DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments filed on 09/11/2025 with respect to claim(s) 1-3 and 5-11 have been fully considered but are not persuasive or moot in view of new ground of rejection provided below which was necessitated based on Applicant’s amendments to the claims. The new ground of rejection for independent claim is based on in combination of Li, Kulkarni, and Nishino. The same reasoning applied to the independent claims also apply to their corresponding dependent claims.
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.
Claim(s) 1-3, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (CN114897102A) (Hereinafter Li) in view of Kulkarni et al. (US 20230321825 A1) (Hereinafter Kulkarni), and further in view of Nishino et al. (US 20220404821 A1) (Hereinafter Nishino).
Regarding Claim 1, Li teaches
A system, comprising:
a processor (See at least Page 13 Para 13 “An embodiment of the present invention
also provides an electronic device, the electronic device includes: a memory and a processor, and a computer program is stored in the memory; when the computer program is executed by the processor, the processor is caused to execute the industrial robot failure as described in any of
the above-mentioned embodiments.”); and
a memory storing machine-readable instructions that, when executed by the processor (See at least Page 13 Para 13 “An embodiment of the present invention also provides an electronic device, the electronic device includes: a memory and a processor, and a computer program is stored in the memory; when the computer program is executed by the processor, the processor is caused to execute the industrial robot failure as described in any of the above-mentioned embodiments.”), cause the processor to:
continuously monitor an electrical current used by a motor of a robot (See at least Page 6 Para 10 “It should be noted that the periodic operation data of the industrial robot includes feedback speed, feedback torque and feedback current, and the feedback speed is based on the speed change when the industrial robot performs an action or stops performing an action.”, Page 2 Para 3 “obtaining the feedback torque or feedback current corresponding to the acceleration operation stage;”, Page 2 Para 3 “The time-frequency domain feature that the feedback torque or the feedback current is in the time-frequency domain is extracted as the operating feature.”);
compare the electrical current (See at least Page 6 Para 2 “Step 105 , compare the feature similarity with a preset similarity threshold, and determine a fault category corresponding to the industrial robot according to the comparison result.”, Examiner notes that electrical current is one of the features according to Page 2 Para 3 “The time-frequency domain feature that the feedback torque or the feedback current is in the time-frequency domain is extracted as the operating feature.”) … ;
identify, from the electrical current and the secondary electrical current (See at least Page 6 Para 2 “Step 105 , compare the feature similarity with a preset similarity threshold, and determine a fault category corresponding to the industrial robot according to the comparison result.”, Page 7 Para 6 “the feedback torque or feedback current corresponding to the acceleration operation stage is obtained from the periodic operation data, and the feedback torque or feedback current is extracted. The time-frequency domain features in the time-frequency domain are used as running features.”, Examiner notes that electrical current is one of the features according to Page 2 Para 3 “The time-frequency domain feature that the feedback torque or the feedback current is in the time-frequency domain is extracted as the operating feature.”), …
However, Li does not explicitly spell out …
compare the electrical current to a secondary current used by the robot;
identify, from the electrical current and the secondary electrical current, a portion of a robot command instruction set that led to premature wear in the robot; and
move the robot via an altered robot command instruction set, an alteration reducing the premature wear.
Kulkarni teaches …
compare the electrical current to a secondary current used by the robot (See at least Claim 7. The robotic system of claim 5, wherein the one or more processors are further configured to: analyze the historical current utilization dataset in connection with learning current utilization levels associated with items too heavy to be lifted by the robotic arm., Fig 15, Fig 16, Para [0364] “FIG. 16 is a diagram or a graph of conveyor current utilization over time according to various embodiments. In the example shown, the system has been trained to determine a current utilization threshold that is used to detect heavy items or whether a weight on the source conveyor exceeds a weight threshold (e.g., a heavy item is on the conveyor or several smaller items). Current utilization 1605 is illustrated in chart 1600 as a function of time. Current utilization threshold 1610 is set (e.g., by training the system or by an administrator, etc.) to be the threshold used to detect heavy items. As illustrated in chart 1600, the current utilization at 1615 may be indicative of a heavy item. In response to detecting current utilization 1605 exceeding current utilization threshold 1610, the system may implement an active measure, such as a subroutine to handle the heavy item and/or stopping/slowing the flow of items to the conveyor while the heavy item is handled. In some embodiments, in response to detecting current utilization 1605 exceeding current utilization threshold 1610, the system implements a verification that the conveyor comprises a heavy item…”) …
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Kulkarni and include the feature of keeping track of a secondary current used by the robot, thereby providing the opportunity of checking premature wear for which robot command instruction is altered to help reduce a premature wear of the robot (See at least Para [0342] “In some embodiments, the system improves the accuracy of detecting heavy items by combining the monitoring of current utilization from a conveyor motor…”).
