DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the amendments filed on 06/23/2026, in which claims 1, 4, 6, 9, 11-12, 14, 16, and 19 are pending and addressed below.
Response to Amendment
Applicant has amended the claims to remove generic placeholders. Accordingly, the claims are no longer subject to interpretation under 35 U.S.C. 112(f).
Applicant has amended the claims to overcome the 35 U.S.C. 101 rejections. Accordingly, the 35 U.S.C. 101 rejections have been withdrawn.
Response to Arguments
Applicant's arguments filed 06/23/2026 have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 103 rejections:
Applicant argues on page 12 of the remarks that the cited references fail to teach the amended independent claims. Applicant specifically argues on page 13 of the remarks that Deyle, Matsukawa, and Xiao fail to teach “classify the human-class instance into one of a plurality of transportation-vulnerable categories according to the calculated height” because Matsukawa determines whether a sensed person is an adult or a child based on height but “does not determine whether an adult or child belongs to a transportation-vulnerable category.” Applicant argues on page 14 of the remarks that Matsukawa teaches expanding a virtual obstacle region but the cited references fail to teach “determine a safety-zone type corresponding to the classified transportation-vulnerable category” because Matsukawa is “silent on determining a safety-zone type.” Applicant also argues on page 14 of the remarks that the references fail to teach “control movement of the driving robot according to the second driving route plan by variably controlling a degree of deceleration of the driving robot according to the determined safety-zone type” because “Matsukawa merely discloses that an autonomous mobile device 1 decelerates in order to avoid the collision,” but “Matsukawa is silent on determining a safety-zone type.”
In response to applicant’s arguments, the examiner respectfully disagrees that the cited references fail to teach the amended independent claims. Under broadest reasonable interpretation, Matsukawa teaches “classify the human-class instance into one of a plurality of transportation-vulnerable categories according to the calculated height” because Matsukawa teaches using a height of a sensed person to classify the person as an adult or an up to 10-year old child (Matsukawa [0096]). Matsukawa teaches a child is considered to belong to a “transportation-vulnerable category” because the movement of a child has more fluctuation and is more difficult to predict (Matsukawa [0095]). Accordingly, a child requires a larger surrounding space with enhanced safety because a child may suddenly change walking direction (Matsukawa [0153], [0186]). Furthermore, instant application [0069] defines transportation vulnerable as including children. Therefore, Matsukawa teaches classifying a human into one of a plurality of transportation-vulnerable categories according to the calculated height because Matsukawa uses height of a person to identify a child that requires enhanced safety compared to an adult.
Regarding the “determine a safety-zone type corresponding to the classified transportation-vulnerable category” limitation, the examiner respectfully disagrees that “Matsukawa is silent on determining a safety-zone type.” The instant application determines a type of safety zone by setting a size of the safety zone around a person based on the height of the person (instant application [0122]-[0132], Fig. 6). Matsukawa teaches a virtual obstacle region is set around a person based on a person’s predicted movement and predicted collision with an autonomous mobile device (Matsukawa [0042], [0072]). A virtual obstacle region (i.e., a safety-zone) is increased if a height indicates a person is a child because the movement of a child has more fluctuation and is more difficult to predict compared to an adult (Matsukawa [0095]-[0096]). Therefore, Matsukawa teaches “determine a safety-zone type corresponding to the classified transportation-vulnerable category” because Matsukawa teaches expanding a virtual obstacle region for enhanced safety when a person is identified as a child (Matsukawa [0186]).
In response to applicant’s arguments, the examiner respectfully disagrees that Matsukawa fails to teach the “control movement of the driving robot according to the second driving route plan by variably controlling a degree of deceleration of the driving robot according to the determined safety-zone type” limitation. Applicant argues that Matsukawa cannot teach the limitation because “Matsukawa is silent on determining a safety-zone type.” However, as explained above, Matsukawa teaches determining a safety-zone type by determining whether to expand a virtual obstacle region for enhanced safety based on whether a person is identified as a child. Furthermore, Matsukawa teaches “control movement of the driving robot according to the second driving route plan by variably controlling a degree of deceleration of the driving robot according to the determined safety-zone type” because Matsukawa teaches using height information and a virtual obstacle region to decelerate an autonomous mobile device and avoid collision (Matsukawa [0145], [0151], [0048]).
