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 office action is in response to an Amendment filed on 2/13/2026. Claims 13 and 15-24 are pending.
Response to Amendment
Amendments filed on 2/13/2026 are under consideration. Claims 13 and 15-24 are amended. Claim 6 is newly added. Claim 14 is cancelled.
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 13 and 15-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claims 13 and 15-23 are directed to A device (i.e., a machine). Therefore, claims 13 and 15-23 are within at least one of the four statutory categories
Claims 24 is directed to A method, (i.e., a process). Therefore, claim 24 is within at least one of the four statutory categories
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed
to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity,
and/or c) mental processes.
Independent claim 13 includes limitations that recite an abstract idea (mental process)
and will be used as a representative claim for the remainder of the 101 rejections. Claim 13 and 24 recites:
that detects an error in a determination by the device of a travel path of a vehicle on a road section, the device comprising one or more sensors configured to capture sensor data during a journey of a vehicle on a road section; and a processor configured to determine the travel path via an optimization method wherein the optimization method optimizes an error function that depends on the sensor data, determine a parameter value of at least one parameter characterizing the performance of the optimization method; and determine, based on the parameter value,. that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “…that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section.” and “determine a parameter value of at least one parameter characterizing the performance of the optimization method” in the context of this claim encompasses a person determining a route being travelled upon by formulating a mathematical function, which can be done with a human’s mental ability as this is simply determining a path of travel while ensuring an error function is minimize, and is a mathematical formulation as the error function is solved for this determination. Again, “that detects an error in a determination by the device of a travel path of a vehicle on a road section” and “determine…that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section.” in the context of this claim encompasses a determination about whether or not the path detected is the one that is assumed to be travelled upon, which can be done with a human’s mental ability as this is simply reading an outputted value and responding with a judgement about the path being travelled upon. Accordingly, the claim recites at least one abstract idea via a mental process and a mathematical formulation.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
that detects an error in a determination by the device of a travel path of a vehicle on a road section, the device comprising one or more sensors configured to capture sensor data during a journey of a vehicle on a road section; and a processor configured to determine the travel path via an optimization method wherein the optimization method optimizes an error function that depends on the sensor data, determine a parameter value of at least one parameter characterizing the performance of the optimization method; and determine, based on the parameter value,. that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of, “capture sensor data during a journey of the vehicle on a road section”, “that depends on sensor data”, and “based on the parameter value,” the examiner submits that these limitations are insignificant extra-solution activities that merely use generic computer components (processor) to perform the mental process and mathematical formulation. Each of the above cited limitations are simply further defining the mental process and collecting environmental data or data from a previously calculated formula (i.e., as a general means for determining a problem on the path of travel), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Additionally the limitations of “the device comprising one or more sensors configured to”, and “and a processor configured to” the examiner submits that these limitations are insignificant extra-solution activities that merely use a processor and sensors to perform the mental process. As the device used has generic components (‘sensors’ and ‘processor’) and the applicant does not make an attempt to improve the functioning of a computer with specialized components. The system, is recited at a high level of generality and merely automates a detection of a problem within the path of travel.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond
generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 13 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above, the additional limitations of “capture sensor data during a journey of the vehicle on a road section”, “that depends on sensor data”, and “based on the parameter value,” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in
Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood,
routine, conventional activity in the field. The additional limitations of “capture sensor data during a journey of the vehicle on a road section”, “that depends on sensor data”, and “based on the parameter value,” are well-understood, routine, and conventional activities because the specification recites that the components are all conventional computer components mounted on the vehicle, and the specification does not provide any indication that the system is anything other than what a conventional computer does within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claims 15-23, do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional
aspects of the judicial exception and/or well-understood, routine and conventional additional
elements that do not integrate the judicial exception into a practical application. Claim 15 mentions “… compare the determined parameter with a threshold value for the parameter…”, which would fail under Step 2A Prong 1 as a mathematical formulation as a threshold is used to compared a determined value with and thus would not make claims 15 to be considered patent eligible subject matter. Claim 16 mentions “adapt the error function” which would fail under Step 2A Prong 1 as a mathematical formulation as a formula is being changed thus would not make claims 16 to be considered patent eligible subject matter.. Claim 17 mentions “comprises a value of a mean square error function” which would fail under Step 2A Prong 1 as a mathematical formulation as a formula is being further defined thus would not make claims 17 to be considered patent eligible subject matter. Claim 18 mentions, “optimization method comprises a number of iterations” which would fail under Step 2A Prong 1 as a mathematical formulation as a formula is being repeatedly calculated thus would not make claims 18 to be considered patent eligible subject matter. Claim 19 mentions, “wherein the error function depends on a graph” which would fail under Step 2A Prong 1 as a mathematical formulation as a formula is being further defined thus would not make claims 19 to be considered patent eligible subject matter. Claim 20 mentions, “wherein the edge error term respectively comprises a square edge error for a multiplicity of edges of the graph” which would fail under Step 2A Prong 1 as a mathematical formulation as a formula is being further defined thus would not make claims 20 to be considered patent eligible subject matter. Claim 21 mentions “determine…, a type of the problem when determining the travel path from a set of different problem types” which would fail under Step 2A Prong one as a mental process as the device is just further configured to make a judgement about whether or not a problem is had based on the detected path, which is something a human mind is capable of doing with pen and paper which would not make claim 21 to be considered patent eligible subject matter. Claim 22 mentions, “odometry measured values relating to a movement” which would fail under Step 2A Prong 2 as this is an insignificant extra solution activity by way of data gathering as the quote is further defining the type of measurement being taken and thus would not make claim 22 patent eligible material. Claim 23 mentions, “respectively determine… whether or not there is a problem when determining the respective travel path” which would fail under Step 2A Prong one as a mental process as the device is just further configured to make a judgement about whether or not a problem is had based on the detected path, which is something a human mind is capable of doing with pen and paper which would not make claim 23 to be considered patent eligible subject matter
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 13-14, 16-19, and 21-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wilbers et al. (DE102018117660A1).
