DETAILED ACTION
Response received 26 June 2026 is acknowledged. Claims 1-7 and 9-19 amended 20 February 2026 are pending and have been considered as follows.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-6, 9, 13-14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kojima (US Pub. No. 2019/0015980) in view Terasawa (WO 2020/075423 A1; citations to US Pub. No. 2021/0402598).
As per Claim 1, Kojima discloses a method of moving (as per “transporting the workpiece W to a target position” in ¶85) an object (W) by a robot (R) (Figs. 3, 6; ¶67, 74-88), the method comprising:
retrieving a model (as per “spatial information regarding the workpiece W” in ¶129, as per “the workpiece W is imaged and measured” in ¶130, as per “recognizes (acquires) the workpiece W” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), the model (as per “spatial information regarding the workpiece W” in ¶129, as per “the workpiece W is imaged and measured” in ¶130, as per “recognizes (acquires) the workpiece W” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) indicating one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), wherein the one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) are intrinsic properties (as per “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W) (Figs. 14-15; ¶128-130);
retrieving robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67) associated with the robot (R), wherein the robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67) comprises at least one of effector type (as per “The end effector E may have a gripping mechanism for gripping by opening and closing multiple fingers, a mechanism for suction of the workpiece W, or a combination therefore, or may be able to hold the workpiece W without being provided with a special mechanism” in ¶72; as per “contact conditions corresponding to the end effector E are stored in advance” in ¶131) (Figs. 1, 5, 9, 14-15; ¶64-68, 72, 111-114, 128-131) and {joint limits associated with the robot};
obtaining grasp point data (as per “orientation calculation unit 92 calculates a gripping point or a gripping position of the workpiece … contact conditions corresponding to the end effector E are stored … and a gripping point of the workpiece W can be specified” in ¶131) associated with the object (W) (Figs. 5, 14-15; ¶72, 128-131);
based on the robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67), the one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), and the grasp point data (as per “orientation calculation unit 92 calculates a gripping point or a gripping position of the workpiece … contact conditions corresponding to the end effector E are stored … and a gripping point of the workpiece W can be specified” in ¶131), selecting a path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) for moving the object (W) from a first location (“as per gripping position of the workpiece W” in ¶131) to a second location (as per “target position” in ¶85) so as to define a selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81), the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) defining a grasp pose (as per “orientation of the robot R” in ¶71) for the robot (R) to carry the object (W), a velocity (as per “the speed … of the robot” in ¶71) associated with moving the object (W) in the grasp pose (as per “orientation of the robot R” in ¶71), and an acceleration (as per “the acceleration … of the robot” in ¶71) associated with moving the object (W) in the grasp pose (as per “orientation of the robot R” in ¶71) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131); and
effecting movement (as per S34, S36) of the object (W), by the robot (R), from the first location (“as per gripping position of the workpiece W” in ¶131) to the second location (as per “target position” in ¶85) in the grasp pose (as per “orientation of the robot R” in ¶71) of the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-87, 129-131).
Kojima does not expressly disclose wherein the selecting is performed automatically.
Terasawa discloses a robot device (10) that operates to grip and move with an arm an object (1) (Fig. 1; ¶17-20). The robotic device (10) includes a robot control unit (30), storage unit (20), and control unit (40) (Fig. 2). The robot control unit (30) is a processing unit for controlling the robot mechanism of the robotic device (10) and includes an object information acquisition unit (31), a grip unit (32), and a drive unit (33) (Fig. 2; ¶22, 35). The storage unit (20) includes a task database (21), an object information database (22), a constraint condition database (23), and a set value database (24) (Fig. 2; ¶22-23). The control unit (40) plans the motion trajectory of the robotic device (10) and includes a task management unit (41), an action determination unit (42), and an arm control unit (45) (Fig. 2; ¶22-23, 40). The action determination unit (42) includes a constraint condition determination unit (43) and a planning unit (44) (Fig. 2; ¶40-43). In operation, the constraint condition determination unit (43) determines (S104, S106) the constraint condition and the planning unit (44) plans (S107) the motion trajectory while observing the constraint condition determined (S104, S106) (Fig. 5; ¶51-55). Embodiments for the constraint condition include speed, acceleration, and joint angle (¶76-77). The constraint condition determination unit (43) is informed of a task to be performed based on an initial value a target value of a motion plan given by a user or based on analysis of image data (¶51). In this way, autonomy is enhanced (¶56-58). Like Kojima, Terasawa is concerned with robot control systems.
Therefore, from these teachings of Kojima and Terasawa, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa to the system of Kojima since doing so would enhance autonomy of the system. Applying the teachings of Terasawa to the system of Kojima would result in a system that operates “wherein the selecting is performed automatically” in that Terasawa discloses embodiments in which constraint conditions are determined automatically in response to detected image data.
As per Claim 5, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 1. Kojima further discloses:
determining a plurality of path constraints (as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) that define a plurality of grasp poses (as per “a gripping point of the workpiece W can be specified by calculating a contact point based on the shape of the workpiece W … An orientation of the extremity portion for gripping the workpiece W at the calculated gripping point is then calculated” in ¶131) in which the robot (R) can move the object (W) from the first location (“as per gripping position of the workpiece W” in ¶131) to the second location (as per “target position” in ¶85) without dropping (as per “keeping the orientation of the workpiece W gripped by the end effector E constant” in ¶85) the object (W) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131); and
selecting the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) from the plurality of path constraints (as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) based on the velocity (as per “the speed … of the robot” in ¶71) and acceleration (as per “the acceleration … of the robot” in ¶71) of the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131).