Nishino teaches …
identify, from the electrical current and the secondary electrical current, a portion of a robot command instruction set that led to premature wear in the robot (See at least Fig 2, Para [0047] “… the facility diagnosis device 100 measures deterioration degrees from the information on the control target portions measured by a predetermined algorithm to specify deteriorated portions, and specifies one or more candidates of the deterioration prevention mode according to the deteriorated portions.”, Para [0049] “The facility diagnosis device 100 includes, as processing units, a deterioration detection unit 101, a deterioration degree prediction unit 103, a KPI calculation unit 104, a deterioration prevention determination unit 105, and a control information output unit 110. The facility diagnosis device 100 includes a deterioration prevention mode definition storage unit 102 as a storage unit.”, Para [0050] “The deterioration detection unit 101 obtains a facility feature amount from the dual-arm robot 200, estimates a deterioration degree of each portion of the dual-arm robot 200, and inputs deteriorated portions and deterioration degrees thereof to the deterioration prevention determination unit 105.”, Para [0051] “FIG. 2 is a diagram illustrating an example of a deterioration detection method. The deterioration detection unit 101 measures a driving current of the motor corresponding to each movable shaft of the dual-arm robot 200 as the facility feature amount by the current sensor serving as the device data acquisition unit 202, and measures a deterioration degree of the motor from power of a harmonic band with respect to a rotation frequency.”); and
move the robot via an altered robot command instruction set, an alteration reducing the premature wear (See at least Para [0057] “The control information output unit 110 outputs control information on the manufacturing facility or the portion of the manufacturing facility according to the deterioration prevention mode that should be executed. Specifically, the control information output unit 110 receives the deterioration prevention mode output from the deterioration prevention determination unit 105, and outputs the control information corresponding to the deterioration prevention mode to the control input unit 201 of the dual-arm robot 200. For example, the control information output unit 110 outputs an instruction to control a load for each of control portions according to the deterioration prevention mode.”, Para [0046] “The facility diagnosis device 100 acquires measurement information obtained from the diagnosis target facility to select a deterioration prevention mode so as to satisfy a necessary KPI, and outputs control information on the diagnosis target facility.”, Para [0029] “In general, manufacturing facilities include various devices, and devices used for manufacturing an industrial product include a robot device including a moveable arm. In addition, manufacturing devices also include a production line device including a plurality of such robot devices and controlling an integrated movement. In the embodiments according to the invention, basically, a dual-arm robot 200 including two movable arms (hereinafter, each arm may be referred to as an arm A or an arm B for convenience) is assumed as the manufacturing device. Each arm has three degrees of freedom, and a gripper for gripping and moving an object is provided at a tip end of the arm.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Nishino and include the feature of identifying from the electrical current and the secondary electrical current, a portion of a robot command instruction set that led to premature wear in the robot and move the robot via an altered robot command instruction set, an alteration reducing the premature wear, thereby provide solution by changing the instruction set leading to premature wear which will help reduce premature wear of the robot (See at least Para [0005] “An object of the invention is to prevent progress of deterioration of a manufacturing facility while keeping a KPI within an allowable range.”).
Regarding Claim 2, modified Li teaches all the elements of claim 1. Li further teaches the system of claim 1, wherein the machine-readable instruction that, when executed by the processor, causes the processor to compare the electrical current to the secondary electrical current used by the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to compare the electrical current to the secondary electrical current using a machine-learning instruction set (See at least Page 5 Para 15 “Step 104: Input the feature sample pair into a preset target twin neural network to obtain the feature similarity corresponding to the feature sample pair.”, Page 5 Para 17- Page 6 Para 1 “In the specific implementation, the running features and fault feature samples in the feature sample pair are input into the preset target twin neural network respectively, and the running feature and fault feature samples are dimensionally reduced through the target twin neural network, and the running feature and fault feature samples are calculated in the low-dimensional space. The Euclidean distance between the feature and the fault feature sample provides the data basis for similarity comparison, so as to obtain the similarity between the running feature and the fault feature sample.”).