Regarding Claim 13 Wilbers teaches A device (Pg. 1 – [0001] – “The present invention relates to a method and a system for determining the position of a vehicle.”) that detects an error in a determination by the device of a travel path of a vehicle on a road section, the device comprising: (Pg. 12 – [0045] – “This advantageously allows for verification of the quality of landmark detection or the quality of map data. For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” & See Also Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time. In this process, the factor graph is marginalized in such a way that the resulting factor graph does not exceed a predetermined maximum size. In particular, the procedure is carried out at regular time intervals. This makes it advantageous to track the trajectory of the vehicle.” (equates to that detects an error in a determination by the device of a travel path of a vehicle on a road section, the device comprising: as the first quote shows a problem or a determination of quality data being used to localize a vehicle and the second quote showing how this is done to relate to a trajectory of a vehicle or a travel path.)) one or more sensors configured to capture sensor data during a journey of a vehicle on a road section; (Pg. 21 – [0080] – “Furthermore, in step S31, hypotheses are formulated as to which of the detected landmarks stored in the buffer belong to the same physical object in the vicinity of vehicle 1. For example, the same landmark can be detected multiple times by the same detector or by different sensors” (equates to one or more sensors configured to capture sensor data during a journey of the vehicle on a road section as the quote shows a sensor used to detect landmarks.)) and a processor configured to determine the travel path via an optimization method, wherein the optimization method optimizes an error function that depends on the sensor data, (Pg. 13 – [0051] – “a processing unit for determining landmark measurement data for detected landmarks in the vehicle's environment based on the environmental data” & See Also Pg. 1 – [0002] – “Many automatic driving functions in modern vehicles require an accurate estimate of the current vehicle position. Various approaches have been developed in the past to address this problem of localization, including positioning using global navigation satellite systems (GNSS), such as the global positioning system GPS. However, the accuracy of such systems is typically insufficient for use in automated driving functions. However, alternative systems often require excessively high computing power and therefore – given the computing power typically available in vehicles – excessively long computing times for real-time control.” & See Also Pg. 3 – [0012] – “According to the invention, the determination of the position or pose of a vehicle is treated as an optimization problem.” & See Also Pg. 13 – [0050] – “In the method according to the invention, the factor graph comprises landmark position nodes as well as prior landmark data, which may in particular be represented by global landmark factors. In state-of-the-art methods, landmark position nodes are often removed by marginalization. Marginalizing the landmark position nodes can lead to errors in this case, which arise from the approximation (marginalization errors). This type of error is avoided in the method according to the invention. The method according to the invention advantageously provides improved information about the landmark positions. These can be used to check the structure of the factor graph over time for meaningfulness and internal consistency. If necessary, the quality of the map can also be assessed.” & See Also Pg. 1 – [0003] – “In this process, a route is travelled multiple times and trajectory and perception data are recorded” & See Also Pg. 4 – [0017] – “When using landmarks, an abstraction layer is used between the raw sensor data and the localization step, and different sensors can be used to detect landmarks” (equates to and a processor configured to determine the travel path via an optimization method, wherein the optimization method optimizes an error function that depends on the sensor data, as the first quote shows the processing unit configured to perform the identification configuration of the device, quotes 2 and 3 shows the localization techniques of the art being described which includes a determination of a vehicle trajectory and position, the fourth quote shows an optimization problem being formulated by the art and the fifth quote showing the errors being mitigated within the detection, last quote showing the use of sensor data for input values. )) determine a parameter value of at least one parameter characterizing performance of the optimization method; (Pg. 12 – [0045] – “In particular, it may be possible to generate and output quality data based on the prior landmark data and the optimized landmark position data… For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” (equates to determine a parameter value of at least one parameter characterizing performance of the optimization method as the quote shows quality data or a parameter being output based on the implemented optimization method. Wherein the quality data may be used to update map information and thus the quality data is the parameter value in which the optimization method is determined to be correcting map data.)) and determine, based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section. (Pg. 12 – [0045] – “This advantageously allows for verification of the quality of landmark detection or the quality of map data. For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” & See Also Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time. In this process, the factor graph is marginalized in such a way that the resulting factor graph does not exceed a predetermined maximum size. In particular, the procedure is carried out at regular time intervals. This makes it advantageous to track the trajectory of the vehicle.” (equates to based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section as the first quote shows the use of the of quality data as the parameter to determine whether or not information detected is accurate with map data. The second quote shows how the output is affecting and is used to determine vehicle trajectory or the fault in the travel path as the updated map information using the parameter value or quality data is used to track the trajectory of the vehicle. ))