As per Claim 6, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 5. Kojima further discloses wherein determining the plurality of path constraints (as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) further comprises:
based on the robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67), the one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), and the grasp point data (as per “orientation calculation unit 92 calculates a gripping point or a gripping position of the workpiece … contact conditions corresponding to the end effector E are stored … and a gripping point of the workpiece W can be specified” in ¶131), formulating and solving a constraint optimization problem (as per “generate motions for the robot based on an optimum algorithm selected from among the speed prioritization algorithm, the acceleration priority algorithm, and the orientation priority algorithm” in ¶33) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 91, 94, 129-131).
As per Claim 9, further discloses an autonomous system comprising:
a robot (R) within a robotic cell (as per “surrounding environment input unit 72” in ¶112), the robot (R) defining an end effector (E) configured to grasp (as per “for gripping” in ¶72) an object (W) within a physical environment (Figs. 3, 5-6; ¶67, 72-88);
one or more processors (22) (Figs. 1-2; ¶64-67); and
a memory (20, 24) storing instructions (as per “computer program” in ¶65-66) that, when executed by the one or more processors (22) (Figs. 1-2; ¶64-67), cause the autonomous system to:
retrieve a model (as per “spatial information regarding the workpiece W” in ¶129, as per “the workpiece W is imaged and measured” in ¶130, as per “recognizes (acquires) the workpiece W” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), the model (as per “spatial information regarding the workpiece W” in ¶129, as per “the workpiece W is imaged and measured” in ¶130, as per “recognizes (acquires) the workpiece W” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) indicating one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), wherein the one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) are intrinsic properties (as per “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W) (Figs. 14-15; ¶128-130);
retrieve robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67) associated with the robot (R), wherein the robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67) comprises at least one of effector type (as per “The end effector E may have a gripping mechanism for gripping by opening and closing multiple fingers, a mechanism for suction of the workpiece W, or a combination therefore, or may be able to hold the workpiece W without being provided with a special mechanism” in ¶72; as per “contact conditions corresponding to the end effector E are stored in advance” in ¶131) (Figs. 1, 5, 9, 14-15; ¶64-68, 72, 111-114, 128-131) and {joint limits associated with the robot};
obtain grasp point data (as per “orientation calculation unit 92 calculates a gripping point or a gripping position of the workpiece … contact conditions corresponding to the end effector E are stored … and a gripping point of the workpiece W can be specified” in ¶131) associated with the object (W) (Figs. 5, 14-15; ¶72, 128-131); and
based on the robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67), the one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), and the grasp point data (as per “orientation calculation unit 92 calculates a gripping point or a gripping position of the workpiece … contact conditions corresponding to the end effector E are stored … and a gripping point of the workpiece W can be specified” in ¶131), select a path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) for moving the object (W) from a first location (“as per gripping position of the workpiece W” in ¶131) to a second location (as per “target position” in ¶85) so as to define a selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81), the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) defining a grasp pose (as per “orientation of the robot R” in ¶71) for the robot (R) to carry the object (W), a velocity (as per “the speed … of the robot” in ¶71) associated with moving the object (W) in the grasp pose (as per “orientation of the robot R” in ¶71), and an acceleration (as per “the acceleration … of the robot” in ¶71) associated with moving the object (W) in the grasp pose (as per “orientation of the robot R” in ¶71) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131).
Kojima does not expressly disclose wherein the selecting is performed automatically.
See rejection of Claim 1 for discussion of teachings of Terasawa
Therefore, from these teachings of Kojima and Terasawa, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa to the system of Kojima since doing so would enhance autonomy of the system. Applying the teachings of Terasawa to the system of Kojima would result in a system that operates “wherein the selecting is performed automatically” in that Terasawa discloses embodiments in which constraint conditions are determined automatically in response to detected image data.
As per Claim 13, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 9. Kojima further discloses the memory (20, 24) further storing instructions (as per “computer program” in ¶65-66) that, when executed by the one or more processors (22), further cause the autonomous system to:
determine a plurality of path constraints (as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) that define a plurality of grasp poses (as per “a gripping point of the workpiece W can be specified by calculating a contact point based on the shape of the workpiece W … An orientation of the extremity portion for gripping the workpiece W at the calculated gripping point is then calculated” in ¶131) in which the robot (R) can move the object from (W) the first location (“as per gripping position of the workpiece W” in ¶131) to the second location (as per “target position” in ¶85) without dropping (as per “keeping the orientation of the workpiece W gripped by the end effector E constant” in ¶85) the object (W) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131); and
select the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) from the plurality of path constraints (as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) based on the velocity (as per “the speed … of the robot” in ¶71) and acceleration (as per “the acceleration … of the robot” in ¶71) of the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131).