Regarding Claim 3, modified Li teaches all the elements of claim 1. Li further teaches the system of claim 1, wherein the machine-readable instruction that, when executed by the processor, causes the processor to identify, from the electrical current and the secondary electrical current, the portion of the robot command instruction set … comprises a machine-readable instruction that, when executed by the processor, causes the processor to identify the portion using a machine-learning instruction set (See at least Page 5 Para 15 “Step 104: Input the feature sample pair into a preset target twin neural network to obtain the feature similarity corresponding to the feature sample pair.”, Page 5 Para 17- Page 6 Para 1 “In the specific implementation, the running features and fault feature samples in the feature sample pair are input into the preset target twin neural network respectively, and the running feature and fault feature samples are dimensionally reduced through the target twin neural network, and the running feature and fault feature samples are calculated in the low-dimensional space. The Euclidean distance between the feature and the fault feature sample provides the data basis for similarity comparison, so as to obtain the similarity between the running feature and the fault feature sample.”, Page 6 Para 2 “Step 105 , compare the feature similarity with a preset similarity threshold, and determine a fault category corresponding to the industrial robot according to the comparison result.”).
However, Li does not explicitly spell out … that led to the premature wear in the robot …
Nishino teaches … that led to the premature wear in the robot (See at least Fig 2, Para [0047]
“… the facility diagnosis device 100 measures deterioration degrees from the information on the control target portions measured by a predetermined algorithm to specify deteriorated portions, and specifies one or more candidates of the deterioration prevention mode according to the deteriorated portions.”, Para [0049] “The facility diagnosis device 100 includes, as processing units, a deterioration detection unit 101, a deterioration degree prediction unit 103, a KPI calculation unit 104, a deterioration prevention determination unit 105, and a control information output unit 110. The facility diagnosis device 100 includes a deterioration prevention mode definition storage unit 102 as a storage unit.”, Para [0050] “The deterioration detection unit 101 obtains a facility feature amount from the dual-arm robot 200, estimates a deterioration degree of each portion of the dual-arm robot 200, and inputs deteriorated portions and deterioration degrees thereof to the deterioration prevention determination unit 105.”, Para [0051] “FIG. 2 is a diagram illustrating an example of a deterioration detection method. The deterioration detection unit 101 measures a driving current of the motor corresponding to each movable shaft of the dual-arm robot 200 as the facility feature amount by the current sensor serving as the device data acquisition unit 202, and measures a deterioration degree of the motor from power of a harmonic band with respect to a rotation frequency.”)…
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the teachings of Li with the teachings of Nishino and include the feature of robot command instruction set that led to the premature wear in the robot, thereby providing the opportunity of checking premature wear for which robot command instruction is altered to help reduce a premature wear of the robot (See at least Para [0005] “An object of the invention is to prevent progress of deterioration of a manufacturing facility while keeping a KPI within an allowable range.”).
Regarding Claim 11, modified Li teaches all the elements of claim 1. Li further teaches the system of claim 1, wherein the machine-readable instruction that, when executed by the processor, causes the processor to identify, from the electrical current and the secondary electrical current (See at least Page 6 Para 2 “Step 105 , compare the feature similarity with a preset similarity threshold, and determine a fault category corresponding to the industrial robot according to the comparison result.”, Page 7 Para 6 “the feedback torque or feedback current corresponding to the acceleration operation stage is obtained from the periodic operation data, and the feedback torque or feedback current is extracted. The time-frequency domain features in the time-frequency domain are used as running features.”, Examiner notes that electrical current is one of the features according to Page 2 Para 3 “The time-frequency domain feature that the feedback torque or the feedback current is in the time-frequency domain is extracted as the operating feature.”),
However, Li does not explicitly spell out … the portion of the robot command instruction set that led to the premature wear in the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to identify the portion of the robot command instruction set that led to premature wear in a functioning robot.
Nishino teaches … the portion of the robot command instruction set that led to the premature wear in the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to identify the portion of the robot command instruction set that led to premature wear in a functioning robot (See at least Fig 2, Para [0010] “FIG. 2 is a diagram illustrating an example of a deterioration detection method.”, Para [0051] “FIG. 2 is a diagram illustrating an example of a deterioration detection method. The deterioration detection unit 101 measures a driving current of the motor corresponding to each movable shaft of the dual-arm robot 200 as the facility feature amount by the current sensor serving as the device data acquisition unit 202, and measures a deterioration degree of the motor from power of a harmonic band with respect to a rotation frequency.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Nishino and include the feature of identifying from the electrical current and the secondary electrical current, a portion of a robot command instruction set that led to premature wear in the robot, thereby provide indication of robotic wear so that an altered command can be applied which will help reduce premature wear of the robot (See at least Para [0005] “An object of the invention is to prevent progress of deterioration of a manufacturing facility while keeping a KPI within an allowable range.”).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al (CN114897102A) (Hereinafter Li) in view of Kulkarni et al. (US 20230321825 A1) (Hereinafter Kulkarni), Nishino et al. (US 20220404821 A1) (Hereinafter Nishino), and further in view of Nakata et al. (US 20190375098 A1) (Hereinafter Nakata).
