also figs., 7 and 8, “Performance evaluation: The performance evaluation module analyzes the data in real time. The speed, acceleration, energy consumption, criteria pollutant emissions and many other performance indices of interest can be analyzed on the cloud server, and their results can be sent back to the physical world or displayed on the DVI... [t]he fuel and emissions results of the same trip are analyzed by the performance evaluation in the cyber world as well, which inform the driver regarding his/her environmental impact in real time. As shown in Fig. 8, the ramp vehicle is shown to consume more fuel and produce more pollutant emissions than the two mainline vehicles due to its drastic speed changes[wherein the training data set is only generated from such training data and/or only such training data is added to the training data set, which training data has at least one key performance indicator that meets at least one predefined selection criterion].”).4,5
Regarding claim 18, Kain in view of Wang teaches the method of claim 14, wherein the simulation and training are performed on a central server(Wang, pgs., 3-4, see also figs., 1 and 2, “All computations of this V2C based ADAS are conducted on the cloud server, where digital replicas of the physical entities (e.g., vehicles, drivers, roadways) and the relevant functional modules are created in the cyber world[wherein the simulation]... [a]ll the system algorithms and models arerunning on a Dell R630 server located at the server room of University of California, Riverside[and training are performed on a central server]”).6
Regarding claim 19, Kain teaches a device for reconfiguring a system architecture of an autonomous vehicle, wherein the system architecture comprises
a plurality of application entities and a plurality of computing nodes, wherein the application entities are executed in a distributed manner across the computing nodes in accordance with a configuration(Kain, pgs., 2-3, see also fig. 1, “The reconfiguration measures determined by the reconfiguration layer are then executed by the architecture layer. This layer is responsible for distributing application instances among the available computing nodes as instructed by the layer situated above, whereby a minimum level of safety has to be preserved.”),
wherein sensor data detected by at least one sensor is supplied to at least some of the application entities, and wherein at least some of the application entities generate and provide control signals for controlling the vehicle(Kain, pgs., 3-4, see also fig. 2, “From these context observations, a set of required software applications can be implied. This set may, for example, include applications for detecting pedestrians, planning trajectories taking the rough weather conditions into account, as well as entertainment applications that are included in the ride... the context observations, we can imply the safety-criticality of the respective applications...[t]he former task, extracting context observations, necessitates perceiving environment parameters. These parameters are, for example, determined by sensors the car is equipped with, communicating with backend services, and interacting with the passengers.”).
Kain does not teach: the device having: a context detecting apparatus; and a reconfiguration apparatus; wherein the context detecting apparatus is configured to detect and/or to obtain at least one item of contextual information of a current context in which the vehicle is operated; and wherein the reconfiguration apparatus is configured to provide a trained machine learning method, to supply the at least one item of contextual information that is detected and/or obtained to the trained machine learning method and to have the trained machine learning method estimate a configuration based on the at least one item of contextual information, and to adapt the configuration in accordance with the estimated configuration.
However, Wang teaches:
the device having: a context detecting apparatus(Wang, pgs., 3-5, see also figs., 2-3, “Human behavior model: This module predicts the speed tracking error generated by the driver, and compensates for the raw advisory speed in real time. The output of this model is the advisory speed sent to the physical world, which already considers the speed error generated by the driver.”);
and a reconfiguration apparatus(Wang, pgs., 3-5, see also figs., 2-3, “Due to the presence of speed tracking error generated by the human driver at each time step, the human behavior model trains a nonlinear autoregressive (NAR) neural network to predict the error at the next time step... [t]he raw advisory speed can then be compensated by this predicted error...where
v
^
i
(
t
+
δ
t
)
is the advisory speed being sent to the physical world and displayed on the DVI device.”);
wherein the context detecting apparatus is configured to detect and/or to obtain at least one item of contextual information of a current context in which the vehicle is operated(Wang, pgs., 3-5, see also figs., 2-3, “Human behavior model: This module predicts the speed tracking error generated by the driver, and compensates for the raw advisory speed in real time. The output of this model is the advisory speed sent to the physical world, which already considers the speed error generated by the driver.”);
and wherein the reconfiguration apparatus is configured to provide a trained machine learning method, to supply the at least one item of contextual information that is detected and/or obtained to the trained machine learning method and to have the trained machine learning method estimate a configuration based on the at least one item of contextual information, and to adapt the configuration in accordance with the estimated configuration (Wang, pgs., 3-5, see also figs., 2-3, “Due to the presence of speed tracking error generated by the human driver at each time step, the human behavior model trains a nonlinear autoregressive (NAR) neural network to predict the error at the next time step... [t]he raw advisory speed can then be compensated by this predicted error...where
v
^
i
(
t
+
δ
t
)
is the advisory speed being sent to the physical world and displayed on the DVI device.”).