Regarding Claim 16 Wilbers teaches The device of claim 13, wherein the processor is further configured to, (Pg. 25 – [0093] – “3 Processing unit”) determine an updated travel path (Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time.” (equates to determine an updated travel path as an iterative solution to solving the vehicle trajectory is displayed and thus the travel path is determined again. )) via adapting the error function, adapting the optimization method and/or excluding at least some of the sensor data (Pg. 21 – [0080] – “This can be done, for example, using a nearest neighbor strategy, whereby thresholds for excessively large distances lead to the rejection of the hypothesis. Another possibility is to evaluate specific descriptors of the landmarks, especially different landmark types. Generally, a distance function is designed and checked to see which of the other possible landmarks are located at a shorter distance than a threshold value. If a unique solution is found in the exemplary embodiment, it is accepted as a hypothesis. If there are several possible candidates, the hypothesis formation appears too uncertain and is therefore not accepted.” (equates to via adapting the error function, adapting the optimization method and/or excluding at least some of the sensor data as the quote shows the sensor data being captured from outside a threshold distance and being discarded based on not being within a distance threshold thus an exclusion of data from the travel path determination is seen.))
Regarding Claim 17 Wilbers teaches The device of claim 13, wherein the parameter comprises a value of a mean square error function after performing the optimization method. (Pg. 3 – [0013] – “In this procedure, a factor graph is determined and used to represent an optimization problem using the least squares method with nonlinear model functions” (equates to wherein the parameter comprises a value of a mean square error function after performing the optimization method as the quote shows the optimization problem using a least squares method which is minimizing the sum of the squares and finding a line or mean that best represents the dataset.))
Regarding Claim 18 Wilbers teaches The device of claim 13, wherein the parameter comprises a number of iterations when performing the optimization method until a convergence criterion is achieved. (Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time.” & See Also Pg. 12 – [0048] – “Furthermore, data from earlier times can be aggregated, for example into a single estimate for a parameter for a past period, while more recent data are considered as individual measurements.” (equates to wherein the parameter comprises a number of iterations when performing the optimization method until a convergence criterion is achieved as the first quote shows the optimization method of the art being ran iteratively and the second quote showing that a variety of parameters are combined into a single point over the iteratively window thus leading to a convergence of a single solution within the method.))
Regarding Claim 19 Wilbers teaches The device of claim 13, wherein the error function depends on a graph having a multiplicity of nodes for describing the travel path, (Pg. 3 – [0011] – “Map data is collected, including prior landmark data. A factor graph is determined, where the factor graph includes vehicle position nodes representing preliminary vehicle position data and landmark position nodes representing landmark position data.” & See Also Pg. 3 – [0013] – “In this procedure, a factor graph is determined and used to represent an optimization problem using the least squares method with nonlinear model functions” (equates to wherein the error function depends on a graph having a multiplicity of nodes for describing the travel path, as the first quote show a plurality of nodes being present for describing a travel path which includes vehicle positions and landmark positions.)) wherein nodes from the multiplicity of nodes are connected to one another in pairs via an edge in each case, (Pg. 9 – [0036] – “The edges or factors connect the nodes. A factor can be connected to any number of nodes. In particular, the factors are connected to only one node (unary factors) or they connect two nodes in pairs (binary factors).”) wherein the error function comprises an edge error term that depends on odometry measured values in the sensor data, (Pg. 9 – [0036] – “The edges or factors connect the nodes. A factor can be connected to any number of nodes. In particular, the factors are connected to only one node (unary factors) or they connect two nodes in pairs (binary factors).”) & See Also Pg. 10 – [0039] – “In a further development of the procedure, an association of landmark measurement data is carried out when determining the factor graph, whereby associated landmark measurement data are determined depending on a specific time point in time. In particular, the procedure identifies multiple, repeated observations of the same landmark. This advantageously allows the scope of the optimization problem to be solved computationally to be reduced and solved more efficiently by associating repeated observations of the same landmark. Following the solution of the optimization problem, the results obtained can also be compared with the prior information to check the correctness and consistency of the prior information” & See Also Pg. 24 – [0091] – “The procedure can also integrate additional landmarks if they can be represented geometrically and an error function can be specified for them.” & See Also Pg. 18 – [0071] – “The input data includes preliminary position data, which in this embodiment are acquired using odometry methods and as global pose estimates, in particular using GPS” & See Also (equates to wherein the error function comprises an edge error term which depends on odometry measured values in the sensor data as the first quote shows the connection of the nodes via an edge, the second quote showing the landmark data is a part of the factor graph wherein the factor is the node, and third quote shows the error term for the landmark data. The fourth and fifth quote show the taking of the odometry data relating the position. being taken to the landmark detected. ) ) and wherein the parameter depends on the edge error term. (Pg. 24 – [0091] – “The procedure can also integrate additional landmarks if they can be represented geometrically and an error function can be specified for them” & See Also Pg. 9 – [0036] – “A factor can be connected to any number of nodes. In particular, the factors are connected to only one node (unary factors) or they connect two nodes in pairs (binary factors).” & See Also Pg. 10 – [0038] – “Global landmark factors similarly describe a prior over a landmark node that represents the parameters of a landmark to be estimated” (equates to and wherein the parameter depends on the edge error term as the art shows an error function or error term being provided with additional landmarks wherein the third quote shows how landmarks are nodes within the art and the third quote showing nodes may be connected via an edge and thus the edge error term would be used for the additional landmark within the edge that is being detected.))