Applicant’s arguments with respect to Deyle and Xiao have been considered but are moot because the rejection does not rely on Deyle or Xiao for any teaching or matter specifically challenged in the argument. Accordingly, the combination of Deyle in view of Matsukawa and Xiao teach the entirety of the amended independent claims.
Applicant’s arguments have been fully considered and have been found not persuasive.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6, 9, 11-12, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Deyle et al., U.S. Patent Application Publication No. 2020/0050206 A1 (hereinafter Deyle), in view of Matsukawa et al., U.S. Patent Application Publication No. 2009/0043440 A1 (hereinafter Matsukawa), and further in view of Xiao et al., U.S. Patent Application Publication No. 2024/0077875 A1 (hereinafter Xiao).
Regarding claim 1, Deyle discloses an apparatus for generating a semantic map-based robot driving route plan for transportation vulnerable (see at least Deyle Fig. 1), the apparatus comprising:
one or more non-transitory computer-readable media comprising an instruction; and one or more processors configured, by executing the instruction, to (see at least Deyle [0128]: “It should also be noted that the robot 100 includes component necessary to communicatively couple and control the components of the robot, including but not limited to: on-board computers, controllers, and processors; electric circuitry (e.g., motor drivers); computer memory; storage media (e.g., non-transitory computer-readable storage mediums, such as flash memory, hard drives, and the like); communication buses; cooling or heat dissipation systems; and the like.”):
generate a semantic map for an area where a driving robot is driving based on depth images obtained from a plurality of cameras (see at least Deyle [0211]: “In some embodiments, the robot 100 can use one or more sensors, such as…3D depth cameras”; [0120]: “The location of obstructions, and paths within the building floor can be detected by the scanners 726 and recorded onto the semantic map. Likewise, objects can be detected during the robot's movement (for instance, by the cameras 722), and information describing the detected objects and the location of the detected objects can be included within a semantic map.”),
odometry estimated from an inertial measurement unit (IMU) (see at least Deyle [0126]: “In embodiments where a robot arm is extended, the robot may reposition or balance itself to compensate for the shift in the center of gravity of the robot, for instance using inertial sensors (such as 3-axis gyroscopes, accelerometers, or magnetometers).”),
and semantic images estimated from RGB images acquired by the plurality of cameras (see at least Deyle [0112]: “In some embodiments, camera pairs can capture 3D video, and in some embodiments, images or video captured by multiple cameras can be stitched together using one or more stitching operations to produce a stitched image or video. In addition to capturing images or video in the visible light spectrum, the cameras can capture images within the IR spectrum or can capture thermal images.”; images in the visible light spectrum include RGB images);
classify each instance unit into at least one of a human class and an obstacle class (see at least Deyle [0326]: “For example, if the confidence score for an object being a table is 72%, and the confidence score for the object being a chair is 66%, the robot can present the captured image to a human operator with both candidate classifications (“table” and “chair”), along with the corresponding confidence scores.”; [0227]: “The robot can detect and classify or identify objects, and can determine a state or other characteristics of the objects.”; [0193]: “Likewise, the location of the person 1516 displayed as a humanoid icon on the local map corresponds to the location of the person within the building as detected by the robot.”);
calculate heights of a classified human-class instance (see at least Deyle [0141]: “For instance, the robot can capture images or videos of the individuals using the cameras 722, and can perform facial recognition on the captured images or videos. Likewise, the robot can identify a height or size of the individual, or can scan a badge of the individual (for instance, using an RFID reader).”; [0266]: “In some embodiments, the robot 100 can determine a height of detected objects, and can generate a 3D semantic map based on the detected heights.”);
