DETAILED ACTION
The amendments filed 9/2/2026 have been entered. Claims 1-21 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-7, and 9-21 are rejected under 35 U.S.C. 103 as being unpatentable over Hayakawa (US Publication No. 2018/0308358) in view of Nakka et al. (US Publication No. 2020/0258388).
Hayakawa teaches:
Re claim 1. One or more processors comprising processing circuitry configured to (CPU 11, Fig. 1; and paragraph [0031]: “CPU has an operation circuit”):
determine, based at least on processing sensor data obtained using one or more sensors of an ego-machine, a virtual parking segment representing a real-world parking segment in an environment (cameras 1a-1d, Figure 1; detect available parking spaces, 103, Figure 2; and paragraph [0065]: “The control device 10 detects parking spaces into which the subject vehicle V moving in the direction of the arrow (indicated ahead of the subject vehicle V) can be parked.”);
cause performance of one or more control operations associated with navigation of the ego-machine based at least on a parking assistance output indicative of the parking rule (execute parking assist 109, Fig. 2; and paragraph [0109]).
Hayakawa fails to specifically teach: (re claim 1) determine, based at least on feature data determined using the sensor data, one or more parking rules and a respective confidence in an accuracy of a determination of each of the one or more parking rules;
determine a parking rule to apply to the virtual parking segment, the parking rule including:
a determined parking rule when the respective confidence in the accuracy of the determination of at least one parking rule of the one or more parking rules is above a threshold; or
a default parking rule when the respective confidence in the accuracy of the determination of each of the one or more parking rules is below the threshold.
Nakka teaches, at paragraphs [0041-0043], using data from a GPS or other location sensors coupled to a vehicle to determine a geographical location of a parking space, and various parameters, including an arrival time of the vehicle to the parking space location, a departure time of the vehicle from the parking space location, a time during which the vehicle remains at the parking space location, a date at which the vehicle stays at the parking space location, a place type (e.g., an apartment complex, a shopping mall, an office parking, a gasoline station, or any other place type), a number of parking recurrences that indicates how many times the vehicle is parked at the same parking space location. This data derived from the GPS is used to determine a confidence level that a parking space is an assigned parking space, and Nakka determines a parking space is an assigned parking space if a confidence level is greater than a predetermined threshold. When the confidence level is above the threshold, the system determines the parking space is an assigned parking space; when the confidence level is below the threshold, the system determines the parking space is not an assigned parking space (default parking rule). This allows for such systems to learn an assigned parking space based on observed data.
In view of Nakka’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Hayakawa, (re claim 1) determine, based at least on feature data determined using the sensor data, one or more parking rules and a respective confidence in an accuracy of a determination of each of the one or more parking rules; determine a parking rule to apply to the virtual parking segment, the parking rule including: a determined parking rule when the respective confidence in the accuracy of the determination of at least one parking rule of the one or more parking rules is above a threshold; or a default parking rule when the respective confidence in the accuracy of the determination of each of the one or more parking rules is below the threshold, with a reasonable expectation of success, since Nakka teaches using data from a GPS coupled to a vehicle to determine various parameters, and using these parameters to determine a confidence level that a parking space is an assigned parking space, and Nakka determines a parking space is an assigned parking space if a confidence level is greater than a predetermined threshold. When the confidence level is above the threshold, the system determines the parking space is an assigned parking space; when the confidence level is below the threshold, the system determines the parking space is not an assigned parking space (default parking rule). This allows for such systems to learn an assigned parking space based on observed data.
Hayakawa further teaches:
Re claim 3. Wherein the processing circuitry is further configured to:
determine a geometry of the virtual parking segment based at least on the sensor data, wherein the parking assistance output is further indicative of the geometry (paragraphs [0129, 0132-0134] detect dimensions of parking area based on parking frame lines).
Hayakawa fails to specifically teach: (re claim 4) wherein the parking rule comprises an indication of a permission associated with at least one of parking or stopping within the virtual parking segment.
Nakka teaches, at paragraph [0043] and 922, Fig. 9, determining whether a parking space is an assigned parking space and whether a vehicle is permitted to park in the assigned parking space. This allows for such systems to learn an assigned parking space based on observed data.