As per Claim 14, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 13. Kojima further discloses the memory (20, 24) further storing instructions (as per “computer program” in ¶65-66) that, when executed by the one or more processors (22), further cause the autonomous system to:
based on the robot configuration data (as per “initial/target orientation input unit 12 is a unit for receiving an input of an initial orientation and a target orientation of the robot R” in ¶67), the one or more physical properties (as per “spatial information” in ¶129 and “measured” in ¶130; as per “shape of the workpiece W that is actually recognized by the image sensor S” in ¶131) of the object (W), and the grasp point data (as per “orientation calculation unit 92 calculates a gripping point or a gripping position of the workpiece … contact conditions corresponding to the end effector E are stored … and a gripping point of the workpiece W can be specified” in ¶131), formulating and solving a constraint optimization problem (as per “generate motions for the robot based on an optimum algorithm selected from among the speed prioritization algorithm, the acceleration priority algorithm, and the orientation priority algorithm” in ¶33) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 91, 94, 129-131).
As per Claim 16, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 9. Kojima further discloses the memory (20, 24) further storing instructions (as per “computer program” in ¶65-66) that, when executed by the one or more processors (22), further cause the autonomous system to:
move the object (W), by the robot (R), from the first location (“as per gripping position of the workpiece W” in ¶131) to the second location (as per “target position” in ¶85) in the grasp pose (as per “orientation of the robot R” in ¶71) of the selected path constraint (as per “the priority item input unit 14 selects the corresponding priority item PI and outputs it to the motion generation unit 10” in ¶70; as per “the speed, the acceleration, and the orientation of the robot R are indicated as the priority items” in ¶71; as per “multiple priority items PI may be selected at the same time” in ¶81) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131).
As per Claim 17, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 1. The combination of Kojima and Terasawa further teaches or suggests a non-transitory computer-readable storage medium (20, 24 of Kojima) including instructions (as per “computer program” in ¶65-66 of Kojima) that, when processed by a computing system (22 of Kojima) cause the computing system to perform the method according to claim 1 (see rejection of Claim 1).
As per Claim 18, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 1. Kojima does not expressly disclose wherein the one or more physical properties of the objects are selected from the group consisting of: mass, geometric size dimensions, weight distribution and material of the object.
See rejection of Claim 1 for discussion of teachings of Terasawa. Terasawa further discloses wherein the object information database (22) stores a feature amount of the object, the feature amount including area, center of gravity, length, and position (¶37).
Therefore, from these teachings of Kojima and Terasawa, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa to the system of Kojima since doing so would enhance autonomy of the system. Applying the teachings of Terasawa to the system of Kojima would result in a system that operates “wherein the one or more physical properties of the objects are selected from the group consisting of: mass, geometric size dimensions, weight distribution and material of the object” in that the system of Kojima would be informed by a feature amount as per Terasawa.
As per Claim 19, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 9. Kojima does not expressly disclose wherein the one or more physical properties of the objects are selected from the group consisting of: mass, geometric size dimensions, weight distribution and material of the object.
See rejection of Claim 1 for discussion of teachings of Terasawa. Terasawa further discloses wherein the object information database (22) stores a feature amount of the object, the feature amount including area, center of gravity, length, and position (¶37).
Therefore, from these teachings of Kojima and Terasawa, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa to the system of Kojima since doing so would enhance autonomy of the system. Applying the teachings of Terasawa to the system of Kojima would result in a system that operates “wherein the one or more physical properties of the objects are selected from the group consisting of: mass, geometric size dimensions, weight distribution and material of the object” in that the system of Kojima would be informed by a feature amount as per Terasawa.
Claims 2-4 and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kojima (US Pub. No. 2019/0015980) in view Terasawa (WO 2020/075423 A1; citations to US Pub. No. 2021/0402598), further in view Kim (US Pub. No. 2016/0052132).
As per Claim 2, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 1. Kojima does not expressly disclose extracting, from the robot configuration data, a maximum velocity value and a maximum acceleration value at which the robot is designed to travel.
See rejection of Claim 1 for discussion of teachings of Terasawa.
Kim discloses a robot driving module (140) for driving a robot, the robot driving module (140) informed by an input trajectory generation module (110), a data extraction module (120), and a data restoration module (130) (Fig. 1; ¶39-43). The data restoration module (130) interpolates restoration motion data to generate an output motion trajectory satisfying the condition that the restored signal does not exceed physical limits of the robot (¶91-95). Physical limits for the robot include velocity and acceleration (¶95). In this way, the robot is driven with enhanced stability (¶159-160). Like Kojima, Kim is concerned with robot control systems.
Therefore, from these teachings of Kojima, Terasawa, and Kim one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Kim to the system of Kojima since doing so would: enhance autonomy of the system; and enhance stability. Applying the teachings of Terasawa and Kim to the system of Kojima would result in a system that operates “extracting, from the robot configuration data, a maximum velocity value and a maximum acceleration value at which the robot is designed to travel” in that generated motions as per Kojima as modified in view of Terasawa would informed by physical limit values as per Kim.
As per Claim 3, the combination of Kojima, Terasawa, and Kim teaches or suggests all limitations of Claim 2. Kojima does not expressly disclose wherein at least one of the velocity of the selected path constraint and the acceleration of the selected path constraint is equivalent to the maximum velocity value and the maximum acceleration value, respectively.
See rejection of Claim 1 for discussion of teachings of Terasawa.
See rejection of Claim 2 for discussion of teachings of Kim.
Therefore, from these teachings of Kojima, Terasawa, and Kim one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Kim to the system of Kojima since doing so would: enhance autonomy of the system; and enhance stability. Applying the teachings of Terasawa and Kim to the system of Kojima would result in a system that operates “wherein at least one of the velocity of the selected path constraint and the acceleration of the selected path constraint is equivalent to the maximum velocity value and the maximum acceleration value, respectively” in that generated motions as per Kojima as modified in view of Terasawa would informed by physical limit values as per Kim.
