Detailed Action
This Office Action is taken in response to Applicant’s Amendment and Remarks filed on 12/10/2025 regarding Application No. 18/178,725 originally filed on 03/06/2023. Claims 1-2, 4-9, 11-16, 18-20 as filed are currently pending and have been considered as follows:
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 .
Response to Arguments
The applicant argues “Christie and Di fail to teach or suggest at least ‘assigning a confidence value to the second label, wherein the confidence level indicates whether colliding with the obstacle would constitute safe driving,’ …” and further argues that Tao “fails to cure the deficiencies of Christie and/or Di” because Tao’s “confidence score” is directed to confidence in status/trajectory prediction rather than the claimed “safe driving” collision acceptability concept. (Remarks, p. 11-13). The examiner respectfully disagrees.
Ferguson expressly discloses associating a confidence value with whether an object is safe for the vehicle to drive over or not. (as per “Each classification may be provided with a confidence value indicative of the likelihood that the object is safe for the vehicle to drive over or not.” in ¶21, as per “The computing device 110 may then use the classification … and associated confidence value … to make a determination as to whether it is safe or not for the vehicle to drive over the object in real time …” in ¶60). Ferguson further discloses that when an object is classified as drivable, “the vehicle may proceed to drive over the object,” and when not drivable, “the vehicle may stop or maneuver around the object.” (as per ¶22). Driving over an object in the vehicle’s path inherently involves contact/collision, and Ferguson’s confidence value is explicitly tied to whether doing so is safe. Thus, Ferguson teaches the amended clause “wherein the confidence level indicates whether colliding with the obstacle would constitute safe driving.” Applicant’s traversal is therefore unpersuasive.
The applicant argues Tao “describes changing a trajectory course of a vehicle when the trajectory of an object is relatively known… It is NOT a description of accepting or not accepting one or more trajectory plans based on an obstacle confidence value.” (Remarks, p. 12-13). The examiner respectfully disagrees.
Tao expressly discloses comparing obstacle confidence to threshold criteria to decide whether to adjust/maintain the ADV path. (as per “the ADV can determine whether or not to adjust the ADV's path based on whether the confidence score of the obstacle satisfies some threshold criteria.” in C10L25-35). Tao further discloses that where confidence is high the ADV may “alter the route” to prevent negative interactions (e.g., collision) and where confidence is low the ADV may “choose not to alter the route.” (as per “Where the point confidence scores are low, the ADV can choose not to alter the route, but where point confidence scores are high, then the ADV can alter the route…” in C2L39-44). These are alternative planning outcomes that correspond to not accepting one or more collision-involving plans versus accepting a current/maintained plan, responsive to a confidence metric and threshold criteria. Moreover, Di teaches selectively adopting avoidance measures depending on obstacle risk (e.g., avoiding for large/damaging obstacles versus not adopting avoidance measures for low-risk obstacles). (as per P4¶10-¶13). Therefore, the applicant’s traversal is unpersuasive.
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) 1-5, 7-12, 14-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Christie (WO Pub. No. 2023/025777) in view of Di (CN Pub. No. 115728782) in view of Tao (US Pat. No. 10928820) in further view of Ferguson (US Pub. No. 20180032078).