In view of Nakka’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Hayakawa, (re claim 4) wherein the parking rule comprises an indication of a permission associated with at least one of parking or stopping within the virtual parking segment, with a reasonable expectation of success, since Nakka teaches, at paragraph [0043] and 922, Fig. 9, determining whether a parking space is an assigned parking space and whether a vehicle is permitted to park in the assigned parking space. This allows for such systems to learn an assigned parking space based on observed data.
Hayakawa further teaches:
Re claim 5. Wherein the one or more sensors of the ego-machine comprise at least one of a camera sensor (camera 1a-1d, Fig. 1), a RADAR sensor, an ultrasonic sensor, or a LiDAR sensor.
Hayakawa fails to specifically teach: (re claim 6) the feature data representing one or more features that include at least one of: a sign, an intersection, a color of a path surface, or a symbol on the path surface.
Nakka teaches, at 908-911, Fig. 9, determining rules related to a parking spot based on color, words and characters detected in images. This allows for such systems to determine parking restrictions based on curb colors or signs.
In view of Nakka’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Hayakawa, (re claim 6) the feature data representing one or more features that include at least one of: a sign, an intersection, a color of a path surface, or a symbol on the path surface, with a reasonable expectation of success, since Nakka teaches, at 908-911, Fig. 9, determining rules related to a parking spot based on color, words and characters detected in images. This allows for such systems to determine parking restrictions based on curb colors or signs.
Hayakawa fails to specifically teach: (re claim 7) wherein the processing circuitry is further configured to:
filter the feature data to filter out features having a distance greater than a specified distance threshold from at least one of the ego-machine or a planned path of travel of the ego-machine.
Hayakawa teaches, at paragraph [0176], displaying close parking space images in a larger size than far away parking space images. At a certain point, parking spaces are too far away to be meaningfully represented on a display, or to be accurately detected. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the processor as taught by Hayakawa, (re claim 7) wherein the processing circuitry is further configured to: filter the feature data to filter out features having a distance greater than a specified distance threshold from at least one of the ego-machine or a planned path of travel of the ego-machine, with a reasonable expectation of success, since Hayakawa teaches displaying close parking space images in a larger size than far away parking space images; and at a certain point, parking spaces are too far away to be meaningfully represented on a display, or to be accurately detected. Not wasting computational or display resources on such far away parking spaces would free up these resources for a more responsive user experience, without eliminating any data that is meaningful for a user’s current location.
Hayakawa further teaches:
Re claim 9. Wherein the processing circuitry is further configured to:
generate one or more virtual parking signs associated with one or more boundaries of the virtual parking segment based at least on the sensor data, the one or more virtual parking signs comprising a first virtual parking sign at a first boundary of the virtual parking segment comprising a representation of the parking rule and a second virtual parking sign indicating a termination of the virtual parking segment at a second boundary of the virtual parking segment (102-105, Figure 2; paragraphs [0040-0041]: virtual overhead viewpoint is generated at 102, Figure 2; Figure 9 and paragraph [0180], virtual parking signs indicating parking spaces into which parking is not possible (PL2, PR3), and parking spaces into which parking is possible (PR2, PR4, PR5, PL3, PL4, and PL5).).
Re claim 10. Wherein the processing circuitry is further configured to:
compute one or more distances of the first virtual parking sign and the second virtual parking sign from the ego-machine (Fig. 9 and paragraph [0180]); and
update the one or more distances of the first virtual parking sign and the second virtual parking sign from the ego-machine based at least on a tracked motion of the ego- machine (Fig. 9 and paragraph [0180]).
Re claim 11. Wherein the processing circuitry is further configured to:
control a display of the ego-machine to display a first graphic for a first virtual sign corresponding to a starting boundary of the virtual parking segment and a second graphic for a second virtual sign corresponding to an ending boundary of the virtual parking segment and display a representation of the parking rule (Fig. 9).
Re claim 12. Wherein the one or more processors are comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (paragraph [0028]);
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for generating synthetic data using machine learning;
a system for generating multi-dimensional assets using a collaborative content creation platform;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
Re claim 13. A system comprising one or more processors configured to:
extract, using sensor data obtained using one or more on-board sensors of an ego-machine, feature data indicative of a geometry and a location of a real-world parking segment (cameras 1a-1d, Figure 1; detect available parking spaces, 103, Figure 2; and paragraph [0065]: “The control device 10 detects parking spaces into which the subject vehicle V moving in the direction of the arrow (indicated ahead of the subject vehicle V) can be parked.”);
generate a virtual parking segment having the geometry and a relative position with respect to a path of travel of the ego-machine based at least on the feature data (paragraphs [0129, 0132-0134] detect dimensions of parking area based on parking frame lines; paragraph [0037]: motion stereo technique is used for detection by the cameras; Figures 7A-11C, the locations of the symbols representing the parking spaces in the overhead view will change as a vehicle moves and the location of the parking spaces moves relative to the vehicle. Figure 6B and paragraph [0145], the system works with parallel parking spaces); and
control one or more operations to assist in navigating the ego-machine based at least on the virtual parking segment and the parking rule associated with the virtual parking segment (execute parking assist 109, Fig. 2; and paragraph [0109]).