As per Claim 4, the combination of Kojima, Terasawa, and Kim teaches or suggests all limitations of Claim 2. Kojima does not expressly disclose wherein the velocity of the selected path constraint is less than the maximum velocity value and the acceleration of the selected path constraint is less than the maximum acceleration value.
See rejection of Claim 1 for discussion of teachings of Terasawa.
See rejection of Claim 2 for discussion of teachings of Kim.
Therefore, from these teachings of Kojima, Terasawa, and Kim one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Kim to the system of Kojima since doing so would: enhance autonomy of the system; and enhance stability. Applying the teachings of Terasawa and Kim to the system of Kojima would result in a system that operates “wherein the velocity of the selected path constraint is less than the maximum velocity value and the acceleration of the selected path constraint is less than the maximum acceleration value” in that generated motions as per Kojima as modified in view of Terasawa would informed by physical limit values as per Kim.
As per Claim 10, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 9. Kojima does not expressly disclose the memory further storing instructions that, when executed by the one or more processors, further cause the autonomous system to:
extract, from the robot configuration data, a maximum velocity value and a maximum acceleration value at which the robot is designed to travel.
See rejection of Claim 1 for discussion of teachings of Terasawa.
See rejection of Claim 2 for discussion of teachings of Kim.
Therefore, from these teachings of Kojima, Terasawa, and Kim one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Kim to the system of Kojima since doing so would: enhance autonomy of the system; and enhance stability. Applying the teachings of Terasawa and Kim to the system of Kojima would result in a system that operates “the memory further storing instructions that, when executed by the one or more processors, further cause the autonomous system to: extract, from the robot configuration data, a maximum velocity value and a maximum acceleration value at which the robot is designed to travel” in that generated motions as per Kojima as modified in view of Terasawa would informed by physical limit values as per Kim.
As per Claim 11, the combination of Kojima, Terasawa, and Kim teaches or suggests all limitations of Claim 10. Kojima does not expressly disclose wherein at least one of the velocity of the selected path constraint and the acceleration of the selected path constraint is equivalent to the maximum velocity value and the maximum acceleration value, respectively.
See rejection of Claim 1 for discussion of teachings of Terasawa.
See rejection of Claim 2 for discussion of teachings of Kim.
Therefore, from these teachings of Kojima, Terasawa, and Kim one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Kim to the system of Kojima since doing so would: enhance autonomy of the system; and enhance stability. Applying the teachings of Terasawa and Kim to the system of Kojima would result in a system that operates “wherein at least one of the velocity of the selected path constraint and the acceleration of the selected path constraint is equivalent to the maximum velocity value and the maximum acceleration value, respectively” in that generated motions as per Kojima as modified in view of Terasawa would informed by physical limit values as per Kim.
As per Claim 12, the combination of Kojima, Terasawa, and Kim teaches or suggests all limitations of Claim 10. Kojima does not expressly disclose wherein the velocity of the selected path constraint is less than the maximum velocity value and the acceleration of the selected path constraint is less than the maximum acceleration value.
See rejection of Claim 1 for discussion of teachings of Terasawa.
See rejection of Claim 2 for discussion of teachings of Kim.
Therefore, from these teachings of Kojima, Terasawa, and Kim one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Kim to the system of Kojima since doing so would: enhance autonomy of the system; and enhance stability. Applying the teachings of Terasawa and Kim to the system of Kojima would result in a system that operates “wherein the velocity of the selected path constraint is less than the maximum velocity value and the acceleration of the selected path constraint is less than the maximum acceleration value” in that generated motions as per Kojima as modified in view of Terasawa would informed by physical limit values as per Kim.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kojima (US Pub. No. 2019/0015980) in view Terasawa (WO 2020/075423 A1; citations to US Pub. No. 2021/0402598), further in view Yamazaki (US Pub. No. 2017/0028562).
As per Claim 7, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 5. Kojima does not expressly disclose wherein determining the plurality of path constraints further comprises:
based on the robot configuration data, the one or more physical properties of the object, and the grasp point data, simulating a plurality of trajectories; and
assigning a reward value to each of the plurality of trajectories based on velocity values, acceleration values, and grasp poses associated with the respective trajectories.
See rejection of Claim 1 for discussion of teachings of Terasawa.
Yamazaki discloses a robot (14) governed by a controller (16) that is informed by a machine learning device (20) (Fig. 1; ¶26-27). The machine learning device (20) includes an operation result obtaining unit (26) and a learning unit (22), the learning unit (22) including a reward computation unit (23) and a value function update unit (24) (Fig. 1; ¶26-27, 71). The operation result obtaining unit (26) obtains a result of picking up of a workpiece (12) by the robot (14) (¶41). The reward computation unit (23) computes a reward based on success or failure of picking up the workpiece (12) (¶71). The value function update unit (24) provides a record in the form of a value function describing the picking operation (¶72-74). In one embodiment, the machine learning device (20) performs picking simulation based on a plurality of hand models during the picking operation (¶81). In this way, the robot (14) is adapted to learn an optimal operation in picking up randomly placed workpieces (¶8). Like Kojima, Yamazaki is concerned with robot control systems.
