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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification (MPEP 608.01, ¶6.31).
Claim Objections
Claim 1 is objected to because of the following informalities:
Amended claim 1 recites ”An vehicle comprising”, which should be “A vehicle comprising”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
Applicant’s amendments sufficiently integrate the idea into a practical application, and thus are considered eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US20230382371A1) as applied to claim 1 and further in view of Iagnemma (US20180113457A1) and RideFlux (US202200730099A1).
Regarding claim 1, Park teaches;
An vehicle (taught as a vehicle, element 100) comprising:
at least one sensor (taught as a sensor, element 110, such as camera, paragraph 0032, LIDAR, paragraph 0034, or RADAR, paragraph 0036) configured to detect surrounding environment of the vehicle and to generate surrounding environment information (taught as camera, paragraph 0032, LIDAR, paragraph 0034, and RADAR being used to detect other objects, paragraph 0036);
a processor (taught as a processor, element 130), during autonomous driving of the vehicle, configured to generate vehicle state information by monitoring a state of the vehicle (taught as the processor performing determination related to the control of the vehicle, paragraph 0045, such as by monitoring the state around the vehicle, paragraph 0072), and to determine whether a minimum risk maneuver (MRM) is required based on at least one of the surrounding environment information and the vehicle state information (taught as the processor generating a request for minimal risk maneuver [MRM] in accordance to the detection result, paragraph 0072); and
a controller (taught as a controller, element 120) configured to control operations of the vehicle under the control of the processor (taught as the controller controlling the driving of the vehicle according to the processor, paragraph 0041), wherein the processor is further configured to:
determine, based on a determination that the MRM is required, determine an MRM type (taught as selecting a type of MRM, Fig 3 S130, paragraph 0075),
recognize, based on the surrounding environment information, a shoulder area (taught as recognizing a shoulder using the sensors, paragraph 0088);
based on the determined MRM type being a shoulder stop (taught as a shoulder stop, Fig 11 level 4, paragraph 0152),
generate, based on the determined score of each of the virtual areas at least one stop position candidate group (taught as determining a safety zone in which the vehicle can safely stop when performing the MRM, paragraph 0200, Fig 15) and
control the vehicle to follow a path (taught as controlling the subject vehicle in performing the MRM, e.g. paragraph 0138) to the at least one stop position candidate group (taught as controlling the vehicle to travel to and stop in a safety zone, paragraph 0206).
However, Park does not explicitly teach; partition the shoulder area into a plurality of virtual areas;
based on the determined MRM type being a shoulder stop, determine, based on a speed of the vehicle and based on a free space on the shoulder area, a score of each of the virtual areas;
generate, based on the determined score of each of the virtual areas at least one stop position candidate group.
Iagnemma teaches; partition the shoulder area into a plurality of virtual areas (taught as defining a subset of regions/points in the proximity region to be a goal region, paragraph 0089, and further defining sub-regions of the goal region, paragraph 0117),
based on the determined MRM type being a shoulder stop, determine, [[based on a speed of the vehicle]] and based on a free space on the shoulder area, a score of each of the virtual areas (taught as filtering a proximity region down to a goal region based on an acceptable stopping area criteria [e.g. a binary yes, able to stop, or no, unable to stop], paragraph 0089, and further ranking sub-regions of the goal region, paragraph 0117, based on the nature of the activity, the autonomous vehicle, measure of desirability etc. paragraph 0118);
generate, based on the determined score of each of the virtual areas at least one stop position candidate group (taught as determining a stopping place in the goal region based on the feasibility, desirability, and optimization algorithms of the trajectory planning process, paragraph 0133).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
However, Iagnemma does not explicitly teach; determine, based on a speed of the vehicle, a score of each of the virtual areas.
RideFlux teaches; determine, based on a speed of the vehicle a score of each of the virtual areas (taught as calculating scores for candidate driving plans according to speed profiles to finalize a driving plan, paragraph 0057).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate speed considerations into the scoring of candidates as taught by RideFlux in the system taught by Park as modified by Iagnemma in order to improve selection of maneuvers. Such speed considerations/profiles allow for better personalization. For example, RideFlux suggests that, without such profiles, tendencies and characteristics of a driver or passenger in the autonomous vehicle cannot be taken into consideration (paragraph 0007).
Regarding claim 3, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 1 (see claim 1 rejection). However, Park does not explicitly teach; wherein the processor is configured to assign a predetermined score to a virtual area, of the plurality of virtual areas, comprising an obstacle.
Iagnemma teaches; wherein the processor is configured to assign a predetermined score to a virtual area, of the plurality of virtual areas, comprising an obstacle (taught as considering spaces with parked vehicles or other obstacles as not being a feasible stopping space, paragraph 0062, e.g. a low score akin to a ‘0’)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 4, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 1 (see claim 1 rejection). However, Park does not explicitly teach; wherein the processor is configured to:
match a virtual window area corresponding to a size of the vehicle to the plurality of virtual areas; and
generate the at least one stop position candidate group based on a value obtained by summing the scores of the respective virtual areas included in the virtual window area.