Hayakawa fails to specifically teach: (re claim 13) the feature data further indicative of a parking rule representing one or more permissions associated with the real-world parking segment;
determine, based at least on the feature data, a confidence in an accuracy of the parking rule;
associate the parking rule with the virtual parking segment based at least on the confidence in the accuracy of the parking rule satisfying a threshold.
Nakka teaches, at paragraphs [0041-0043], using data from a GPS or other location sensors coupled to a vehicle to determine a geographical location of a parking space, and various parameters, including an arrival time of the vehicle to the parking space location, a departure time of the vehicle from the parking space location, a time during which the vehicle remains at the parking space location, a date at which the vehicle stays at the parking space location, a place type (e.g., an apartment complex, a shopping mall, an office parking, a gasoline station, or any other place type), a number of parking recurrences that indicates how many times the vehicle is parked at the same parking space location. This data derived from the GPS is used to determine a confidence level that a parking space is an assigned parking space, and Nakka determines a parking space is an assigned parking space if a confidence level is greater than a predetermined threshold. When the confidence level is above the threshold, the system determines the parking space is an assigned parking space. This allows for such systems to learn an assigned parking space based on observed data.
In view of Nakka’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the system as taught by Hayakawa, (re claim 13) the feature data further indicative of a parking rule representing one or more permissions associated with the real-world parking segment; determine, based at least on the feature data, a confidence in an accuracy of the parking rule; associate the parking rule with the virtual parking segment based at least on the confidence in the accuracy of the parking rule satisfying a threshold, with a reasonable expectation of success, since Nakka teaches using data from a GPS coupled to a vehicle to determine various parameters, and using these parameters to determine a confidence level that a parking space is an assigned parking space, and Nakka determines a parking space is an assigned parking space if a confidence level is greater than a predetermined threshold. When the confidence level is above the threshold, the system determines the parking space is an assigned parking space. This allows for such systems to learn an assigned parking space based on observed data.
Hayakawa fails to specifically teach: (re claim 14) wherein the one or more permissions indicate at least one of:
parking is allowed within the virtual parking segment;
stopping is allowed within the virtual parking segment;
no parking is allowed within the virtual parking segment;
no stopping is allowed within the virtual parking segment; or
parking is allowed within the virtual parking segment subject to permit.
Nakka teaches, at paragraph [0043], determining a confidence level that a parking space is an assigned parking space. This allows for such systems to learn an assigned parking space based on observed data.
In view of Nakka’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the system as taught by Hayakawa, (re claim 14) wherein the one or more permissions indicate at least on one of: parking is allowed within the virtual parking segment; stopping is allowed within the virtual parking segment; no parking is allowed within the virtual parking segment; no stopping is allowed within the virtual parking segment; or parking is allowed within the virtual parking segment subject to permit, with a reasonable expectation of success, since Nakka teaches, at paragraph [0043], determining a confidence level that a parking space is an assigned parking space. This allows for such systems to learn an assigned parking space based on observed data.
Hayakawa further teaches:
Re claim 15. The one or more processors further configured to:
generate a parking assistance output indicative of the parking rule and the relative position of the virtual parking segment (105 and 109, Fig. 2); and
control the one or more operations based at least on the parking assistance output (105 and 109, Fig. 2).
Hayakawa fails to specifically teach: (re claim 16) wherein the feature data comprises one or more features that include at least one of: a sign, an intersection, a color of a path surface, or a symbol on the path surface..
Nakka teaches, at 908-911, Fig. 9, determining rules related to a parking spot based on color, words and characters detected in images. This allows for such systems to determine parking restrictions based on identifying colors or signs.