generate a second driving route plan that detours the expected collision range while the driving robot is traveling according to a first driving route plan (see at least Deyle [0275]: “A robot 100 can navigate within an area using a semantic map or a generated floor map, for instance by selecting a route that avoids obstacles (e.g., by a threshold distance), by selecting routes that avoid high-trafficked areas, by selecting routes that maximize the robot's exposure or proximity to high-value assets or other objects, and the like. In some embodiments, the robot can plan a route through an area (such as a building floor) in advance using the semantic map, or can dynamically adjust a route by querying the semantic map to identify an alternative route to a location (for instance, in the event that a route is blocked or in the event that suspicious activity or a security violation is detected).”);
Deyle fails to expressly disclose generating a safety zone for a specific object determined to be the transportation vulnerable based on the calculated height. However, Matsukawa teaches
classify the human-class instance into one of a plurality of transportation-vulnerable categories according to the calculated height; determine a safety-zone type corresponding to the classified transportation-vulnerable category (see at least Matsukawa [0095]-[0096]: “Also, for example, a movement of a child is hard to assume in comparison with that of an adult and the temporary fluctuation of the movement of a child is larger than that of an adult…The height of the person sensed by the sixth sensing unit 516 contributes to expand the virtual obstacle region A if the height of the person is lower than the predetermined height. That is, in the present embodiment, if the sensed person is an adult or a child is determined based on the height. According to a method of determining if the sensed person is an adult or a child based on his/her height, it is expected that the determination between an adult and up to a 10-year old child who moves quickly and thus whose movement is hard to predict can be done precisely to a certain degree. And therefore, it is possible to set the virtual obstacle region A in accordance with the movement of the child if the obstacle is a child.”; under broadest reasonable interpretation classifying a person as an up to 10-year old child is classifying a human-class into a transportation vulnerable category because the movement of a child is harder to predict and requires more space to avoid a collision);
calculate an expected collision range corresponding to the determined safety-zone type, wherein a size of the expected collision range is inversely proportional to the calculated height of the classified human-class instance (see at least Matsukawa [0185]: “As described above, the individual information may be the height information. Accordingly, the autonomous mobile device can carry out the operation control to properly avoid a collision based on this height information and can set a larger virtual obstacle region for a child than that of an adult considering that it is hard to predict the pathway of a child, namely, can carry out a different avoidance control between a child and an adult when the child or the adult walks past the autonomous mobile device, thereby carrying out a safe and easy operation control of a collision avoidance even for a child according to the difference between the adult and the child without bearing an unnecessarily large detour.”);
and control movement of the driving robot according to the second driving route plan by variably controlling a degree of deceleration of the driving robot according to the determined safety-zone type (see at least Matsukawa [0145]: “FIG. 7 illustrates a case example of carrying out a preliminary operation to avoid a collision considering an acceleration of the person 2…In view of the above, in FIG. 7, the virtual obstacle region of the person 2 at the time t+1, the time t+2, and the time t+3 with regard to the time t can be represented by Z71, Z72, and Z73, respectively. If the autonomous mobile device 1 keeps moving at a speed of, for example, 1 m/sec., the autonomous mobile device 1 comes close to the person 2 after 2 seconds and they collide with each other immediately thereafter. Therefore, the autonomous mobile device 1 decelerates in order to avoid the collision.”; [0151]: “FIG. 9 illustrates a case example in which the autonomous mobile device 1 preliminary takes a collision avoidance operation with regard to the person 2 by using the height information of the person 2 in addition to the eyes or the face orientation of the person 2 as the attribute information (individual information) of the person 2.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the apparatus disclosed by Deyle with Matsukawa with reasonable expectation of success. Matsukawa is directed towards the related field of controlling an autonomous mobile device based on an obstacle. Therefore, one of ordinary skill in the art would be motivated to modify Deyle with Matsukawa to perform a safe, easy, and smooth evasive action according to obstacle information (see at least Matsukawa [0007]: “The present invention is directed to provide an autonomous mobile device which can take a safe, easy, and smooth evasive action with regard to the movement of a person.”).