Regarding Claim 5, modified Li teaches all the elements of claim 1.
However, Li does not explicitly spell out the system of claim 1, wherein:
the secondary electrical current is a current used by the motor at a different point in time;
the portion is identified based on a difference between the electrical current and the secondary electrical current; and
the machine-readable instruction that, when executed by the processor, causes the processor to move the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to change an operation of the motor to reduce the difference between the electrical current and the secondary electrical current.
Kulkarni teaches the system of claim 1, wherein:
the secondary electrical current is a current used by the motor at a different point in time (See at least Claim 7. The robotic system of claim 5, wherein the one or more processors are further configured to: analyze the historical current utilization dataset in connection with learning current utilization levels associated with items too heavy to be lifted by the robotic arm., Fig 15, Fig 16, Para [0364] “FIG. 16 is a diagram or a graph of conveyor current utilization over time according to various embodiments. In the example shown, the system has been trained to determine a current utilization threshold that is used to detect heavy items or whether a weight on the source conveyor exceeds a weight threshold (e.g., a heavy item is on the conveyor or several smaller items). Current utilization 1605 is illustrated in chart 1600 as a function of time. Current utilization threshold 1610 is set (e.g., by training the system or by an administrator, etc.) to be the threshold used to detect heavy items. As illustrated in chart 1600, the current utilization at 1615 may be indicative of a heavy item. In response to detecting current utilization 1605 exceeding current utilization threshold 1610, the system may implement an active measure, such as a subroutine to handle the heavy item and/or stopping/slowing the flow of items to the conveyor while the heavy item is handled. In some embodiments, in response to detecting current utilization 1605 exceeding current utilization threshold 1610, the system implements a verification that the conveyor comprises a heavy item…”);
the portion is identified based on a difference between the electrical current and the secondary electrical current (See at least time (See at least Claim 7. The robotic system of claim 5, wherein the one or more processors are further configured to: analyze the historical current utilization dataset in connection with learning current utilization levels associated with items too heavy to be lifted by the robotic arm., Fig 15, Fig 16, Para [0364] “FIG. 16 is a diagram or a graph of conveyor current utilization over time according to various embodiments. In the example shown, the system has been trained to determine a current utilization threshold that is used to detect heavy items or whether a weight on the source conveyor exceeds a weight threshold (e.g., a heavy item is on the conveyor or several smaller items). Current utilization 1605 is illustrated in chart 1600 as a function of time. Current utilization threshold 1610 is set (e.g., by training the system or by an administrator, etc.) to be the threshold used to detect heavy items. As illustrated in chart 1600, the current utilization at 1615 may be indicative of a heavy item. In response to detecting current utilization 1605 exceeding current utilization threshold 1610, the system may implement an active measure, such as a subroutine to handle the heavy item and/or stopping/slowing the flow of items to the conveyor while the heavy item is handled. In some embodiments, in response to detecting current utilization 1605 exceeding current utilization threshold 1610, the system implements a verification that the conveyor comprises a heavy item…”); and …
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Kulkarni and include the feature of keeping track of a secondary current used by the robot, thereby providing the opportunity of checking premature robot wear for which robot command instruction is altered to help reduce a premature wear of the robot (See at least Para [0342] “In some embodiments, the system improves the accuracy of detecting heavy items by combining the monitoring of current utilization from a conveyor motor…”).
Nakata teaches …
the machine-readable instruction that, when executed by the processor, causes the processor to move the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to change an operation of the motor to reduce the difference between the electrical current and the secondary electrical current (See at least [0105] “FIG. 17 illustrates motor command electric current Im calculated using Equation (11). Although Id1A is added to FIG. 16 in FIG. 17, absolute value Ia of motor command electric current Im does not exceed the maximum allowable value.”, Para [0110] “As described above, a robot control method according to the present exemplary embodiment controls motion of a robot arm by using servo motors connected to reduction gears, and includes: determining whether temperature is less than or equal to a predetermined value; determining whether an absolute value of a motor electric current command is less than or equal to a predetermined value; determining whether a level of detected overload is less than or equal to a predetermined value; and adding d-axis electric current.”, Fig 16, Fig 17).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Nakata and include the feature of changing an operation of the motor to reduce the difference between the electrical current and the secondary electrical current, thereby help reducing a premature wear of the robot.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al (CN114897102A) (Hereinafter Li) in view of Kulkarni et al. (US 20230321825 A1) (Hereinafter Kulkarni), Nishino et al. (US 20220404821 A1) (Hereinafter Nishino), Yoshida et al. (US20200298416A1) (Hereinafter Yoshida), and further in view of Nakata et al. (US 20190375098 A1) (Hereinafter Nakata).