As per Claim 1, Christie discloses of a method using sensor fusion of radar, lidar, camera systems, the method comprising:
generating one or more data points from one or more sensors coupled to a vehicle (as per "combining the output from the sensors shown in figure 1 ( e.g. using the system shown in figure 2), the fused sensor system can be obtained." in P10L25-30), wherein:
the one or more sensors comprise:
a Light Detection and Ranging (LiDAR) sensor; (as per "Current sensors typically employed in autonomous systems or advanced driver-assistance systems (ADAS) in vehicles typically include one or more of: radar, LIDAR, and Stereo Cameras (or a camera array)." in P2L20-30)
a camera, (as per "Current sensors typically employed in autonomous systems or advanced driver-assistance systems (ADAS) in vehicles typically include one or more of: radar, LIDAR, and Stereo Cameras (or a camera array)." in P2L20-30)
the one or more data points comprise:
a LiDAR point cloud generated by the LiDAR sensor; (as per "To put it differently, the LIDAR is used to scan a wide area and the data from the LIDAR is fused with point cloud data from the fast, long-range radar and the camera array before being analyzed in the infrastructure model 8210." in P15L30-35, P16L1-5)
an image captured by the camera; (as per "a fast scan radar and camera array technology are used as the platform backbone" in P13L20-25)
using a processor:
detecting one or more obstacles within the LiDAR point cloud; (as per “At this point partial object identification can be performed. Object identification may be based on the number, or the relative shape, of reflected signals returned from an area." in P13L1-10, as per "Detection using radar sensors therefore provides more time to take action or alert the driver upon the detection of an obstacle ahead." in P13L30-35,P14L1-5)
generating at least one patch in the LiDAR point cloud indicative of the one or more obstacles; (as per “the LIDAR is operated to process scans over a narrow area and is focused on a small angular region around the detected object(s).” in P16L5-10, as per “When a number of points are detected in the same region (angle, radial range and velocity) of each sensor, they are clustered into blocks.” in P19L20-25)
projecting the at least one patch of the LiDAR point cloud onto the image to obtain combined data, (as per “This optical data may be fused with the radar data 8130. An AI or machine learning engine can be used to fuse the radar data and the optical data.” in P14L20-30, as per “the LIDAR is used to scan a wide area and the data from the LIDAR is fused… with point cloud data from the fast, long-range radar and the camera array before being analysed in the infrastructure model 8210.” in P15L30-34 & P16L1-5) wherein said at least one patch designates a region of the image that is to be analyzed (as per “This fused point cloud can then be added to the 15 dynamic object model 8220 for object identification and tracking, and to further enhance the infrastructure model using engine 8210.” In P16L10-20)
performing a factor query using the combined data to query at least one factor feature for points of the at least one patch associated with each of the one or more obstacles; (as per "an object is classified by accessing a predefined classification based on one or more of the detected object's size, shape, and colour (from the camera array)." in P20L4-10, as per “selecting a classification for the object from a plurality of classifications.” In P6L10-15)
Christie fails to expressly disclose:
for each obstacle of the one or more obstacles, based on the at least one factor feature and a first label indicating an object type of a plurality of object types,
labeling the obstacle with a second label, the second label being assigned a confidence value,
wherein the confidence level indicates whether colliding with the obstacle would constitute safe driving;
based on the first and second labels of the one or more obstacles, planning a trajectory of the vehicle, the planning of the trajectory comprising:
comparing the confidence value to a threshold value:
accepting one or more plans that would collide with the one or more obstacles, when the confidence value is above the threshold value; and
not accepting one or more plans that would collide with the one or more obstacles, when the confidence value is below the threshold value.
Di discloses judging danger of an obstacle, comprising:
for each obstacle of the one or more obstacles, based on the at least one factor feature and a first label indicating an object type of a plurality of object types, (as per “after detecting the obstacle, the obstacle is primarily classified by conventional algorithm, such as cone, pedestrian, bicycle and so on” in P3¶16-¶18, as per “For example, a plastic bag flee from the front face, and a stone block, the damage to the vehicle is completely different. If they are not distinguished, the same avoidance measures are taken to all obstacles, which may cause accidental and unnecessary damage to the vehicle.” in P1¶5-¶8, as per “It should be noted that if the motion track of the obstacle is straight line or parabolic shape, it is determined that the obstacle quality is large, may cause large damage to the vehicle, at this time, avoiding the obstacle; if the motion track of the obstacle is irregular shape, the barrier quality is small, it may cause small damage to the vehicle or no damage, at this time, it can not adopt avoiding measures.” in P4¶10-¶13)
labeling the obstacle with a second label, (as per “if the first judgment, the second judgment, the third judgment in any one of judging the positive result, then judging the obstacle is high risk obstacle.” in Claim 2)
based on the first (as per “classifying the obstacle, obtaining the classification result;” in Abstract) and second labels of the one or more obstacles (as per “if the first judgment, the second judgment, the third judgment in any one of judging the positive result, then judging the obstacle is high risk obstacle.” in Claim 2), planning a trajectory of the vehicle. (as per “sending out high risk pre-warning, and/or sending control instruction of avoiding the obstacle.” in Claim 3)
accepting one or more plans that would collide with the one or more obstacles, (as per “It should be noted that if the motion track of the obstacle is straight line or parabolic shape, it is determined that the obstacle quality is large, may cause large damage to the vehicle, at this time, avoiding the obstacle; if the motion track of the obstacle is irregular shape, the barrier quality is small, it may cause small damage to the vehicle or no damage, at this time, it can not adopt avoiding measures.” in P4¶10-¶13, as per “if the first judgment, the second judgment, the third judgment in any one of judging the positive result, then judging the obstacle is high risk obstacle.” in Claim 2)
In this way, Di is concerned with determining risk of an obstacle to a vehicle (P1¶6). Like Christie, Di is concerned with vehicle systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the automotive sensor fusion by Christie with the obstacle risk judgement as taught by Di to enable another standard means determining the risk of an obstacle to a vehicle (P1¶4-¶8). Such modification also allows the system to determine the risk difference between a plastic bag and a stone block and act accordingly (P¶4-¶8).