Regarding Claim 21 Wilbers teaches The device of claim 13, wherein the processor is further configured to (Pg. 25 – [0093] – “3 Processing unit”): determine parameter values for a plurality of parameters characterizing the performance of the optimization method, (Pg. 7 – [0029] – “Position and environmental data can be recorded, in particular for a large number of times and/or positions during the movement of the vehicle. In this case, the movement of the vehicle can be tracked along a trajectory, that is, along a specific path as a function of time.” & See Also Pg. 9 - [0035] – “The factor graph determined by the procedure consists of a set of nodes and edges, which here represent factors. The nodes here comprise estimated quantities to be determined, in particular vehicle poses at specific times and landmarks with their corresponding poses” & See Also Pg. 3 – [0011] – “An optimization of the factor graph is performed, whereby optimized vehicle position data is determined” (equates to determine parameter values for a plurality of parameters characterizing the performance of the optimization method as the first quote shows a plurality of parameter values being captured (landmark and vehicle position), the second quote shows that the data captured is put into a factor graph wherein the last quote shows the factor graph being a part of the optimization problem.))) and determine, based on the determined parameter values, that the determined travel path does not correspond to the actual trajectory due to a type of problem from a set of problem types, (Pg. 3 – [0012] – “According to the invention, the determination of the position or pose of a vehicle is treated as an optimization problem. The application of factor graphs according to the invention advantageously achieves a particularly intuitive and clear description of the problem underlying the optimization, which in particular represents a system of equations. Optimizing the factor graph solves the optimization problem it represents, and this solution can be represented and solved particularly efficiently.” & See Also Pg. 7 – [0029] – “Position and environmental data can be recorded, in particular for a large number of times and/or positions during the movement of the vehicle. In this case, the movement of the vehicle can be tracked along a trajectory, that is, along a specific path as a function of time.” & See Also Pg. 9 - [0035] – “The factor graph determined by the procedure consists of a set of nodes and edges, which here represent factors. The nodes here comprise estimated quantities to be determined, in particular vehicle poses at specific times and landmarks with their corresponding poses” & See Also Pg. 7 – [0029] – “Position and environmental data can be recorded, in particular for a large number of times and/or positions during the movement of the vehicle. In this case, the movement of the vehicle can be tracked along a trajectory, that is, along a specific path as a function of time.” (equates to and determine, based on the determined parameter values, that the determined travel path does not correspond to the actual trajectory due to a type of problem from a set of problem types as the first quote shows the set of problem types being solved being the position or pose of the vehicle, and is doing so it’s based on optimizing the factor graph wherein the second quote shows the position and environmental data (plurality of parameters) being incorporated into the factor graph for the determination of the travel path. )) wherein the set of problem types comprises: erroneous measured values in the sensor data, (Pg. 13 – [0050] – “The procedure can also integrate additional landmarks if they can be represented geometrically and an error function can be specified for them.” & See Also Pg. 21 – [0080] – “For example, the same landmark can be detected multiple times by the same detector or by different sensors” (equates to wherein the set of problem types comprises: erroneous measured values in the sensor data, as the first quote shows an error function being defined for landmarks being integrated wherein the second quote shows landmarks are measured via sensors and thus the error function if for erroneous measured values of the sensor data.)) erroneous association between (a ) measured values from the sensor data, relating to a landmark in an environment of the vehicle, (Pg. 13 – [0050] – “The procedure can also integrate additional landmarks if they can be represented geometrically and an error function can be specified for them.” & See Also Pg. 21 – [0080] – “For example, the same landmark can be detected multiple times by the same detector or by different sensors” (equates to erroneous association between (a ) measured values from the sensor data, relating to a landmark in an environment of the vehicle, as the first quote shows an error function being defined for landmarks being integrated wherein the second quote shows landmarks are measured via sensors..)) and (b) nodes and/or edges of an optimization graph for determining the travel path, and erroneous and/or unsuitable initialization of the optimization method. (Pg. 3 – [0011] – “Map data is collected, including prior landmark data. A factor graph is determined, where the factor graph includes vehicle position nodes representing preliminary vehicle position data and landmark position nodes representing landmark position data” (equates to and (b) nodes and/or edges of an optimization graph for determining the travel path, and erroneous and/or unsuitable initialization of the optimization method as the quote shows the landmark data relating to the nodes and thus the previously mapped landmark data via sensor detection the errors detected in the landmarks pertain to the nodes as well.))