Regarding Claim 6, modified Li teaches all the elements of claim 1. Li further teaches …
the portion is identified based on a difference between the electrical current and the secondary electrical current (See at least Page 6 Para 2 “Step 105 , compare the feature similarity with a preset similarity threshold, and determine a fault category corresponding to the industrial robot according to the comparison result.”, Page 7 Para 6 “the feedback torque or feedback current corresponding to the acceleration operation stage is obtained from the periodic operation data, and the feedback torque or feedback current is extracted. The time-frequency domain features in the time-frequency domain are used as running features.”); …
However, Li does not explicitly spell out the system of claim 1, wherein:
the secondary electrical current is a current used by a different motor of the robot; …
the machine-readable instruction that, when executed by the processor causes the processor to move the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to change an operation of at least one of the motor or the different motor to reduce the difference between the electrical current and the secondary electrical current.
Yoshida teaches … the system of claim 1, wherein:
the secondary electrical current is a current used by a different motor of the robot (See at least
Para [0058] “At least one of the motors (plurality of actuators) mounted respectively on the first to third joint shafts AX1 to AX3 is set as a monitoring target, and at least one type of monitoring parameter is assigned to the or each motor set as the monitoring target. The monitoring parameter is a parameter related to the operation of the motor. In the present embodiment, position deviation, speed deviation, acceleration deviation, and current value related to the operation of the motor are assigned as monitoring parameters”, Para [0070] FIGS. 3A to 6C show examples of ranking results obtained in the ranking step of the monitoring method according to the embodiment of the present invention. In the present embodiment, as shown in FIGS. 3Ato 6C, the ranking is made based on each of the monitoring parameters (position deviation, speed deviation, acceleration deviation, and current value) for each of the motors mounted on the joint shafts (AX1, AX2, and AX3).”, Para [0077] “FIGS. 6A to 6C show an example of a current value ranking result obtained in the ranking step of the monitoring method according to the embodiment of the present invention. The meaning of the current value ranking is the same as those of the position deviation ranking, the speed deviation ranking, and the acceleration deviation ranking, except that the current value ranking is made based on all of the current values detected in the preliminary operation step S1.”, Para [0078]); and …
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the teachings of Li with the teachings of Yoshida and include the feature of secondary electrical current being a current used by a different motor of the robot, thereby providing an environment to facilitate multirobot operation environment by comparing current used by motors of two different robots.
Nakata teaches …
the machine-readable instruction that, when executed by the processor causes the processor to move the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to change an operation of at least one of the motor or the different motor to reduce the difference between the electrical current and the secondary electrical current (See at least Para [0105] “FIG. 17 illustrates motor command electric current Im calculated using Equation (11). Although Id1A is added to FIG. 16 in FIG. 17, absolute value Ia of motor command electric current Im does not exceed the maximum allowable value.”, Para [0110] As described above, a robot control method according to the present exemplary embodiment controls motion of a robot arm by using servo motors connected to reduction gears, and includes: determining whether temperature is less than or equal to a predetermined value; determining whether an absolute value of a motor electric current command is less than or equal to a predetermined value; determining whether a level of detected overload is less than or equal to a predetermined value; and adding d-axis electric current.”, Fig 16, Fig 17).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Nakata and include the feature of change an operation of the motor to reduce the difference between the electrical current and the secondary electrical current, thereby help reducing a premature wear of the robot.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al (CN114897102A) (Hereinafter Li) in view of Kulkarni et al. (US 20230321825 A1) (Hereinafter Kulkarni), Nishino et al. (US 20220404821 A1) (Hereinafter Nishino), Wilson et al. (US 10189159 B1) (Hereinafter Wilson), and further in view of Nagase (JP5966294B2).
Regarding Claim 7, modified Li teaches all the elements of claim 1.
However, Li does not explicitly spell out the system of claim 1, wherein:
the secondary electrical current is a zero current indicating an idle portion of a cycle of the
robot; and
the machine-readable instruction that, when executed by the processor, causes the processor to move the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to reduce an operating speed of the motor to reduce a duration of the idle portion.
Wilson teaches the system of claim 1, wherein:
the secondary electrical current is a zero current indicating an idle portion of a cycle of the robot
(See at least Col 13 Lines 37-40 “based on both the electrical power and the mechanical power being zero, it may be determined that a possible state of operation of the robotic device includes the robotic device being in an idle state.”); and …
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the teachings of Li with the teachings of Wilson and include the feature of the secondary electrical current being a zero current indicating an idle portion of a cycle of the robot, thereby keeping track of the robot’s idle state for calculation to make improvements to the operation of robots, such as manufacturing robots, by enabling the detection of features of the robot command programming/instruction set that may lead to premature wear of the robot, thus elongating the robot's effective life, reducing robot downtime, and overall increasing the productivity of the robot.