In view of Nakka’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Hayakawa, (re claim 16) wherein the feature data comprises one or more features that include at least one of: a sign, an intersection, a color of a path surface, or a symbol on the path surface, with a reasonable expectation of success, since Nakka teaches, at 908-911, Fig. 9, determining rules related to a parking spot based on color, words and characters detected in images. This allows for such systems to determine parking restrictions based on identifying colors or signs.
Hayakawa fails to specifically teach: (re claim 17) the one or more processors further configured to:
filter the feature data to filter out features having a distance greater than a specified distance threshold from at least one of the ego-machine or a planned path of travel of the ego-machine.
Hayakawa teaches, at paragraph [0176], displaying close parking space images in a larger size than far away parking space images. At a certain point, parking spaces are too far away to be meaningfully represented on a display, or to be accurately detected.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the processor as taught by Hayakawa, (re claim 17) the one or more processors further configured to: filter the feature data to filter out features having a distance greater than a specified distance threshold from at least one of the ego-machine or a planned path of travel of the ego-machine, with a reasonable expectation of success, since Hayakawa teaches displaying close parking space images in a larger size than far away parking space images; and at a certain point, parking spaces are too far away to be meaningfully represented on a display, or to be accurately detected. Not wasting computational or display resources on such far away parking spaces would free up these resources for a more responsive user experience, without eliminating any data that is meaningful for a user’s current location.
Hayakawa further teaches:
Re claim 18. The one or more processors further configured to:
control a display of the ego-machine to display a first graphic for a first virtual sign corresponding to a starting boundary of the virtual parking segment and a second graphic for a second virtual sign corresponding to an ending boundary of the virtual parking segment and display a representation of the parking rule (Fig. 9).
Re claim 19. The one or more processors further configured to:
generate one or more virtual parking signs associated with one or more boundaries of the virtual parking segment based at least on the feature data, the one or more virtual parking signs comprising a first virtual parking sign at a first boundary of the virtual parking segment comprising a representation of the parking rule and a second virtual parking sign indicating a termination of the virtual parking segment at a second boundary of the virtual parking segment (102-105, Figure 2; paragraphs [0040-0041]: virtual overhead viewpoint is generated at 102, Figure 2; Figure 9 and paragraph [0180], virtual parking signs indicating parking spaces into which parking is not possible (PL2, PR3), and parking spaces into which parking is possible (PR2, PR4, PR5, PL3, PL4, and PL5).).
Re claim 20. The system of claim 13, wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (paragraph [0028]);
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for generating synthetic data using machine learning;
a system for generating multi-dimensional assets using a collaborative content creation platform;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
Re claim 21. A method comprising:
generating a parking assistance output (execute parking assist 109, Fig. 2; and paragraph [0109]) based at least on:
generating a virtual parking segment based at least on processing sensor data obtained using one or more on-board sensors of an ego-machine (detect available parking spaces, 103, Figure 2; and paragraph [0065]); and
causing performance of one or more operations to assist in navigating the ego- machine based at least on the parking assistance output (execute parking assist 109, Fig. 2; and paragraph [0109]).
Hayakawa fails to specifically teach: (re claim 21) determining a parking rule;
determining a confidence in an accuracy of the parking rule based at least on the processing of the sensor data;
associating the parking rule with the virtual parking segment based at least on the confidence exceeding a threshold.
Nakka teaches, at paragraph [0043], determining a confidence level that a parking space is an assigned parking space, and determining that a parking space is an assigned parking space if a confidence level is greater than a predetermined threshold. When the confidence level is above the threshold, the system determines the parking space is an assigned parking space; when the confidence level is below the threshold, the system determines the parking space is not an assigned parking space (default parking rule). Nakka further teaches, at 920-924, Fig. 9, parking a vehicle based on a user being permitted to park at a designated parking spot. This allows for such systems to learn an assigned parking space based on observed data.
In view of Nakka’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the method as taught by Hayakawa, (re claim 21) determining a parking rule; determining a confidence in an accuracy of the parking rule based at least on the processing of the sensor data; associating the parking rule with the virtual parking segment based at least on the confidence exceeding a threshold, with a reasonable expectation of success, since Nakka teaches determining a confidence level that a parking space is an assigned parking space, and determining that a parking space is an assigned parking space if a confidence level is greater than a predetermined threshold. When the confidence level is above the threshold, the system determines the parking space is an assigned parking space; when the confidence level is below the threshold, the system determines the parking space is not an assigned parking space (default parking rule). This allows for such systems to learn an assigned parking space based on observed data.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Hayakawa (US Publication No. 2018/0308358) as applied to claim 1 above, and further in view of Diegmann et al. (US Publication No. 2016/0343139).