Christie and Di fail to expressly disclose:
the second label being assigned a confidence value
wherein the confidence level indicates whether colliding with the obstacle would constitute safe driving;
the planning of the trajectory comprising:
comparing the confidence value to a threshold value:
when the confidence value is above the threshold value; and
not accepting one or more plans that would collide with the one or more obstacles, when the confidence value is below the threshold value.
Tao discloses of confidence levels along a predicted trajectory of an obstacle, comprising:
the second label being assigned a confidence value (as per “the process includes determining a current status confidence score (also referred to as confidence level) associated with a current status of a moving obstacle, the current status of the moving obstacle including one or more of speed, location, heading, acceleration, or a type of the moving obstacle” in C9L30-40, as per “determining a trajectory confidence score associated with the trajectory of the moving obstacle, the trajectory confidence score being determined based on the current status confidence score and the current status of the moving obstacle.” in C10L5-15)
the planning of the trajectory comprising:
comparing the confidence value to a threshold value (as per “the ADV can determine whether or not to adjust the ADV's path based on whether the confidence score of the obstacle satisfies some threshold criteria.” in C10L25-35):
when the confidence value is above the threshold value; (as per “where point confidence scores are high, then the ADV can alter the route to prevent possible negative interactions (such as a collision) with the obstacle.” in C2L40-45, as per “the ADV can determine whether or not to adjust the ADV's path based on whether the confidence score of the obstacle satisfies some threshold criteria” in C10L25-35, as per “Each point confidence score of a trajectory can be determined based on a) using the trajectory confidence score of the trajectory as a baseline, and b) one or more environmental factors… at least one environmental factor includes one or more of a) a distance between a respective point and a starting location of the moving obstacle, b) the traffic rules, or c) the map data.” in C13L10-25)
not accepting one or more plans that would collide with the one or more obstacles, when the confidence value is below the threshold value. (as per “Where the point confidence scores are low, the ADV can choose not to alter the route, but where point confidence scores are high, then the ADV can alter the route to prevent possible negative interactions (such as a collision) with the obstacle” in C2L40-45)
In this way, Tao operates to determine multiple confidence scores along a single predicted trajectory of a moving obstacle (C2L15-20). Like Christie and Di, Tao is concerned with vehicle systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the automotive sensor fusion by Christie and the obstacle risk judgement by Di with the obstacle confidence levels as taught by Tao to enable another standard means determining a current confidence score/level with a status of a moving obstacle (C9L30-40).