Nagase teaches …
the machine-readable instruction that, when executed by the processor, causes the processor to move the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to reduce an operating speed of the motor to reduce a duration of the idle portion (See at least Page 3 Para 3 “This includes an emergency operation of moving the arm 32 in the direction to retract the arm 32. Further, as an emergency operation, for example, the control unit 21 moves the arm unit 32 at a speed slower than the moving speed of the arm unit 32 at the normal time”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Nagase and include the feature of reducing an operating speed of the motor, thereby providing improvements to the operation of robots, such as manufacturing robots, by enabling the detection of features of the robot command programming/instruction set that may lead to premature wear of the robot, thus elongating the robot's effective life, reducing robot downtime, and overall increasing the productivity of the robot (See at least Page 1 Para 5 “The present invention has been made to solve the above-described problems, and an object thereof is to provide an abnormality determination device, a drive device, and a robot device capable of performing abnormality determination in a shorter time.”).
Claim(s) 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (CN114897102A) (Hereinafter Li) in view of Kulkarni et al. (US 20230321825 A1) (Hereinafter Kulkarni), Nishino et al. (US 20220404821 A1) (Hereinafter Nishino), and further in view of Yoshida et al. (US20200298416A1) (Hereinafter Yoshida).
Regarding Claim 8, modified Li teaches all the elements of claim 1.
However, Li does not explicitly spell out the system of claim 1, wherein the machine-readable
instruction that, when executed by the processor, causes the processor to continuously monitor the electrical current used by the motor of the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to continuously monitor the electrical current used by each joint motor of a multi-axis robot.
Yoshida teaches the system of claim 1, wherein the machine-readable instruction that,
when executed by the processor, causes the processor to continuously monitor the electrical current used by the motor of the robot comprises a machine-readable instruction that, when executed by the processor, causes the processor to continuously monitor the electrical current used by each joint motor of a multi-axis robot (See at least Para [0058] “At least one of the motors (plurality of actuators) mounted respectively on the first to third joint shafts AX1 to AX3 is set as a monitoring target, and at least one type of monitoring parameter is assigned to the or each motor set as the monitoring target. The monitoring parameter is a parameter related to the operation of the motor. In the present embodiment, position deviation, speed deviation, acceleration deviation,
and current value related to the operation of the motor are assigned as monitoring parameters”, Para [0070] FIGS. 3A to 6C show examples of ranking results obtained in the ranking step of the monitoring method according to the embodiment of the present invention. In the present embodiment, as shown in FIGS. 3Ato 6C, the ranking is made based on each of the monitoring parameters (position deviation, speed deviation, acceleration deviation, and current value) for each of the motors mounted on the joint shafts (AX1, AX2, and AX3).”, Para [0077] “FIGS. 6A to 6C show an example of a current value ranking result obtained in the ranking step of the monitoring method according to the embodiment of the present invention. The meaning of the current value ranking is the same as those of the position deviation ranking, the speed deviation ranking, and the acceleration deviation ranking, except that the current value ranking is made based on all of the current values detected in the preliminary operation step S1.”, Para [0078])).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Yoshida and include the feature of continuously monitoring the electrical current used by each joint motor of a multi-axis robot, thereby provide precise and accurate calculation to help reducing a premature wear of the robot.
Regarding Claim 9, modified Li teaches all the elements of claim 1.
However, Li does not explicitly spell out the system of claim 1, wherein the machine-readable
instructions further comprise:
a machine-readable instruction that, when executed by the processor, causes the processor to rank operating inefficiencies of multiple motors of the robot; and
a machine-readable instruction that, when executed by the processor, causes the processor to select, based on a ranking, the motor of the robot for which an alteration is generated.