Deyle in view of Matsukawa fail to expressly disclose generating a semantic cloud from the semantic map and classifying the semantic cloud through clustering. However, Xiao teaches
generate a semantic cloud from the semantic map (see at least Xiao [0054]: “Block 016, the robot 100 establishes a current semantic map according to the current environment image and the current depth image, the current semantic map includes current point cloud information and second object type labels corresponding to the current point cloud information”) ;
cluster the semantic cloud into a plurality of instance units (see at least Xiao [0055]: “Block 017, the robot 100 clusters the current point cloud information in the current semantic map according to the second object type labels, identifies each object, and obtains second bounding box of each object, the second bounding box is an independent space corresponding to point clouds with same second object type label after clustering the point clouds”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the apparatus disclosed by Deyle in view of Matsukawa with Xiao with reasonable expectation of success. Xiao is directed towards the related field of robot positioning. Therefore, one of ordinary skill in the art would be motivated to modify Deyle in view of Matsukawa with Xiao to quickly and accurately determine proper robot positioning (see at least Xiao [0129]: “The above robot 100 can quickly and accurately determine associated node pairs (i.e., associated object pairs) through topology map matching, therefore, the search branch with the highest matching degree can be quickly determined through the number of associated nodes with the largest number of associated node pairs in the current local topology map and the full topology map, and then the current pose of the robot 100 can be determined according to the search branch with the highest matching degree.”).
Regarding claim 6, Deyle in view of Matsukawa and Xiao teach all elements of the apparatus according to claim 1 as explained above. Matsukawa further teaches wherein to determine the safety-zone type, the one or more processors are configured to:
generate a first safety zone for a first specific object having a height smaller than a first reference value among specific objects (see at least Matsukawa [0095]: “When the obstacle is a person, the control device may determine whether the obstacle is an adult or a child and may shift the virtual obstacle region A in accordance with the adult and the child.”; [0153]: “When using the height information, the autonomous mobile device 1 may determine that a tall person is an adult, and therefore expands the virtual obstacle region in his/her traveling direction since an adult may walk faster, whereas the autonomous mobile device 1 may determine that a short person is a child, and therefore, expands the virtual obstacle region in a direction orthogonal to his/her traveling direction since a child may suddenly change his/her walking direction to any direction around him/her.”; under broadest reasonable interpretation an adult height is smaller than a reference value for an obstacle that is not detected to be a person);
and generate a second safety zone for a second specific object having a height smaller than a second reference value among the specific objects, and wherein the second reference value is smaller than the first reference value (see at least Matsukawa [0096]: “The height of the person sensed by the sixth sensing unit 516 contributes to expand the virtual obstacle region A if the height of the person is lower than the predetermined height. That is, in the present embodiment, if the sensed person is an adult or a child is determined based on the height.”; [0153]: “When using the height information, the autonomous mobile device 1 may determine that a tall person is an adult, and therefore expands the virtual obstacle region in his/her traveling direction since an adult may walk faster, whereas the autonomous mobile device 1 may determine that a short person is a child, and therefore, expands the virtual obstacle region in a direction orthogonal to his/her traveling direction since a child may suddenly change his/her walking direction to any direction around him/her.”).
Regarding claim 9, Deyle in view of Matsukawa and Xiao teach all elements of the apparatus according to claim 1 as explained above. Matsukawa further teaches wherein to generate the second driving route plan, the one or more processors are configured to:
accelerate a driving speed of the driving robot while the driving robot is driving with the first driving route plan (see at least Matsukawa [0176]: “With such a configuration, the autonomous mobile device can acquire the information as to the actual conditions of the movement of the moving object from the information relating to the velocity vector and the accelerated velocity vector and further acquire a basic pathway for obtaining the possible pathways of the moving object which are used for setting the virtual obstacle region, thereby enabling the autonomous mobile device to avoid the thus set virtual obstacle region.”; [0122]: “The autonomous mobile device 1 carries out at least one operation of a stop, a deceleration, an acceleration, and a change of direction such that the autonomous mobile device 1 can avoid a collision with the person 2 as a moving object or the stationary obstacle in accordance with a driving control of the collision avoidance control device 200.”);
and decelerate the driving speed while the driving robot is driving with the second driving route plan (see at least Matsukawa [0145]: “FIG. 7 illustrates a case example of carrying out a preliminary operation to avoid a collision considering an acceleration of the person 2…Therefore, the autonomous mobile device 1 decelerates in order to avoid the collision.”).