Therefore, from these teachings of Kojima, Terasawa, and Yamazaki one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Yamazaki the system of Kojima since doing so would: enhance autonomy of the system; and enhance the system by adapting the system to pick up randomly placed workpieces. Applying the teachings of Terasawa and Yamazaki to the system of Kojima would result in a system that operates “wherein determining the plurality of path constraints further comprises: based on the robot configuration data, the one or more physical properties of the object, and the grasp point data, simulating a plurality of trajectories; and assigning a reward value to each of the plurality of trajectories based on velocity values, acceleration values, and grasp poses associated with the respective trajectories” in that the system for generating motions as per Kojima modified in view of Terasawa would be adapted to perform simulations and learning as per Yamazaki.
As per Claim 15, the combination of Kojima and Terasawa teaches or suggests all limitations of Claim 9. Kojima does not expressly disclose the memory further storing instructions that, when executed by the one or more processors, further cause the autonomous system to:
based on the robot configuration data, the one or more physical properties of the object, and the grasp point data, simulating a plurality of trajectories; and
assign a reward value to each of the plurality of trajectories based on velocity values, acceleration values, and grasp poses associated with the respective trajectories.
See rejection of Claim 1 for discussion of teachings of Terasawa.
See rejection of Claim 7 for discussion of teachings of Yamazaki.
Therefore, from these teachings of Kojima, Terasawa, and Yamazaki one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa and Yamazaki the system of Kojima since doing so would: enhance autonomy of the system; and enhance the system by adapting the system to pick up randomly placed workpieces. Applying the teachings of Terasawa and Yamazaki to the system of Kojima would result in a system that operates wherein “the memory further storing instructions that, when executed by the one or more processors, further cause the autonomous system to: based on the robot configuration data, the one or more physical properties of the object, and the grasp point data, simulating a plurality of trajectories; and assign a reward value to each of the plurality of trajectories based on velocity values, acceleration values, and grasp poses associated with the respective trajectories” in that the system for generating motions as per Kojima modified in view of Terasawa would be adapted to perform simulations and learning as per Yamazaki.
Response to Arguments
Applicant's arguments filed 26 June 2026 have been fully considered as follows.
Regarding the rejection of Claim 1, Applicant argues (page 2-3 of Response):
As a first difference, the cited references Kojima and Terasawa do not teach "retrieving a model of the object, the model indicating one or more physical properties of the object, wherein the one or more physical properties are intrinsic properties of the object." The Examiner maps this element to Kojima paragraphs [0129]-[0131]. Applicant respectfully disagrees.
Kojima paragraph [0129] discloses obtaining "spatial position information" of the workpiece, which is extrinsic locational data describing where the workpiece is situated in the workspace, for example on a conveyor or in an irregular arrangement. This is not an intrinsic physical property of the object. Paragraph [0130] states the workpiece is "imaged and measured," but the stated purpose is to localize the workpiece so that an initial or target orientation can be computed, not to retrieve a model indicating intrinsic physical properties. Paragraph [0131] references the "shape of the workpiece W that is actually recognized by the image sensor S," but this shape recognition serves a narrow and specific purpose: matching the recognized shape to stored model shapes (circular column, sphere, cuboid) to calculate a gripping point and a corresponding extremity orientation. It is not used as an input to any path constraint selection process.
As described in the Applicant's specification at paragraph [0019], the "physical properties" indicated by the object model are properties such as mass, geometric size dimensions, weight distribution, and material. These are properties inherent to the object itself that directly affect how the robot must constrain its motion to maintain grasp stability. No such concept exists in Kojima.
The Examiner's Response to Arguments states that Kojima "further discloses retrieving a model indicating intrinsic properties of the object as claimed" but provides no explanation of how spatial position information or shape-based gripping point calculation constitutes retrieving intrinsic physical properties of the object. This is a conclusory finding that lacks the requisite articulated reasoning.
Comparing teachings of the cited references to the claim language at issue, the cited references teach or suggest “retrieving a model of the object, the model indicating one or more physical properties of the object, wherein the one or more physical properties are intrinsic properties of the object” in that Kojima discloses a robot (R) that grips and transports a workpiece (W) (Figs. 3, 6; ¶67, 74-88) based in part on information about the workpiece (W), the information from an image sensor (S) that gathers positional information about the workpiece (W) as well as shape, color, and the like of the workpiece (W) (Figs. 14-15; ¶128-130). Accordingly, Kojima discloses all limitations in the claim language at issue. As such, Applicant’s argument involves an improper interpretation of the claim language at issue and/or an improper interpretation of the cited references. Therefore, Applicant’s argument does not identify a proper basis for finding that any rejection is improper.
As to Applicant’s assertion that “the stated purpose is to localize the workpiece so that an initial or target orientation can be computed, not to retrieve a model indicating intrinsic physical properties”, this argument is not relevant to the rejection in that the claim language at issue does not recite a specified purpose.
As to Applicant’s assertion that “this shape recognition serves a narrow and specific purpose: matching the recognized shape to stored model shapes (circular column, sphere, cuboid) to calculate a gripping point and a corresponding extremity orientation … It is not used as an input to any path constraint selection process”, this assertion is not relevant to the rejection in that the claim language at issue does not recite “an input to any path constraint selection process”.