Yoshida teaches the system of claim 1, wherein the machine-readable instructions further
comprise:
a machine-readable instruction that, when executed by the processor, causes the processor to rank operating inefficiencies of multiple motors of the robot (See at least Para [0093] “In the present embodiment, once the ranking step S2 is completed, a notification of completion of the ranking step S2 may be provided. This allows the user to know the completion of the ranking step S2. Thus, for example, the user can, before start of the normal operation step S3, take measures such as performing a maintenance work on the motors that operate in the process phases in which the highly ranked monitoring parameters were detected.”, Para [0058] “At least one of the motors (plurality of actuators) mounted respectively on the first to third joint shafts AX1 to AX3 is set as a monitoring target, and at least one type of monitoring parameter is assigned to the or each motor set as the monitoring target. The monitoring parameter is a parameter related to the operation of the motor. In the present embodiment, position deviation, speed deviation, acceleration deviation,
and current value related to the operation of the motor are assigned as monitoring parameters”, Para [0070] FIGS. 3A to 6C show examples of ranking results obtained in the ranking step of the monitoring method according to the embodiment of the present invention. In the present embodiment, as shown in FIGS. 3Ato 6C, the ranking is made based on each of the monitoring parameters (position deviation, speed deviation, acceleration deviation, and current value) for each of the motors mounted on the joint shafts (AX1, AX2, and AX3).”, Para [0077] “FIGS. 6A to 6C show an example of a current value ranking result obtained in the ranking step of the monitoring method according to the embodiment of the present invention. The meaning of the current value ranking is the same as those of the position deviation ranking, the speed deviation ranking, and the acceleration deviation ranking, except that the current value ranking is made based on all of the current values detected in the preliminary operation step S1.”, Para [0078] “In the present embodiment, when the current value ranking is made for the motor of the first joint shaft AX1, the result as shown in FIGS. 6A to 6C is obtained; namely, the seventh process phase in which the load level is 97% is ranked first, the eleventh process phase in which the load level is 95% is ranked second, and the twenty-ninth process phase in which the load level is 89% is ranked third. For the motor of the second joint shaft AX2, the twentieth process phase in which the load level is 91% is ranked first, the twenty-second process phase in which the load level is 81% is ranked second, and the thirtieth process phase in which the load level is 79% is ranked third. For the motor of the third joint shaft AX3, the twenty-first process phase in which the load level is 90% is ranked first, the seventeenth process phase in which the load level is 79% is ranked second, and the forty-third process phase in which the load level is 77% is ranked third.”); and
a machine-readable instruction that, when executed by the processor, causes the processor to select, based on a ranking, the motor of the robot for which an alteration is generated (See at least Para [0093] “In the present embodiment, once the ranking step S2 is completed, a notification of completion of the ranking step S2 may be provided. This allows the user to know the completion of the ranking step S2. Thus, for example, the user can, before start of the normal operation step S3, take measures such as performing a maintenance work on the motors that operate in the process phases in which the highly ranked monitoring parameters were detected.”, Para [0078] “In the present embodiment, when the current value ranking is made for the motor of the first joint shaft AX1, the result as shown in FIGS. 6A to 6C is obtained; namely, the seventh process phase in which the load level is 97% is ranked first, the eleventh process phase in which the load level is 95% is ranked second, and the twenty-ninth process phase in which the load level is 89% is ranked third. For the motor of the second joint shaft AX2, the twentieth process phase in which the load level is 91% is ranked first, the twenty-second process phase in which the load level is 81% is ranked second, and the thirtieth process phase in which the load level is 79% is ranked third. For the motor of the third joint shaft AX3, the twenty-first process phase in which the load level is 90% is ranked first, the seventeenth process phase in which the load level is 79% is ranked second, and the forty-third process phase in which the load level is 77% is ranked third.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Yoshida and include the feature of ranking operating inefficiencies of multiple motors of the robot and selecting, based on a ranking, the motor of the robot for which an alteration is generated, thereby providing improvements to the operation of robots, such as manufacturing robots, by enabling the detection of features of the robot command programming/instruction set that may lead to premature wear of the robot, thus elongating the robot's effective life, reducing robot downtime, and overall increasing the productivity of the robot (See at least Para [0008] “… a robot monitoring method can be provided which is able to reduce the amount of data to be handled as compared to conventional methods and thereby solve various problems”).
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al (CN114897102A) (Hereinafter Li) in view of Kulkarni et al. (US 20230321825 A1) (Hereinafter Kulkarni), Nishino et al. (US 20220404821 A1) (Hereinafter Nishino), Heredia et al. (J. Heredia, C. Schlette and M. B. Kjærgaard, "Breaking Down the Energy Consumption of Industrial and Collaborative Robots: A Comparative Study," 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Sinaia, Romania, 2023, pp. 1-8) (Hereinafter Heredia), and further in view of Yoshida et al. (US20200298416A1) (Hereinafter Yoshida).
24. Regarding Claim 10, modified Li teaches all the elements of claim 1.
However, Li does not explicitly spell out the system of claim 1, wherein the machine-readable
instructions further comprise:
a machine-readable instruction that, when executed by the processor, causes the processor to rank operating inefficiencies of multiple motors of the robot; and
a machine-readable instruction that, when executed by the processor, causes the processor to select, based on a ranking, the motor of the robot for which an alteration is generated.