Christie, Di, and Tao fail to expressly disclose:
wherein the confidence level indicates whether colliding with the obstacle would constitute safe driving;
Ferguson discloses of determining drivability of objects for autonomous vehicles, comprising:
wherein the confidence level indicates whether colliding with the obstacle would constitute safe driving; (as per “Each classification may be provided with a confidence value indicative of the likelihood that the object is safe for the vehicle to drive over or not. This confidence value may be compared with a threshold value to determine whether the object is to be classified as drivable or not drivable” in ¶21, as per “each classification will be associated with a confidence value. This confidence value provides an accuracy estimate for the actual classification. For instance, sensor information defining characteristics of an object, such as the shape, height or other dimensions, location, speed, color, object type, etc. of a squirrel, depending on the classification designations for the classifier, the output of the classifier may be that the object is 0.05 or 5% likely to be drivable, 0.95 or 95% likely to be not drivable, and 0.8 or 80% not drivable but likely to move away on its own” in ¶30, as per “For instance, as an example, if an object is identified as drivable, the vehicle may proceed to drive over the object. Alternatively, if an object is classified as not drivable, the vehicle may stop or maneuver around the object” in ¶22)
In this way, Ferguson operates to determine drivability of an object by associating a confidence value with whether an object is safe for the vehicle to drive over or not (i.e., “a confidence value indicative of the likelihood that the object is safe for the vehicle to drive over or not” in ¶21) and using the classification and associated confidence value “to make a determination as to whether it is safe or not for the vehicle to drive over the object in real time” (¶60). Like Christie, Di, and Tao, Ferguson is concerned with vehicle systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Christie, Di, and Tao with the obstacle drivability confidence determination as taught by Ferguson to enable another standard means determining whether colliding with the obstacle would constitute safe driving (¶21, ¶60). Such modification also allows the system to differentiate between objects which the vehicle must drive around to avoid an accident versus objects which the vehicle can drive over, thereby avoiding unnecessary maneuvering or stopping for objects that are safe to drive over and improving overall safety (¶16).
As per Claim 2, the combination of Christie, Di, Tao, and Ferguson teaches or suggests all limitations of Claim 1. Christie further discloses wherein the plurality of object types comprise:
a piece of vegetation; (as per "Visibility - trees, buildings etc. (from the infrastructure model)" in P21L31-35)
a pedestrian; (as per "performing a continuous micro-doppler analysis from the radar to determine small movements (such as arm or leg movement), the object type is further
refined to indicate, for example, a higher probability that the object is a pedestrian" in P20L25-35)
not a pedestrian; and/or(as per "Semantic segmentation (which is also referred to as Infrastructure modelling), 5 while not being a safety critical function, enables classification of the objects which appear in the immediate surroundings of the vehicle. For example, semantic segmentation allows the system to classify objects as road kerbs, fencing, side walls, road markings, signage etc." in P15L1-10)
a vehicle. (as per "object classification is performed using a predefined lookup table of vehicle types." in P20L1-5)
As per Claim 4, the combination of Christie, Di, Tao, and Ferguson teaches or suggests all limitations of Claim 1. Christie fails to expressly disclose wherein the planning the trajectory comprises:
using the processor:
for each of the one or more obstacles, based on the second label of the obstacle, determining one or more vehicle actions for the vehicle to perform;
causing the vehicle to perform the one or more actions.
See Claim 1 for teachings of Di. Di further discloses wherein the planning the trajectory comprises:
using the processor:
for each of the one or more obstacles, based on the second label of the obstacle, determining one or more vehicle actions for the vehicle to perform; (as per “It should be noted that if the motion track of the obstacle is straight line or parabolic shape, it is determined that the obstacle quality is large, may cause large damage to the vehicle, at this time, avoiding the obstacle… if there is no deformation in the barrier movement process, the barrier hardness is large, the damage of the vehicle is large;” in P4¶11, as per “finally, summing the score, if the total score is equal to 0, considering the obstacle risk is small, otherwise, considering the barrier risk is large, immediately sending risk pre-warning to the safety guard, or sending control instruction of the obstacle avoidance, to automatically control the carrier avoiding” in P4¶20)
causing the vehicle to perform the one or more actions. (as per “sending out high risk pre-warning, and/or sending control instruction of avoiding the obstacle.” in Claim 3)
In this way, Di is concerned with determining risk of an obstacle to a vehicle (P1¶6). Like Christie, Tao, and Ferguson, Di is concerned with vehicle systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Christie, Tao, and Ferguson with the obstacle risk judgement as taught by Di to enable another standard means determining the risk of an obstacle to a vehicle (P1¶4-¶8). Such modification also allows the system to determine the risk difference between a plastic bag and a stone block and act accordingly (P¶4-¶8).