As to Applicant’s assertions that “As described in the Applicant's specification at paragraph [0019], the ‘physical properties’ indicated by the object model are properties such as mass, geometric size dimensions, weight distribution, and material”, “These are properties inherent to the object itself that directly affect how the robot must constrain its motion to maintain grasp stability”, and “No such concept exists in Kojima”, this assertion is not relevant to the rejection in that the claim language at issue does not recite “mass, geometric size dimensions, weight distribution, and material”.
As to Applicant’s assertions that “The Examiner's Response to Arguments states that Kojima ‘further discloses retrieving a model indicating intrinsic properties of the object as claimed’ but provides no explanation of how spatial position information or shape-based gripping point calculation constitutes retrieving intrinsic physical properties of the object” and “This is a conclusory finding that lacks the requisite articulated reasoning”, this assertion is not consistent with the written record in that the Response section (see “as set forth in the above rejections” on page 20 of 20 April 2026 Office action), clearly refers the reader back to the Claim Rejections section of the Office action.
For these reasons, Applicant’s arguments regarding “a first difference” do not identify a proper basis for finding that any rejection is improper.
Regarding the rejection of Claim 1, Applicant argues (page 3-4 of Response):
As a second difference, the cited references do not teach "retrieving robot configuration data associated with the robot, wherein the robot configuration data comprises at least one of effector type and joint limits associated with the robot." The Examiner maps "robot configuration data" primarily to Kojima paragraph [0067], which discloses the "initial/target orientation input unit 12" as "a unit for receiving an input of an initial orientation and a target orientation of the robot R." This is motion endpoint data that tells the system where the robot starts and where it should go. It is not robot configuration data describing the capabilities or specifications of the robot itself.
The Examiner further maps "effector type" to Kojima paragraph [0072], which describes what types of end effectors a robot may have (gripping by multiple fingers, suction, or a combination thereof), and to paragraph [0131], which notes that "contact conditions corresponding to the end effector E are stored in advance." However, neither passage discloses retrieving effector type as a data input that feeds into a path constraint selection process. In Kojima, end effector information is used solely for gripping point calculation (paragraph [0131]), which is a separate function from the selection of priority items for motion generation.
Regarding "joint limits," the Examiner's mapping is notably absent. The Office Action identifies "joint limits associated with the robot" as part of the claim element but does not point to any passage in Kojima or Terasawa that discloses retrieving joint limits as robot configuration data that is used as a basis for selecting path constraints. As described in the Applicant's specification at paragraphs [0016] and [0019], robot configuration data includes specifications such as position, velocity, and acceleration limits of the joints of the robot, as well as effector types. These are mechanical specifications of the robot that directly inform the formulation of path constraints. No analogous data retrieval step exists in Kojima.
The Examiner's Response to Arguments states that Kojima "further discloses retrieving robot configuration data as claimed" without any reasoned explanation of how initial and target orientation inputs constitute robot configuration data comprising effector type or joint limits. This conclusory finding does not substantively address Applicant's arguments.
Comparing teachings of the cited references to the claim language at issue, the cited references teach or suggest “retrieving robot configuration data associated with the robot, wherein the robot configuration data comprises at least one of effector type and joint limits associated with the robot” in that Kojima discloses a robot (R) that grips and transports a workpiece (W) (Figs. 3, 6; ¶67, 74-88) based in part on information about an initial orientation and target orientation of the robot (R) (¶67), the information about an initial orientation and target orientation of the robot (R) describing the type of end effector (E) as well as contact conditions corresponding to the end effector (E) (Figs. 1, 5, 9, 14-15; ¶64-68, 72, 111-114, 128-131). Accordingly, Kojima discloses all limitations in the claim language at issue. As such, Applicant’s argument involves an improper interpretation of the claim language at issue and/or an improper interpretation of the cited references. Therefore, Applicant’s argument does not identify a proper basis for finding that any rejection is improper.
As to Applicant’s assertion that “This is motion endpoint data that tells the system where the robot starts and where it should go” and “It is not robot configuration data describing the capabilities or specifications of the robot itself”, this assertion is not relevant to the rejection in that the claim language at issue does not recite “configuration data describing the capabilities or specifications of the robot itself”.
As to Applicant’s assertion that “neither passage discloses retrieving effector type as a data input that feeds into a path constraint selection process” and “end effector information is used solely for gripping point calculation (paragraph [0131]), which is a separate function from the selection of priority items for motion generation”, this assertion is not relevant to the rejection in that the claim language at issue does not recite “data input that feeds into a path constraint selection process” or exclude embodiments in which the configuration data is used “for gripping point calculation”.
As to Applicant’s assertion that “Regarding ‘joint limits,’ the Examiner's mapping is notably absent”, this assertion is not consistent with: the claim language which indicates that the “robot configuration data” comprises at least one of end effector type and joint limits; or the rejection (see page 3 of 20 April 2026 Office action) which emphasizes the alternative nature of the claim limitations and brackets the alternative limitation that is not necessarily an aspect of the claimed method.
As to Applicant’s assertion that “The Examiner's Response to Arguments states that Kojima ‘further discloses retrieving robot configuration data as claimed’ without any reasoned explanation of how initial and target orientation inputs constitute robot configuration data comprising effector type or joint limits” and “This conclusory finding does not substantively address Applicant's arguments”, this assertion is not consistent with the written record in that the Response section (see “as set forth in the above rejections” on page 20-21 of 20 April 2026 Office action), clearly refers the reader back to the Claim Rejections section of the Office action.