Regarding Claim 22 Wilbers teaches The device of claim 13, wherein the sensor data including measured position values relating to the position of the vehicle, and/or odometry measured values relating to a movement of the vehicle during the journey and/or wherein the optimization method comprises a graph Simultaneous Localization and Mapping method. (Pg. 16 – [0062] – “They are therefore related to the data measured by the acquisition unit 3 of an odometry device which captures and records data about the movement, in particular speed and steering angle, during the movement from one pose to another.” (equates to wherein the sensor data including measured position values relating to the position of the vehicle, and/or odometry measured values relating to a movement of the vehicle during the journey and/or wherein the optimization method comprises a graph Simultaneous Localization and Mapping method as the quote shows the utilization of odometry data for capturing vehicle movement.) )
Regarding Claim 23 Wilbers teaches The device of claim 13, wherein the processor is further configured to:, monitor a quality of the sensor data and/or the optimization method. based on successive performances of the optimization method to determine respective travel paths and corresponding determinations of whether the respective travel paths are erroneous. (Pg. 12 – [0045] – “In particular, it may be possible to generate and output quality data based on the prior landmark data and the optimized landmark position data. This advantageously allows for verification of the quality of landmark detection or the quality of map data. For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” & See Also Pg. 6 – [0022] – “Position data therefore includes information about the position, and possibly also, in a broader sense, information about the pose of the vehicle and/or a movement state of the vehicle. This information may relate to a current or earlier point in time, including a series of successive points in time. The position data is collected and provided in a manner that is known per se.” & See Also Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time.” (equates to monitor a quality of the sensor data and/or the optimization method. based on successive performances of the optimization method to determine respective travel paths and corresponding determinations of whether the respective travel paths are erroneous as the quote shows a verification of the data being performed to update map data and thus the data is monitored for quality while the determination is simultaneously being made about the quality of the data relating to a problem with within the travel path. The last two quotes show a successive position taking and the procedure happening iteratively and thus the procedure being the optimization method of the position taking of the vehicle occurring successively ))
Regarding Claim 24 Wilbers teaches A method (Pg. 1 – [0001] – “The present invention relates to a method and a system for determining the position of a vehicle.”) for detecting an error in a determination by a device of a travel path of a vehicle on a road section, (Pg. 12 – [0045] – “This advantageously allows for verification of the quality of landmark detection or the quality of map data. For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” & See Also Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time. In this process, the factor graph is marginalized in such a way that the resulting factor graph does not exceed a predetermined maximum size. In particular, the procedure is carried out at regular time intervals. This makes it advantageous to track the trajectory of the vehicle.” (equates to for detecting an error in a determination by a device of a travel path of a vehicle on a road section as the first quote shows a problem or a determination of quality data being used to localize a vehicle and the second quote showing how this is done to relate to a trajectory of a vehicle or a travel path.)) wherein the travel path is determined by the device performing an optimization method that optimizes an error function, (Pg. 13 – [0051] – “a processing unit for determining landmark measurement data for detected landmarks in the vehicle's environment based on the environmental data” & See Also Pg. 1 – [0002] – “Many automatic driving functions in modern vehicles require an accurate estimate of the current vehicle position. Various approaches have been developed in the past to address this problem of localization, including positioning using global navigation satellite systems (GNSS), such as the global positioning system GPS. However, the accuracy of such systems is typically insufficient for use in automated driving functions. However, alternative systems often require excessively high computing power and therefore – given the computing power typically available in vehicles – excessively long computing times for real-time control.” & See Also Pg. 3 – [0012] – “According to the invention, the determination of the position or pose of a vehicle is treated as an optimization problem.” & See Also Pg. 13 – [0050] – “In the method according to the invention, the factor graph comprises landmark position nodes as well as prior landmark data, which may in particular be represented by global landmark factors. In state-of-the-art methods, landmark position nodes are often removed by marginalization. Marginalizing the landmark position nodes can lead to errors in this case, which arise from the approximation (marginalization errors). This type of error is avoided in the method according to the invention. The method according to the invention advantageously provides improved information about the landmark positions. These can be used to check the structure of the factor graph over time for meaningfulness and internal consistency. If necessary, the quality of the map can also be assessed.” & See Also Pg. 1 – [0003] – “In this process, a route is travelled multiple times and trajectory and perception data are recorded” & See Also Pg. 4 – [0017] – “When using landmarks, an abstraction layer is used between the raw sensor data and the localization step, and different sensors can be used to detect landmarks” (equates to wherein the travel path is determined by the device performing an optimization method that optimizes an error function as the first quote shows the processing unit configured to perform the identification configuration of the device, quotes 2 and 3 shows the localization techniques of the art being described which includes a