The teachings of Hayakawa have been discussed above. Hayakawa fails to specifically teach: (re claim 2) wherein the processing circuitry is further configured to:
determine at least one length of the virtual parking segment based at least on a tracked motion of the ego-machine along a path traveled by the ego-machine.
Diegmann teaches, at paragraphs [0050, 0056 and 0072], and Figures 6 and 7, detecting parking areas over a plurality of frames as a vehicle travels, and identifying the start and end points of a parking area as illustrated by the markers of Figure 6, including the L-shaped markers 114 which bookend the parking area. The computing unit with parking marker detection 203 receives image data 202 from the cameras 201 and corrects the movement of the cameras based on received vehicle odometry calibration data 206 indicating the motion of the vehicle. This allows for vehicles equipped with cameras to detect parking areas around the vehicle as the vehicle travels, without being able to view an entire parking area in a single camera view.
In view of Diegmann’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Hayakawa, (re claim 2) wherein the processing circuitry is further configured to: determine at least one length of the virtual parking segment based at least on a tracked motion of the ego-machine along a path traveled by the ego-machine, with a reasonable expectation of success, since Diegmann teaches, at paragraphs [0050, 0056 and 0072], and Figures 6 and 7, detecting parking areas over a plurality of frames as a vehicle travels, and identifying the start and end points of a parking area as illustrated by the markers of Figure 6, including the L-shaped markers 114 which bookend the parking area. The computing unit with parking marker detection 203 receives image data 202 from the cameras 201 and corrects the movement of the cameras based on received vehicle odometry calibration data 206 indicating the motion of the vehicle. This allows for vehicles equipped with cameras to detect parking areas around the vehicle as the vehicle travels, without being able to view an entire parking area in a single camera view.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Hayakawa (US Publication No. 2018/0308358) as applied to claim 1 above, and further in view of Skudlarek et al. (EP 3 073 224).
The teachings of Hayakawa have been discussed above. Hayakawa fails to specifically teach: (re claim 8) wherein the processing circuitry is further configured to:
filter the feature data to filter out features outside of a trajectory manifold of the ego-machine.
Skudlarek teaches, at paragraph [0031], receiving electronic horizon data indicating possible paths of movement ahead of a vehicle on a road network and receiving sensor data. The system of Skudlarek utilizes sensor data that does not diverge from a possible path indicated by the electronic horizon data and ignores sensor data diverging from the possible paths indicated by the electronic horizon data. This reduces the amount of data that must be processed by ignoring data that is not relevant to the possible paths of movement of a vehicle.
In view of Skudlarek’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the apparatus as taught by Hayakawa, (re claim 8) wherein the processing circuitry is further configured to: filter the feature data to filter out features outside of a trajectory manifold of the ego-machine, with a reasonable expectation of success, since Skudlarek teaches, at paragraph [0031], receiving electronic horizon data indicating possible paths of movement ahead of a vehicle on a road network and receiving sensor data. The system of Skudlarek utilizes sensor data that does not diverge from a possible path indicated by the electronic horizon data and ignores sensor data diverging from the possible paths indicated by the electronic horizon data. This reduces the amount of data that must be processed by ignoring data that is not relevant to the possible paths of movement of a vehicle.
Response to Arguments
Applicant’s arguments, see pages 12-13, filed 9/2/2026, with respect to the objection to claim 14, the double patenting rejection of claims 1-21, and the 35 USC § 102 rejection of claims 1-12 have been fully considered and are persuasive. The objection to claim 14, the double patenting rejection of claims 1-21, and the 35 USC § 102 rejection of claims 1-12 has been withdrawn.
Applicant's arguments filed 9/2/2026 have been fully considered but they are not persuasive.