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 Kain with the teachings of Wang the motivation to do so would be to create a digital-twin environment to simulate the random events of the real world to develop an advanced driver assistance system for a car(Wang, pg., 1, “A digital twin is a digital replica in cyber world of an entity in physical world. Digital twin technology opens the way to real-time monitoring and synchronization of the real-world activities with the virtual counterparts thanks to the physical-cyber connection and the Cyber-Physical Systems (CPS) elements... we propose a digital twin framework for connected vehicles using vehicle-to-cloud (V2C) communication. A paradigm of digital twin is developed for an advanced driver assistance system (ADAS), where the advisory speed calculated by the cloud server is shown on the vehicle on-board driver vehicle interface (DVI) device, so the driver can control the vehicle in a more intelligent manner.”).
Regarding claim 20, Kain teaches a vehicle(Kain, pg., 3, “In the first use case, depicted in Figure 2a, the passenger of an autonomous taxi [a vehicle] booked a premium ride. Furthermore, we assume that the vehicle is currently driving in snowy weather on a highway.”) and for all other claim limitations of claim 20 they are rejected on the same basis as
independent claim 19 since they are analogous claims.
Regarding claim 21, Kain in view of Wang teaches the method of claim 15, wherein at least one training data set generated in the field is obtained and is added to the training data set(Wang, pgs., 3-4, see also figs. 2 and 3, “Fig. 4 shows the visualization of the digital twin in the cyber world, where digital replicas of all three vehicles are moving in the virtual map with real-time position updates from the physical world. The advisory speeds calculated in the cyber world are also shown here, and they are sent to all three vehicles in the physical world through V2C communication”).7
Regarding claim 22, Kain in view of Wang teaches the method of claim 15, wherein the training data set is only generated from such training data and/or only such training data is added to the training data set, which training data has at least one key performance indicator that meets at least one predefined selection criterion(Wang, pgs., 4-5, see also figs., 7 and 8, “Performance evaluation: The performance evaluation module analyzes the data in real time. The speed, acceleration, energy consumption, criteria pollutant emissions and many other performance indices of interest can be analyzed on the cloud server, and their results can be sent back to the physical world or displayed on the DVI... [t]he fuel and emissions results of the same trip are analyzed by the performance evaluation in the cyber world as well, which inform the driver regarding his/her environmental impact in real time. As shown in Fig. 8, the ramp vehicle is shown to consume more fuel and produce more pollutant emissions than the two mainline vehicles due to its drastic speed changes.”).8,9
Regarding claim 23, Kain in view of Wang teaches the method of claim 16, wherein the training data set is only generated from such training data and/or only such training data is added to the training data set, which training data has at least one key performance indicator that meets at least one predefined selection criterion(Wang, pgs., 4-5, see also figs., 7 and 8, “Performance evaluation: The performance evaluation module analyzes the data in real time. The speed, acceleration, energy consumption, criteria pollutant emissions and many other performance indices of interest can be analyzed on the cloud server, and their results can be sent back to the physical world or displayed on the DVI... [t]he fuel and emissions results of the same trip are analyzed by the performance evaluation in the cyber world as well, which inform the driver regarding his/her environmental impact in real time. As shown in Fig. 8, the ramp vehicle is shown to consume more fuel and produce more pollutant emissions than the two mainline vehicles due to its drastic speed changes.”).10,11
Regarding claim 24, Kain in view of Wang teaches the method of claim 15, wherein the simulation and training are performed on a central server (Wang, pgs., 3-4, see also figs., 1 and 2, “All computations of this V2C based ADAS are conducted on the cloud server, where digital replicas of the physical entities (e.g., vehicles, drivers, roadways) and the relevant functional modules are created in the cyber world[wherein the simulation]... [a]ll the system algorithms and models are running on a Dell R630 server located at the server room of University of California, Riverside[and training are performed on a central server]”).12
Regarding claim 25, Kain in view of Wang teaches the method of claim 16, wherein the simulation and training are performed on a central server(Wang, pgs., 3-4, see also figs., 1 and 2, “All computations of this V2C based ADAS are conducted on the cloud server, where digital replicas of the physical entities (e.g., vehicles, drivers, roadways) and the relevant functional modules are created in the cyber world[wherein the simulation]... [a]ll the system algorithms and models are running on a Dell R630 server located at the server room of University of California, Riverside[and training are performed on a central server]”).13
Regarding claim 26, Kain in view of Wang teaches the method of claim 17, wherein the simulation and training are performed on a central server(Wang, pgs., 3-4, see also figs., 1 and 2, “All computations of this V2C based ADAS are conducted on the cloud server, where digital replicas of the physical entities (e.g., vehicles, drivers, roadways) and the relevant functional modules are created in the cyber world[wherein the simulation]... [a]ll the system algorithms and models are running on a Dell R630 server located at the server room of University of California, Riverside[and training are performed on a central server]”).14
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210181739 A1(details different simulations and scenarios for autonomous driving vehicles to test different machine learning models for a given task)
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/Adam C Standke/
Primary Examiner
Art Unit 2129
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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
2 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
3 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
4 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
5 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
6 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
7 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
8 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
9 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
10 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
11 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
12 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
13 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
14 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 Kain with the above teachings of Wang for the same rationale stated at Claim 11.
Prosecution Insights