For these reasons, Applicant’s arguments regarding “a second difference” do not identify a proper basis for finding that any rejection is improper.
Regarding the rejection of Claim 1, Applicant argues (page 4-5 of Response):
As a third (and fundamental) difference, the cited references do not teach or suggest "based on the robot configuration data, the one or more physical properties of the object, and the grasp point data, automatically selecting a path constraint for moving the object from a first location to a second location so as to define a selected path constraint, the selected path constraint defining a grasp pose for the robot to carry the object, a velocity associated with moving the object in the grasp pose, and an acceleration associated with moving the object in the grasp pose."
This element has three distinct requirements that are unmet. First, the path constraint must be selected "based on" all three inputs acting together: robot configuration data, physical properties of the object, and grasp point data. In Kojima, the priority item is selected by the user from a pull- down menu (paragraph [0076], step S32 in Fig. 6) based on the user's own judgment about the task at hand. The initial/target orientation is input in a separate step (step S30), and gripping point calculation occurs in a separate subroutine (Fig. 15). There is no computational link between these data elements and the selection of the priority item. The Examiner has not explained how, in Kojima, the priority item selection is performed "based on" robot configuration data, object physical properties, and grasp point data.
Second, according to claim 1, the selecting must be performed "automatically." The Examiner relics on Terasawa to supply this feature, citing Terasawa paragraphs [0076]-[0077] for the proposition that "embodiments for the constraint condition include speed, acceleration, and joint angle." Applicant respectfully disagrees.
Terasawa paragraph [0077] appears under the heading "Other Embodiments" and states only that a constraint condition "is not limited to an abstract concept of keeping an object horizontal, but it is also possible to set a specific numerical value such as the sound volume, speed, acceleration, or joint angle, degree of freedom of a robot, or the like." This is a brief, aspirational statement of possibility that lacks any working disclosure of a mechanism for implementing the claimed feature, and therefore cannot be said to teach or suggest the claimed path constraint. The breadth and heterogeneity of the list, which includes "sound volume" alongside "speed" and "acceleration", confirms that paragraph [0077] is a loosely drafted catch-all observation about what types of values could theoretically serve as constraint conditions, not a substantive teaching of any particular constraint determination mechanism. Moreover, even under this broader reading of paragraph [0077], the constraint conditions in Terasawa are determined based on task information and object information, not based on the combination of robot configuration data, physical properties of the object, and grasp point data as required by claim 1. This deficiency is structural and paragraph [0077] does nothing to cure it.
Third, the claim requires that the selected path constraint simultaneously defines all three of: a grasp pose, a velocity, and an acceleration. Neither reference teaches such a unified path constraint. In Kojima, "orientation" (grasp pose), "speed" (velocity), and "acceleration" are separate, individually selectable priority items (paragraph [0071]). A user may select multiple priority items at the same time (paragraph [0081]), but each remains a separate prioritization factor, not a unified constraint that defines all three parameters together. In Terasawa, the constraint conditions are primarily qualitative behavioral rules and do not define velocity or acceleration values in any concrete or integrated way.
Comparing teachings of the cited references to the claim language at issue, the cited references teach or suggest “based on the robot configuration data, the one or more physical properties of the object, and the grasp point data, automatically selecting a path constraint for moving the object from a first location to a second location so as to define a selected path constraint, the selected path constraint defining a grasp pose for the robot to carry the object, a velocity associated with moving the object in the grasp pose, and an acceleration associated with moving the object in the grasp pose” in that: Kojima discloses a robot (R) that grips and transports a workpiece (W) (Figs. 3, 6; ¶67, 74-88) based on information including a selected priority item (Pl), the selected priority item (PI) including embodiments describing speed, acceleration, and orientation of the robot (R) and informed by user preferences about positioning of the robot (R), accuracy of movement of the robot (R), properties of the workpiece (W), and orientation of the workpiece (R) as gripped or transported by the robot (R) (Figs. 4-6, 14-15, ¶67, 70-71, 81, 84-85, 129-131); Terasawa discloses, among other features, robotic device (10) that includes a constraint condition determination unit (43) for a that plans a motion trajectory in view of constraints including speed, acceleration and joint angle and task information (Figs. 2, 5; ¶40-43, 51-55, 76-77) by which autonomy of the robotic device (10) is enhanced (¶56-58); and one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Terasawa to the system of Kojima since doing so would enhance autonomy of the system resulting in a system that operates “wherein the selecting is performed automatically” in that Terasawa discloses embodiments in which constraint conditions are determined automatically in response to detected image data. Accordingly, the cited references teach or suggest all limitations in the claim language at issue. As such, Applicant’s argument involves an improper interpretation of the claim language at issue and/or an improper interpretation of the cited references. Therefore, Applicant’s argument does not identify a proper basis for finding that any rejection is improper.
As to Applicant’s assertion that “There is no computational link between these data elements and the selection of the priority item”, this assertion is not relevant to the rejection claim in that the claim does not recite “computational link”.