As per Claim 5, the combination of Christie, Di, Tao, and Ferguson teaches or suggests all limitations of Claim 4. Christie further discloses wherein the one or more actions comprises one or more of:
planning a path of the vehicle; (as per "and if the risk classification meets a predetermined criterion, switching to a third mode wherein a vehicle safety sensor system controls the vehicle to avoid the detected object." in P8L25-35)
increasing a speed of the vehicle;
decreasing a speed of the vehicle; (as per "giving an early warning to drivers to be cautious, and enabling them to safely slow down without causing any undue risk." in P27L14-20)
stopping the vehicle; (as per "The 5D perception engine may then take preventative action based on this determination - for example warning the driver early of the potential danger or by employing automatic braking or other measures to slow or stop the vehicle." in P23L20-25)
adjusting a trajectory of the vehicle. (as per "and if the risk classification meets a predetermined criterion, switching to a third mode wherein a vehicle safety sensor system controls the vehicle to avoid the detected object." in P8L25-35)
As per Claim 7, the combination of Christie, Di, Tao, and Ferguson teaches or suggests all limitations of Claim 1. Christie further discloses wherein the performing the factor query comprises:
performing a color query on the image for each of the one or more obstacles; (as per “an object is classified by accessing a predefined classification based on one or more of the detected object's size, shape, and colour (from the camera array).” in P20L4-10)
performing a shape query on the image for each of the one or more obstacles; (as per “an object is classified by accessing a predefined classification based on one or more of the detected object's size, shape, and colour (from the camera array).” in P20L4-10)
performing a movement query on the image for each of the one or more obstacles. (as per "performing a continuous micro-doppler analysis from the radar to determine small movements (such as arm or leg movement), the object type is further refined to indicate, for example, a higher probability that the object is a pedestrian" in P20L25-35)
Claims 8 and 15 are rejected using the same rationale, mutatis mutandis, applied to Claim 1 above, respectively.
Claims 9 and 16 are rejected using the same rationale, mutatis mutandis, applied to Claim 2 above, respectively.
Claims 10 and 17 are rejected using the same rationale, mutatis mutandis, applied to Claim 3 above, respectively.
Claim 11 is rejected using the same rationale, mutatis mutandis, applied to Claim 4 above, respectively.
Claim 12 is rejected using the same rationale, mutatis mutandis, applied to Claim 5 above, respectively.
Claims 14 and 20 are rejected using the same rationale, mutatis mutandis, applied to Claim 7 above, respectively.
As per Claim 18, the combination of Christie, Di, Tao, and Ferguson teaches or suggests all limitations of Claim 15. Christie further discloses:
wherein the one or more actions comprises one or more of:
planning a path of the vehicle; (as per "and if the risk classification meets a predetermined criterion, switching to a third mode wherein a vehicle safety sensor system controls the vehicle to avoid the detected object." in P8L25-35)
increasing a speed of the vehicle;
decreasing a speed of the vehicle; (as per "giving an early warning to drivers to be cautious, and enabling them to safely slow down without causing any undue risk." in P27L14-20)
stopping the vehicle; (as per "The 5D perception engine may then take preventative action based on this determination - for example warning the driver early of the potential danger or by employing automatic braking or other measures to slow or stop the vehicle." in P23L20-25)
adjusting a trajectory of the vehicle. (as per "and if the risk classification meets a predetermined criterion, switching to a third mode wherein a vehicle safety sensor system controls the vehicle to avoid the detected object." in P8L25-35)
Christie fails to expressly disclose wherein the planning the trajectory comprises:
for each of the one or more obstacles, based on the second label of the obstacle, determining
one or more vehicle actions for the vehicle to perform; and
causing the vehicle to perform the one or more actions, and
See Claim 17 for teachings of Di. Di further discloses wherein the planning the trajectory comprises:
for each of the one or more obstacles, based on the second label of the obstacle, determining one or more vehicle actions for the vehicle to perform; (as per “It should be noted that if the motion track of the obstacle is straight line or parabolic shape, it is determined that the obstacle quality is large, may cause large damage to the vehicle, at this time, avoiding the obstacle… if there is no deformation in the barrier movement process, the barrier hardness is large, the damage of the vehicle is large;” in P4¶11, as per “finally, summing the score, if the total score is equal to 0, considering the obstacle risk is small, otherwise, considering the barrier risk is large, immediately sending risk pre-warning to the safety guard, or sending control instruction of the obstacle avoidance, to automatically control the carrier avoiding” in P4¶20)
causing the vehicle to perform the one or more actions. (as per “sending out high risk pre-warning, and/or sending control instruction of avoiding the obstacle.” in Claim 3)
In this way, Di is concerned with determining risk of an obstacle to a vehicle (P1¶6). Like Christie, Tao, and Ferguson, Di is concerned with vehicle systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Christie, Tao, and Ferguson with the obstacle risk judgement as taught by Di to enable another standard means determining the risk of an obstacle to a vehicle (P1¶4-¶8). Such modification also allows the system to determine the risk difference between a plastic bag and a stone block and act accordingly (P¶4-¶8).
