DETAILED ACTION
Request for Continued Examination received 16 March 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 maxi- mum 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 Yamazai.
Response to Arguments
Applicant's arguments filed 20 February 2026 have been fully considered as follows.
Applicant argues that “The claim recitation ‘retrieve a model of the object …’ now clearly distinguishes over Kojima (see paragraphs [0129-130]) which discloses obtaining spatial position information regarding the workpiece (not an intrinsic property) through imaging and measurement, as acknowledged by the Examiner (Final Office Action, page 19)” (Remarks at “Page 2 of 4”). Consistent with the previous claim language, Kojima discloses obtaining spatial position information regarding the workpiece. Consistent with the amended claim language and as set forth in the above rejections, Kojima further discloses retrieving a model indicating intrinsic properties of the object as claimed. Accordingly, Applicant’s argument involves an improperly narrow interpretation of the teachings of the cited references. Therefore, Applicant’s argument does not identify a proper basis for finding that any rejection is improper.
Applicant argues that “The claim recitation ‘retrieving robot configuration data …’ now clearly distinguishes over Kojima (see paragraphs [0067] and [0122]) which discloses describes receiving initial and target orientations, which is motion endpoint data, not robot configuration data, as acknowledged by the Examiner (Final Office Action, page 20)” (Remarks at “Page 3 of 4”). Consistent with the previous claim language, Kojima discloses receiving initial and target orientations. Consistent with the amended claim language and as set forth in the above rejections, Kojima further discloses retrieving robot configuration data as claimed. Accordingly, Applicant’s argument involves an improperly narrow interpretation of the teachings of the cited references. Therefore, Applicant’s argument does not identify a proper basis for finding that any rejection is improper.
Applicant argues that “neither Terasawa nor any of the other cited references cure the above-stated deficiencies of Kojima” and “The combination of the cited references does not teach or fairly suggest the features of claims 1 and 9 in their entirety” (Remarks at “Page 3 of 4”). However, as discussed above, the alleged deficiencies are not present in Kojima and the combination of cited references teaches or suggests all limitations in the claims. Therefore, Applicants’ argument is moot.
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.
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/STEPHEN HOLWERDA/Primary Examiner, Art Unit 3656
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