determination of a vehicle trajectory and position, the fourth quote shows an optimization problem being formulated by the art and the fifth quote showing the errors being mitigated within the detection, last quote showing the use of sensor data for input values. )) wherein the error function depends on sensor data from one or more sensors of the vehicle captured during a journey of the vehicle on the road section, (Pg. 21 – [0080] – “Furthermore, in step S31, hypotheses are formulated as to which of the detected landmarks stored in the buffer belong to the same physical object in the vicinity of vehicle 1. For example, the same landmark can be detected multiple times by the same detector or by different sensors” (equates to one or more sensors configured to capture sensor data during a journey of the vehicle on a road section as the quote shows a sensor used to detect landmarks.)) the method comprising: determining a parameter value of at least one parameter characterizing the performance of the optimization method; (Pg. 12 – [0045] – “In particular, it may be possible to generate and output quality data based on the prior landmark data and the optimized landmark position data” (equates to the method comprising: determining a parameter value of at least one parameter characterizing the performance of the optimization method as the quote shows quality data or a parameter being output based on the implemented optimization method. )) and determining, based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section (Pg. 12 – [0045] – “This advantageously allows for verification of the quality of landmark detection or the quality of map data. For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” & See Also Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time. In this process, the factor graph is marginalized in such a way that the resulting factor graph does not exceed a predetermined maximum size. In particular, the procedure is carried out at regular time intervals. This makes it advantageous to track the trajectory of the vehicle.” (equates to and determining, based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section as the first quote shows the use of the of quality data as the parameter to determine whether or not information detected is accurate with map data. The second quote shows how the output is affecting and is used to determine vehicle trajectory or the fault in the travel path. ))
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Wilbers as previously mapped above and in view of Koji (JP,3984112,B).
Regarding Claim 15 Wilbers teaches The device of claim 13, wherein the determined parameter value is compared with a threshold value, so as to determine that the determined travel path does not correspond to the actual trajectory, and (Pg. 21 – [0080] – “For example, the same landmark can be detected multiple times by the same detector or by different sensors. This can be done, for example, using a nearest neighbor strategy, whereby thresholds for excessively large distances lead to the rejection of the hypothesis.” (equates to wherein the determined parameter value is compared with a threshold value, so as to determine that the determined travel path does not correspond to the actual trajectory, as the quote shows a parameter being the quality data wherein the threshold is the distance away the data is interpreted from )) on the basis of a multiplicity of reference performances of the optimization method for the purpose of determining a corresponding multiplicity of reference travel paths; (Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time.” & See Also Pg. 3 – [0013] – “In this procedure, a factor graph is determined and used to represent an optimization problem” (equates to on the basis of a multiplicity of reference performances of the optimization method for the purpose of determining a corresponding multiplicity of reference travel paths as the first quote shows iterative repetition of the optimization of the determination vehicle position which is directly linked to determining the vehicle trajectory))
Yet Wilbers fails to teach wherein the threshold value is determined experimentally.
Koji teaches wherein the threshold value is determined experimentally (Pg. 8 – [0017] – “In the vehicle position correction device of the present invention to which the distance threshold value setting method is applied, the distance threshold value related to the reliability of the position data obtained by the GPS and used for determining whether or not to perform the vehicle position correction by the GPS position data is set by comparing with the distance between the vehicle position obtained by the GPS and the vehicle position obtained by the self-contained navigation. The distance threshold value is data serving as a comparison reference when it is determined whether or not the position correction by the GPS data is performed when the map matching by the self-contained navigation becomes impossible.” (equates to wherein the threshold value is determined experimentally as the quote shows a threshold being determined via data received from a GPS and comparing to a navigation unit within the vehicle and thus the threshold is attained experimentally as its done based on live data being received.)) It would have been an advantageous addition to the system disclosed by Wilbers to include threshold value is determined experimentally as this would have allowed the parameter to be determined iteratively and be adjusted based on the environment being detected allowing for a more robust system handling a different terrains in different manner.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include threshold value is determined experimentally as this allows for a threshold to be changed based on the detection of the object allowing for a robust system allowing for the use of the parameter to be based on the ability to alter the threshold for whether or not there is a detected issue with the path being sensed..
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Wilbers as previously mapped above and in view of Kurz (US 2024/0160960 A1) and in further view of Lee et al. (US 2023/0236323 A1)
Regarding Claim 20 Wilbers teaches The device of claim 19, as previously mapped above.
Yet Wilbers fails to teach wherein the edge error term respectively comprises a square edge error for a multiplicity of edges of the graph, and wherein the parameter comprises a maximum value of the edge errors for the multiplicity of edges after performing the optimization method.