Applicant remarks, on pages 13-15:
Applicant respectfully submits that nothing in the asserted combination of Hayakawa and Nakka teaches or suggests "processing circuitry configured to...determine, based at least on feature data determined using the sensor data, one or more parking rules and a respective confidence in an accuracy of a determination of each of the one or more parking rules" as recited in amended claim 1. Amended claim 1 recites that the one or more parking rules and the respective confidence are determined from feature data determined using the same sensor data that is processed to determine the virtual parking segment. The cited portions of Nakka do not describe determining a confidence level from the sensor data used to detect a parking space or feature data determined using the sensor data. Instead, the cited portions of Nakka describe determining the confidence level from parameters associated with the geographic location of the parking space, such as arrival time, departure time, duration of stay, date, place type, and number of parking recurrences. See, for example, paragraphs [0042]-[0043] of Nakka. The cited portions of Nakka also describe that the place type may be determined using an external software application rather than from the sensor data used to detect the parking space. See, for example, paragraph [0042] of Nakka. Even if Hayakawa and Nakka were combined as proposed, the confidence would not be determined from feature data determined using the sensor data used to determine the virtual parking segment. Therefore, nothing in Hayakawa or Nakka, alone or in combination, teaches or suggests determining one or more parking rules and a respective confidence in an accuracy of a determination of each of the one or more parking rules based at least on feature data determined using the sensor data.
Claim 1 requires the one or more parking rules and the respective confidence are determined from feature data determined using sensor data obtained using one or more sensors of an ego-machine. Nakka suggests including a GPS or other location sensors onboard a vehicle to collect data used to determine a geographical location of a parking space, and various parameters related to timing of a vehicle at the parking space and a place type. These parameters correspond to the claimed “feature data” as they are consistent with the description of “feature data” provided at paragraph [0033] of the specification as filed: “the feature data 104 may be derived from sensor data 108 captured by one or more on-board sensors 110 of an ego-machine (e.g., ego-machine 500 of FIGs. 5A-5D).” These parameters are used to determine whether a parking space is an assigned parking space and a confidence level of this determination.
Claim 1 requires both a virtual parking segment and one or more parking rules to be determined based on sensor data obtained using one or more sensors of an ego machine, but does not require the same sub-set of sensors be used for each determination.
Applicant remarks, on pages 15-16:
Applicant respectfully submits that nothing in the asserted combination of Hayakawa and Nakka teaches or suggests "processing circuitry configured to...determine a parking rule to apply to the virtual parking segment, the parking rule including: a determined parking rule when the respective confidence in the accuracy of the determination of at least one parking rule of the one or more parking rules is above a threshold; or a default parking rule when the respective confidence in the accuracy of the determination of each of the one or more parking rules is below the threshold" as recited in amended claim 1. The cited portions of Nakka describe a confidence level that a parking space is an assigned parking space, which is used during a process for learning whether a parking space is an assigned parking space for the vehicle. See, for example, FIG. 5 and paragraph [0043]. Even if the determination that a parking space is an assigned parking space were treated as determining a parking rule, the confidence level in Nakka is a confidence regarding whether the space is an assigned space, not a confidence in the accuracy of a determination of a parking rule. Nakka describes determining one or more rules based on mapping an extracted color or context to a stored rule using a stored data structure, but there is no description of using a confidence in the accuracy of the determination of the rule. See, for example, FIG. 9 and paragraphs [0060]-[0062] of Nakka. Furthermore, in Nakka, when the confidence level is below the threshold, the parking space is determined not to be an assigned parking space, and the method returns to extracting an identifier from one or more captured images. See, for example, FIG. 5 and paragraphs [0043]-[0044] of Nakka. Nakka does not describe applying a default parking rule when the respective confidence in the accuracy of the determination of each of the one or more parking rules is below a threshold. Therefore, nothing in Hayakawa or Nakka, alone or in combination, teaches or suggests the parking rule including a determined parking rule when the respective confidence in the accuracy of the determination of at least one parking rule of the one or more parking rules is above a threshold, or a default parking rule when the respective confidence in the accuracy of the determination of each of the one or more parking rules is below the threshold, as recited in amended claim 1.
The Examiner asserts that the confidence level that the parking space is an assigned parking space is a confidence in the accuracy of a determination of a parking rule. The parking space being an assigned space is a rule regarding that parking space, and the confidence level of Nakka’s determination that a space is an assigned space is a calculation reflecting how sure the system of Nakka is that a space has been accurately identified as an assigned parking space.
By stating, at paragraph [0043], “it is determined that the parking space is an assigned parking space if a confidence level is greater than a predetermined threshold,” Nakka implicitly describes applying a default parking rule of ‘not an assigned parking space’ when the confidence level is not greater than a predetermined threshold.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SPENCER D PATTON/ Primary Examiner, Art Unit 3656