Heredia teaches … of different robots (See at least Page 6 Para 2 – Para 3 “SECTION IV. COMPARISON BETWEEN COBOT AND IR ENERGY CONSUMPTION - … compares a Cobot’s and an IR’s energetic behavior, applying the proposed criteria (payload, joint configurations, movement commands, velocity and acceleration limits, trajectory planning, and joint temperatures). The comparative analysis focuses on determining the influence of the above evaluation criteria on robot EC. IRs have higher payload capacity, velocity, and acceleration limits; therefore, IRs evidently consume more energy than Cobots. Quantitative analysis of the EC on the different scenarios would reflect that IRs consume more energy. Normalized metrics are used to perform a qualitative analysis of the evaluation parameters’ influence.”)…
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Heredia and include the feature of motors of different robots, thereby providing improvements to the operation of different robots, such as manufacturing robots, by enabling the detection of features of the robot command programming/instruction set that may lead to premature wear of robots, thus elongating the robots’ effective life, reducing robot downtime, and overall increasing the productivity of different robots (See at least Para [0008] “… a robot monitoring method can be provided which is able to reduce the amount of data to be handled as compared to conventional methods and thereby solve various problems”).
Yoshida teaches the system of claim 1, wherein the machine-readable instructions further
comprise: a machine-readable instruction that, when executed by the processor, causes the processor to rank operating inefficiencies of multiple motors (See at least Para [0093] “In the present embodiment, once the ranking step S2 is completed, a notification of completion of the ranking step S2 may be provided. This allows the user to know the completion of the ranking step S2. Thus, for example, the user can, before start of the normal operation step S3, take measures such as performing a maintenance work on the motors that operate in the process phases in which the highly ranked monitoring parameters were detected.”, Para [0078] “In the present embodiment, when the current value ranking is made for the motor of the first joint shaft AX1, the result as shown in FIGS. 6A to 6C is obtained; namely, the seventh process phase in which the load level is 97% is ranked first, the eleventh process phase in which the load level is 95% is ranked second, and the twenty-ninth process phase in which the load level is 89% is ranked third. For the motor of the second joint shaft AX2, the twentieth process phase in which the load level is 91% is ranked first, the twenty-second process phase in which the load level is 81% is ranked second, and the thirtieth process phase in which the load level is 79% is ranked third. For the motor of the third joint shaft AX3, the twenty-first process phase in which the load level is 90% is ranked first, the seventeenth process phase in which the load level is 79% is ranked second, and the forty-third process phase in which the load level is 77% is ranked third.”) …; and …
a machine-readable instruction that, when executed by the processor, causes the processor to select, based on a ranking, the motor of the robot for which an alteration is generated (See at least Para [0093] “In the present embodiment, once the ranking step S2 is completed, a notification of completion of the ranking step S2 may be provided. This allows the user to know the completion of the ranking step S2. Thus, for example, the user can, before start of the normal operation step S3, take measures such as performing a maintenance work on the motors that operate in the process phases in which the highly ranked monitoring parameters were detected.”, Para [0078] “In the present embodiment, when the current value ranking is made for the motor of the first joint shaft AX1, the result as shown in FIGS. 6A to 6C is obtained; namely, the seventh process phase in which the load level is 97% is ranked first, the eleventh process phase in which the load level is 95% is ranked second, and the twenty-ninth process phase in which the load level is 89% is ranked third. For the motor of the second joint shaft AX2, the twentieth process phase in which the load level is 91% is ranked first, the twenty-second process phase in which the load level is 81% is ranked second, and the thirtieth process phase in which the load level is 79% is ranked third. For the motor of the third joint shaft AX3, the twenty-first process phase in which the load level is 90% is ranked first, the seventeenth process phase in which the load level is 79% is ranked second, and the forty-third process phase in which the load level is 77% is ranked third.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with the teachings of Yoshida and include the feature of ranking operating inefficiencies of multiple motors of the robot and selecting, based on a ranking, the motor of the robot for which an alteration is generated, thereby providing improvements to the operation of robots, such as manufacturing robots, by enabling the detection of features of the robot command programming/instruction set that may lead to premature wear of the robot, thus elongating the robot's effective life, reducing robot downtime, and overall increasing the productivity of the robot (See at least Para [0008] “… a robot monitoring method can be provided which is able to reduce the amount of data to be handled as compared to conventional methods and thereby solve various problems”).
Conclusion
25. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kuno et al. (US 20180154530 A1) teaches a failure diagnosis device and a method thereof, which are capable of improving failure diagnosis accuracy by eliminating effects of one-off abnormal values, and of diagnosing a failure with a low-cost system configuration.
26. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
27. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHEDA HOQUE whose telephone number is (571)270-5310. The examiner can normally be reached Monday-Friday 8:00 am- 5:00 pm.
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/SHAHEDA HOQUE/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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