Kurz teaches wherein the edge error term respectively comprises a square edge error for a multiplicity of edges of the graph (Pg. 13 – [0037] – “Removing an edge in each case comprises determining, out of the multiplicity of nodes, the node at which the most edges end; edges can thus be removed where the density thereof is high. The edge to be removed can then be selected from the edges ending at said node on the basis of a criterion. One option is to remove the edge having the lowest residuum or the lowest chi-square error” (equates to wherein the edge error term respectively comprises a square edge error for a multiplicity of edges of the graph as the quote shows that in order to remove an false edge a square error for each of the edges is calculated and compared to determine the removal.))
Yet both Wilbers-Kurz teaches and wherein the parameter comprises a maximum value of the edge errors for the multiplicity of edges after performing the optimization method.
Lee teaches and wherein the parameter comprises a maximum value of the edge errors for the multiplicity of edges after performing the optimization method. (Pg. 23 – Table 4 – “Absolute Pose Error – Edge - Max” & See Also Pg. 12 – Fig. 12 & See Also Pg. 19 – [0062] – “FIG. 12 shows edge: 1, planar: 1 (left), edge: 1, planar: 2 (right) per sub-image (60°).” & See Also Pg. 21 – [0103] – “Thereafter, for feature extraction, smoothness of each point is calculated and classified into edge and planar.” & See Also Pg. [0106] – “Finally, the odometry is obtained by calculating a transform matrix between the features having correspondence. At this time, in order to solve the transform matrix as an optimization problem, if a distance of the correspondence becomes close, it means that registration has been properly performed, and optimization is performed with edge correspondence and planar correspondence as costs.” & See Also Pg. 23 – [0152] – “Through the ARM board, input/output of various sensors and algorithm calculation are performed to check a vehicle location and map output” (equates to wherein the parameter comprises a maximum value of the edge errors for the multiplicity of edges after performing the optimization method. As the table shows a multiplicity of edges wherein the edges determined by smoothing points or nodes together into an edge. Wherein the maximum value of the edge error is calculated as seen by the table containing a variety of values including a maximum error. The last two quote shows the optimization problem of the art being for the edge correspondence for the vehicle locating )) It would have been an advantageous addition to the device disclosed by Wilbers-Kurz to include and wherein the parameter comprises a maximum value of the edge errors for the multiplicity of edges after performing the optimization method as this limitation allows for a maximum amount of error to be seen and be filtered out for an accurate mapping result to be attained.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include and wherein the parameter comprises a maximum value of the edge errors for the multiplicity of edges after performing the optimization method as having a maximum value for each edge allows for a value to be excluded and thus allow for quality mapping to be had.
Response to Arguments
Response to 35 U.S.C. § 101 rejection of claims 13 and 15 -24 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on page 1, “Claims 13-24 were rejected under 35 U.S.C.§101 as allegedly directed to patent- ineligible subject matter.
Without conceding the merits of the rejections, it is believed that the current amendments, made for the sole purpose of advancing prosecution, render the rejections moot. It is also respectfully submitted that the claimed invention is directed to solving a technical problem existing in navigation devices that determine travel paths based on sensor data using an optimization method that optimizes an error function (e.g., a SLAM method). As described in the Specification, the sensor data that is used by such devices to determine the travel path can be distorted (e.g., due to travel inside a tunnel that impacts, for example, GPS measured position data) and may result in a determined travel path that is erroneous. See e.g., Appl. at[0003]-[0005]; [0048]. The claimed invention permits the device to check that the travel path that it itself determines based on the sensor data is not erroneously determined due to having received distorted sensor data. Id. Thus, the invention is directed to a technical solution to a technical problem.
For at least the foregoing reasons, the instant claims are directed to patent-eligible subject matter. To the extent claims are not specifically mentioned above, those claims depend from mentioned claims and are likewise directed to patent-eligible subject matter. It is respectfully requested that the instant rejections therefore be withdrawn.”- As to point A the Examiner respectfully disagrees. Applicant appears to argue that at Step 2A Prong 1, the claim provided does not include abstract ideas and that the recitation of " determine the travel path via an optimization method wherein the optimization method optimizes an error function that depends on the sensor data " is not merely a mathematical formulation. As previously stated the limitations " determine the travel path via an optimization method wherein the optimization method optimizes an error function " can be done via a mathematical formulation in which a human is capable of doing with the aid of pen and paper, and therefore, the claim does recite the judicial exception of a mathematical formulation being used. If the aforementioned limitation was given an element of vehicular control in which the vehicle was controlled based on the determined travel path this would render the 35 U.S.C. 101 rejection moot in light that a human mind cannot control a vehicle to move on its own but can optimize an error function.
Response to 35 U.S.C. § 102 & 35 U.S.C. § 103 rejection of claims 13 and 15 -24 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on pages 2-3, “First, Wilber does not disclose detecting an error in a determination by the device of a travel path. To the extent the vehicle position nodes of the factor graph constitutes the determined travel path, Wilber is entirely silent with respect to detecting an error in the determination of the vehicle position nodes of the factor graph. Indeed, the sections of Wilber relied on by the Office Action discuss optimizing landmark position data for landmark position nodes of the factor graph, and then updating prior map data to reflect the optimized landmark position. Id. at [0045]-[0046]. These sections say nothing about detecting an error in a determination of the vehicle position nodes in the factor graph.