As to Applicant’s assertion that “The Examiner has not explained how, in Kojima, the priority item selection is performed ‘based on’ robot configuration data, object physical properties, and grasp point data”, this assertion is not consistent with the written record or the teachings of the cited references. Specifically, as set forth in the rejections (see above and page 3-5 of 20 April 2026 Office action), Kojima expressly discloses that the user selects one or more priority items based on considerations including: a desire for the robot (R) to move to a predetermined position as fast as possible, movement for which accuracy is required, cases where a large-mass workpiece W needs to be gripped, and orientation of the workpiece and therefore of the end effector. In this way, Kojima discloses “based on the robot configuration data, the one or more physical properties of the object, and the grasp point data, selecting a path constraint” as claimed. As such, Applicant’s argument involves an improper interpretation of the claim language at issue and/or an improper interpretation of the cited references. Therefore, Applicant’s argument does not identify a proper basis for finding that any rejection is improper.
As to Applicant’s assertions regarding Terasawa, these assertions are not relevant to the rejection of the claim language in that the rejection does not rely solely on paragraph 77 of Terasawa as per Applicant’s arguments.
As to Applicant’s assertion that “Neither reference teaches such a unified path constraint”, this argument is not relevant to the rejection in that the claim does not recite “unified path constraint”.
As to Applicant’s assertion that “In Kojima, ‘orientation’ (grasp pose), ‘speed’ (velocity), and ‘acceleration’ are separate, individually selectable priority items (paragraph [0071])” and “A user may select multiple priority items at the same time (paragraph [0081]), but each remains a separate prioritization factor, not a unified constraint that defines all three parameters together”, this argument is not relevant to the rejection in that the claim does not recite “unified path constraint that defines all three parameters together”.
As to Applicant’s assertion that “In Terasawa, the constraint conditions are primarily qualitative behavioral rules and do not define velocity or acceleration values in any concrete or integrated way”, this argument is not relevant to the rejection in that the claim does not recite “define velocity or acceleration values in any concrete or integrated way”.
For these reasons, Applicant’s arguments regarding “a third difference” do not identify a proper basis for finding that any rejection is improper.
Regarding the rejection of Claim 1, Applicant concludes (page 5 of Response):
Accordingly, Kojima in view of Terasawa fails to teach or fairly suggest the elements of claim 1 in their entirety. At least for the above reasons, independent claim 1, and dependent claims thereof, are respectfully submitted to be patentable over the cited art.
However, as discussed above, none of Applicant’s arguments identify a proper basis for finding that any rejection is improper. Therefore, Applicant’s argument is not well-founded.
Regarding the rejection of Claim 18, Applicant argues (page 5-6 of Response):
Without conceding the Examiner's position on claim 1, Applicant notes that claim 18 adds a further limitation that is independently not met by the cited art. Claim 18 requires that "the one or more physical properties of the objects are selected from the group consisting of: mass, geometric size dimensions, weight distribution and material of the object." The Examiner relies on Terasawa paragraph [0037], which discloses that a "feature amount of the object" including "area, center of gravity, length, and position" can be extracted from image data.
Terasawa's "feature amounts" are image-derived geometric descriptors extracted by processing camera images of a gripped object. "Center of gravity" in this context refers to the centroid of a pixel region in the image, not the physical weight distribution of an object. "Area," "length," and "position" are likewise two-dimensional image features, not physical properties such as mass, weight distribution, or material. Moreover, even if these features could be broadly construed, Terasawa uses them solely to determine qualitative constraint conditions (e.g., "keep the cup horizontal"), not as inputs for selecting a path constraint defining a grasp pose, velocity, and acceleration. At least for this additional reason, claim 18 is respectfully submitted to be patentable over the cited art.
As to Applicant’s assertion that “’Center of gravity’ in this context refers to the centroid of a pixel region in the image, not the physical weight distribution of an object”, this assertion is not relevant to the rejection in that the claim does not recite “physical weight distribution of an object”.
As to Applicant’s assertion that “’Area,’ ‘length,’ and ‘position’ are likewise two-dimensional image features, not physical properties such as mass, weight distribution, or material”, this assertion is not relevant to the rejection in that the claim does not recite “physical properties such as mass, weight distribution, or material”.
As to Applicant’s assertion that “even if these features could be broadly construed, Terasawa uses them solely to determine qualitative constraint conditions (e.g., ‘keep the cup horizontal’), not as inputs for selecting a path constraint defining a grasp pose, velocity, and acceleration”, this assertion is not relevant to the rejection in that the claim does not exclude embodiments involving “qualitative constraint conditions” or recite “inputs for selecting a path constraint defining a grasp pose, velocity, and acceleration”.
Accordingly, none of Applicant’s arguments identify a proper basis for finding that any rejection is improper. Therefore, Applicant’s argument is not well-founded.
Regarding the rejection of Claims 9 and 17, Applicant argues (page 6 of Response):
The arguments presented in connection with claim 1 apply equally to independent claims 9 and 17, which contain corresponding claim elements. At least for the same reasons, independent claims 9 and 17, and dependent claims thereof, are respectfully submitted to be patentable over the cited art.
However, as discussed above, none of Applicant’s arguments for Claim 1 identify a proper basis for finding that any rejection is improper. Therefore, Applicant’s argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lim (US Pub. No. 2012/0165979), Stubbs (US Patent No. 9,669,543), Nagarajan (US Pub. No. 2019/0248003), Ikeda (US Pub. No. 2020/0189097), Nakasu (US Pub. No. 2020/0198137), and Claussen (US Pub. No. 2020/0262064) disclose robot control systems.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STEPHEN HOLWERDA/Primary Examiner, Art Unit 3656
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