Iagnemma teaches; match a virtual window area corresponding to a size of the vehicle to the plurality of virtual areas (taught as considering the vehicle footprint area, where regions smaller than the footprint are not considered viable stopping locations, paragraph 0094); and
generate the at least one stop position candidate group based on a value obtained by summing the scores of the respective virtual areas included in the virtual window area (taught as combining all the criteria for a stopping candidate location into a rank, wherein a higher rank is more desirable than a lower rank, paragraph 0117, and selecting a stopping place based on the desirability of the location, paragraph 0133).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 5, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 1 (see claim 1 rejection). However, Park does not explicitly teach; wherein the processor is configured to: after generating the at least one stop position candidate group, generate a path from a current position of the vehicle to each stop position candidate of the at least one stop position candidate group.
Iagnemma teaches; after generating the at least one stop position candidate group, generate a path from a current position of the vehicle to each stop position candidate of the at least one stop position candidate group (taught as trajectory planning, identifying trajectories from the current position to a stopping place, paragraph 0130, wherein trajectories/paths are checked against feasibility/viability of stopping places, paragraph 0095).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 6, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 5 (see claim 5 rejection). However, Park does not explicitly teach; wherein the processor is configured to:
select a final stop position based on at least one of: a travel distance of the path from the current position to each stop position candidate, a stop characteristic of the vehicle after following the path from the current position to each stop position candidate, or values obtained by adding scores of virtual areas of an area occupied by the vehicle after following the path from the current position to each stop position candidate.
Iagnemma teaches; select a final stop position based on at least one [interpreted to mean only one criteria is required] of (taught as selecting the stopping place in the goal region): a travel distance of the path from the current position to each stop position candidate (taught as filtering a proximity region down to a goal region based on an acceptable stopping area criteria [e.g. a binary yes, able to stop, or no, unable to stop], paragraph 0089, and determining a desirability based on distance to a goal position, paragraph 0106), a stop characteristic [interpreted to mean a feature of a stop involving the positioning, angle or other constraint, such as exemplified in page 45 of the specification] of the vehicle in the stopping position of the vehicle after following the path from the current position to each stop position candidate (taught a determining desirability of a stopping location, based on further characteristics of a location such as a sightline, paragraph 0108, distance to the curb, paragraph 0108, or type of road, paragraph 0110), or values obtained by adding scores of virtual areas of an area occupied by the vehicle after following the path from the current position to each stop position candidate (taught as selecting the stopping place in the goal region based on the feasibility, relative desirability, and optimization objective, paragraph 0133, such as the most desirable [highest cumulative score] position, paragraph 0136).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 7, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 6 (see claim 6 rejection). However, Park does not explicitly teach; wherein the processor is configured to generate, based on stored map data, a first selection score for each travel distance that is based on the path from the current position to a respective stop position candidate.
Iagnemma teaches; generate, based on stored map data, a first selection score for each travel distance that is based on the path from the current position to a respective stop position candidate (indicated in the initial proximity search being within a certain configurable distance, paragraph 0054, where positions outside the region are not considered [low/no score]).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 8, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 7 (see claim 7 rejection). However, Park does not explicitly teach; wherein the processor is configured to generate, based on the stored map data, a second selection score for each stop characteristic of the vehicle after following the path from the current position to a respective stop position candidate.
Iagnemma teaches; generate, based on the stored map data, a second selection score for each stop characteristic of the vehicle after following the path from the current position to a respective stop position candidate (taught as a generalized cost/utility function for stopping places to normalize desirability factors, paragraph 0114, including criteria based on the type of stopped activity).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 9, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 8 (see claim 8 rejection). However, Park does not explicitly teach; wherein the processor is configured to: generate, for a respective stop position candidate, a final score based on the first selection score, the second selection score, and a third selection score that is obtained by adding the scores of the respective virtual areas of an area occupied by the vehicle after following the path from the current position to a respective stop position candidate, and select, as the final stop position, a stop position candidate having a highest final score among the final scores of the respective stop position candidates.
Iagnemma teaches; generate, for a respective stop position candidate, a final score based on the first selection score, the second selection score, and a third selection score that is obtained by adding the scores of the respective virtual areas of an area occupied by the vehicle after following the path from the current position to a respective stop position candidate(taught as a generalized cost/utility function for stopping places to normalize desirability factors, paragraph 0114, including criteria based on the type of stopped activity), and
select, as the final stop position, a stop position candidate having a highest final score among the final scores of the respective stop position candidates (taught as selecting the stopping place in the goal region based on the feasibility, relative desirability, and optimization objective, paragraph 0133, such as the most desirable [highest cumulative score] position, paragraph 0136).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 10, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 6 (see claim 6 rejection). Park further teaches; wherein the processor is configured to transmit, to the controller, a path-following control command [[for the final stop position]] (taught as initiating a minimal risk maneuver, paragraph 0063). However, Park does not explicitly teach; a path-following control command for the final stop position.