Second, Wilber does not disclose determining a parameter value of at least one parameter characterizing performance of the optimization method. The Office Action contends that parameter characterizing performance of the optimization method is met by output quality data reflecting the difference between the optimized landmark position data and the prior landmark position data provided by the map data. However, the difference between the optimized landmark position data and the prior landmark position data provided by the map data does not characterize the performance of the optimization method, since Wilber assumes that the optimized landmark position data is accurate and therefore updates the map data. Thus, the output quality data in Wilber characterizes the accuracy of the map data-not the performance of the optimization method.
Third, Wilber does not disclose determining, based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section. Wilbers is indeed entirely silent with respect to determining that the vehicle position nodes of the factor graph (i.e., the alleged determined travel path) do not correspond to an actual trajectory of the vehicle. Rather, Wilbers assumes that its optimization method for determining the vehicle position nodes accurately does so. See Wilbers, generally.” - As to point B the examiner respectfully disagrees. Applicant asserts that Wilbers does not teach “detecting an error in a determination by the device of a travel path”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is a sensing of a difference in vale between actual and the sensed path in which the vehicle is travelling over. Wilbers teaches landmark detection in which a difference between map data stored and the actual sensed data of the landmark is differeing and thus an error is established between the map data and the newly gathered landmark data (as mapped above in claim 13 and 24). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Wilbers teaches “detecting an error in a determination by the device of a travel path”. Wilbers discloses: (Pg. 12 – [0045] – “This advantageously allows for verification of the quality of landmark detection or the quality of map data. For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” & See Also Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time. In this process, the factor graph is marginalized in such a way that the resulting factor graph does not exceed a predetermined maximum size. In particular, the procedure is carried out at regular time intervals. This makes it advantageous to track the trajectory of the vehicle.” (equates to that detects an error in a determination by the device of a travel path of a vehicle on a road section, the device comprising: as the first quote shows a problem or a determination of quality data being used to localize a vehicle and the second quote showing how this is done to relate to a trajectory of a vehicle or a travel path.))
Similarly, Applicant asserts that Wilbers does not teach “determining a parameter value of at least one parameter characterizing performance of the optimization method”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is determining any quantity that is related to a variable being a part of the optimization method. Wilbers teaches landmark detection in which a landmark position is determined within the optimization method and thus the parameter value is relating to the landmark position (as mapped above in claim 13 and 24). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Wilbers teaches “determining a parameter value of at least one parameter characterizing performance of the optimization method”. Wilbers discloses: (Pg. 12 – [0045] – “In particular, it may be possible to generate and output quality data based on the prior landmark data and the optimized landmark position data… For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” (equates to determine a parameter value of at least one parameter characterizing performance of the optimization method as the quote shows quality data or a parameter being output based on the implemented optimization method. Wherein the quality data may be used to update map information and thus the quality data is the parameter value in which the optimization method is determined to be correcting map data.))
Lastly, Applicant asserts that Wilbers does not teach “determining, based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is determining based on the any quantity of variable relating to the optimization method about whether or not the calculated travel path matches the actual path of travel as measured by the device within the vehicle. Wilbers teaches landmark detection in which a landmark position is determined within the optimization method and thus the parameter value is relating to the landmark position in which this determining and recognizing the landmark is done every couple of seconds and is used to determine the vehicle trajectory travelling along a path. (as mapped above in claim 13 and 24). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Wilbers teaches “determining, based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section”. Wilbers discloses: (Pg. 12 – [0045] – “This advantageously allows for verification of the quality of landmark detection or the quality of map data. For example, the quality data may include information about whether and to what extent the actually detected landmarks differ from the information provided with the map data. In particular, it may be possible to update the map data based on the quality data” & See Also Pg. 12 – [0046] – “During training, the procedure is iteratively repeated for several points in time and a trajectory of positions is output, whereby a later factor graph, which is assigned to a later point in time, is determined based on an earlier factor graph, which is assigned to an earlier point in time. In this process, the factor graph is marginalized in such a way that the resulting factor graph does not exceed a predetermined maximum size. In particular, the procedure is carried out at regular time intervals. This makes it advantageous to track the trajectory of the vehicle.” (equates to based on the parameter value, that the determined travel path does not correspond to an actual trajectory traveled by the vehicle on the road section as the first quote shows the use of the of quality data as the parameter to determine whether or not information detected is accurate with map data. The second quote shows how the output is affecting and is used to determine vehicle trajectory or the fault in the travel path as the updated map information using the parameter value or quality data is used to track the trajectory of the vehicle. ))
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. DE102018006265B4. - Method for optimizing a vehicle to be designed, comprising the steps: a) selecting a traffic scenario from a database that contains data from at least one trajectory and data on an environment that were recorded specifically for a real vehicle (1); b) determining a sequence of permissible locations (8) for the vehicle to be designed by calculating a permissible location (8) for each location from a sequence of locations of the real vehicle (1) along the trajectory,
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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/R.A.W./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
4/27/26
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