Claim(s) 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Christie (WO Pub. No. 2023/025777) in view of Di (CN Pub. No. 115728782) in view of Tao (US Pat. No. 10928820) in view of Ferguson (US Pub. No. 20180032078) in further view of Singh (US Pub. No. 2022/0388535).
As per Claim 6, the combination of Christie, Di, Tao, and Ferguson teaches or suggests all limitations of Claim 1. Christie further discloses wherein each patch forms a bounding box on the image (as per Fig. 7) and performing the factor query comprises performing the factor query on the resized image. (as per “wide area scanning LIDAR processes described above are halted. Instead, the LIDAR is operated to process scans over a narrow area and is focused on a small angular region around the detected object(s). In one embodiment of the invention, the narrow area or narrow field of view comprises an angular region less than 20 degrees around 10 the object… By concentrating in a narrower region, the radar outputs and the high-resolution camera images can be processed more quickly as complexity is reduced. This highly accurate data can be used to generate an additional fused point cloud. This fused point cloud can then be added to the 15 dynamic object model 8220 for object identification and tracking, and to further enhance the infrastructure model using engine 8210.” in P16L5-20)
PNG
media_image1.png
294
412
media_image1.png
Greyscale
Christie, Di, Tao, and Ferguson fail to expressly disclose cropping the region of the image within the bounding box, forming a cropped image; and resizing the cropped image, forming a resized image.
Singh discloses of image annotation for deep neural networks, further comprising cropping the region of the image within the bounding box, forming a cropped image; (as per "An image 600 can be cropped by starting with an image 500 from FIG. 5 that includes a bounding box 508 and object 502, for example. All the image data outside of the bounding box 508 is deleted, leaving only image 600 including object 502. The cropped image 600 can be input to a DNN 200 to detect the object 502 included in the cropped image." in ¶45) and resizing the cropped image, forming a resized image, wherein performing the factor query comprises performing the factor query on the resized image. (as per "The cropped second image can be transformed to a higher spatial resolution by super resolution. The cropped second image can be transformed to include motion blurring. The cropped second image can be transformed by zooming the cropped second image. The cropped second image can be transformed to include hierarchical pyramid processing to obtain image data including the second object at a plurality of spatial resolutions." in ¶17, as per "At block 720 the cropped image 600 can be modified using super resolution, blurring, zooming, or hierarchical pyramid processing." in ¶59)
In this way, Singh operates to improve annotation of images by modifying the cropped image using super resolution, blurring, zooming, and hierarchical pyramid cropping to improve the training dataset. (as per ¶15). Like Christie, Di, Tao, and Ferguson, Singh is concerned with neural networks.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Christie, Di, Tao, and Ferguson with the image annotation for deep neural networks of Singh to enable another standard means of cropping and resizing the image within the bounding box. Such modification also improves the operation of neural networks (as per ¶40).
Claims 13 and 19 are rejected using the same rationale, mutatis mutandis, applied to Claim 6 above, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chilton (US Pub. No. 20240208492) discloses collision aware path planning systems and methods.
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 TYLER R ROBARGE whose telephone number is (703)756-5872. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm EST.
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, Ramón Mercado can be reached at (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.R.R./Examiner, Art Unit 3658
/TRUC M DO/Primary Examiner, Art Unit 3658