Iagnemma teaches; wherein the processor is configured to transmit, to the controller, a path-following control command for the final stop position (taught as the autonomous system executing the trajectory planning process to route the vehicle to the selected stopping place, paragraph 0130).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claim 11, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 10 (see claim 10 rejection). However, Park does not explicitly teach; wherein the processor is configured to generate, based on a determination that the vehicle fails to arrive at the final stop position within a preset time, a stop position candidate group for changing the stop position.
Iagnemma teaches; wherein the processor is configured to generate, based on a determination that the vehicle fails to arrive at the final stop position within a preset time, a stop position candidate group for changing the stop position (taught as determining whether the vehicle is unable to stop within a specified amount of time, and adopting a strategy as a result, paragraph 0143, including redoing the stop selection process, paragraph 0145).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a stop candidate position as taught by Iagnemma in the system taught by Park to improve safety and desirability of a stopping location. As suggested by Iagnemma, such a system allows for the consideration of the legality, desirability and feasibility of stopping positions (paragraph 0027). In other words, one of ordinary skill in the art would think to apply the more specific stopping location selection taught by Iagnemma in the autonomous risk maneuver system taught by Park in order to more explicitly and effectively stop the vehicle according to optimization criteria.
Regarding claims 12 and 14-20, it has been determined that no further limitations exist apart from those previously addressed in claims 1 and 3-11. Therefore, claims 1 and 14-20 are rejected under the same rationale as claims 1 and 3-11, wherein claim 12 corresponds to claim 1, claim 14 corresponds to claim 4, claims 15-17 correspond to claims 5-7, claim 18 corresponds to claim 9, and claims 19-20 correspond to 10-11 respectively.
Claim(s) 21 is rejected under 35 U.S.C. 103 as being unpatentable over Park (US20230382371A1) as modified by Iagnemma (US20180113457A1) and RideFlux (US202200730099A1), and further in view of Kang (US20240001960A1).
Regarding claim 1, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 1 (see claim 1 rejection). However, Park does not explicitly teach; wherein the processor is configured to determine the score of each of the virtual areas by:
determining, based on an angle between a direction of the vehicle at a time that the vehicle stops and a traveling direction of the vehicle, a stop characteristic of the vehicle; and
assigning the score based on the angle, wherein a smaller angle between the direction of the vehicle and the traveling direction correlates to a higher score.
Kang teaches; determining, based on an angle between a direction of the vehicle at a time that the vehicle stops and a traveling direction of the vehicle, a stop characteristic of the vehicle (taught as determining a score for a candidate stop position based on the angle/posture of the vehicle in the stop location; for example, a bus station, paragraph 0165;)
assigning the score based on the angle, wherein a smaller angle between the direction of the vehicle and the traveling direction correlates to a higher score (taught as oblique angles against the stop location are effectively penalized/scored worse than being parallel, paragraph 0164).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to consider and score angled stopping locations as taught by Kang in the system taught by Park in order to better optimize potential stopping position selection. As suggested by Kang, stopping points often have various obstacles that need to be accounted for (paragraph 0005), and including prioritization of different angle conditions of a selection of stopping locations (paragraph 0023)helps ensure selection of the most suitable stopping location (paragraph 0030).
Claim(s) 22 is rejected under 35 U.S.C. 103 as being unpatentable over Park (US20230382371A1) as modified by Iagnemma (US20180113457A1) and RideFlux (US202200730099A1), and further in view of Kazemi (US20190235499A1).
Regarding claim 22, Park as modified by Iagnemma and RideFlux teaches;
The vehicle of claim 1 (see claim 1 rejection). However, Park does not explicitly teach; wherein the processor is configured to determine the score of each of the virtual areas by:
assigning a higher score to a virtual area, of the virtual areas, having a longer stopping distance relative to the speed of the vehicle [interpreted to effectively indicate a size of the stopping area, such that larger stopping areas are more desirable].
Kazemi teaches; assigning a higher score to a virtual area, of the virtual areas, having a longer stopping distance relative to the speed of the vehicle (taught as cost functions including a minimum stopping distance of the vehicle, paragraph 0032, which is further modified by minimizing the required deceleration, paragraph 0058; in combination, the cost function would effectively optimize for longer stopping distances relative to vehicle speed by minimizing a required deceleration).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to prioritize minimized required deceleration as taught by Kang in the system taught by Park in order to better optimize stopping position selection. As taught by Kazemi, such an optimization helps create a more comfortable stop for passengers of the vehicle (paragraph 0058).
Response to Arguments
The applicant argues that the amendments to the claims overcome the previous rejections.
The examiner agrees, and withdraws the previous rejection. However, new rejections in light of RideFlux have been made above to address the deficiencies of Park and Iagnemma.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For further stopping location planning; US11181921B2, US12071162B2
For further safety maneuver evaluations akin to MRM; US11726492B2, US20190235499A1
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL ANFINRUD whose telephone number is (571)270-3401. The examiner can normally be reached M-F 9:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at (571)270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GABRIEL ANFINRUD/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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