Regarding claims 11 and 12, these claims recite a method performed by the apparatus of claim 1. The combination of Deyle in view of Matsukawa and Xiao also teaches a method performed by the apparatus of claim 1 as outlined in the rejection to claim 1 above. Therefore, claims 11 and 12 are rejected for the same rationale as claim 1.
Regarding claim 16, this claim recites a method performed by the apparatus of claim 6 as explained
above. Therefore, claim 16 is rejected for the same rationale as claim 6.
Regarding claim 19, this claim recites a method performed by the apparatus of claim 9 as explained
above. Therefore, claim 19 is rejected for the same rationale as claim 9.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Deyle in view of Matsukawa and Xiao, and further in view of Sharma Banjade et al., U.S. Patent Application Publication No. 2024/0214786 A1 (hereinafter Sharma).
Regarding claim 4, Deyle in view of Matsukawa and Xiao teach all elements of the apparatus according to claim 1 as explained above. Deyle in view of Matsukawa and Xiao fail to expressly disclose the plurality of transportation-vulnerable categories include a child category, an infant category, and a wheelchair-user category. However, Sharma teaches
wherein the plurality of transportation-vulnerable categories includes at least a child category, an infant category, and a wheelchair-user category (see at least Sharma [0142]: “As mentioned previously, the different types of VRUs have been categorized into the following four profiles: VRU Profile-1: Pedestrians (e.g., pavement users, children, pram, disabled persons, elderly, and/or the like); VRU Profile-2: Bicyclists (e.g., light vehicles carrying persons, wheelchair users, horses carrying riders, skaters, e-scooters, Segways, and/or the like); VRU Profile-3: Motorcyclists with engine equipped which can reach speeds similar to other vehicles; and VRU Profile-4: Animals posing safety risk to other road users (e.g., dogs, wild animals, horses, cows, sheep, and/or the like).”; [0096]: “Type of VRU: with 4 possible categories as: (i) infant; (ii) toddler; (iii) kid; (iv) adult; (v) pet/animal; (b) VRU Size Class: with 3 possible categories as: (i) small; (ii) medium; (iii) large. VRU Weight Class: with 3 possible categories as: (i) low; (ii) medium; (iii) high.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the apparatus disclosed by Deyle in view of Matsukawa and Xiao with Sharma with reasonable expectation of success. Sharma is directed towards the related field of intelligent transport systems for providing services to vulnerable road users. Therefore, one of ordinary skill in the art would be motivated to modify Deyle in view of Matsukawa and Xiao with Sharma to improve traffic safety and efficiency (see at least Sharma [0004]: “Cooperative Intelligent Transport Systems (C-ITS) have been developed to enable an increase in traffic safety and efficiency, and to reduce emissions and fuel consumption. The initial focus of C-ITS was on road traffic safety and especially on vehicle safety. Recent efforts are being made to increase traffic safety and efficiency for vulnerable road users (VRUs), which refers to both physical entities (e.g., pedestrians) and/or user devices (e.g., mobile stations, and/or the like) used by physical entities…However, to date CA/AD vehicles can do very little about detection, let alone correction of the human-error at VRUs' end, even though it is equipped with a sophisticated sensing technology suite, as well as computing and mapping technologies.”).
Regarding claim 14, this claim recites a method performed by the apparatus of claim 4 as explained
above. Therefore, claim 14 is rejected for the same rationale as claim 4.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH J SLOWIK whose telephone number is (571)270-5608. The examiner can normally be reached MON - FRI: 0900-1700.
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/ELIZABETH J SLOWIK/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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