DETAILED ACTION
This action is responsive to the preliminary amendment filed 04/19/2024.
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 .
Status of the Claims
Claims 1-16 and 19-20 are rejected under 35 U.S.C. 103.
Claims 7-8 are rejected under 35 U.S.C. 101.
Claims 17-18 are cancelled.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to software per se. Claim 7 recites “A program” in the preamble. While the claim recites that the program is used by an information processing device to execute a method and that the device is connected to a production apparatus, the claim is directed to the program without any structural limitations that implement the program into a device other than software. Since the claim is directed to software, it does not fall under one of the four categories of patent eligible subject matter. See MPEP 2106.03.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to signals per se. Claim 8 recites “a recording medium”. However, neither the claim or the specification limits the recording medium to only encompassing non-transitory embodiments. Since the BRI of “recording medium” can include transitory embodiments, such as carrier waves, the claim can be construed as being directed to signals per se, which are not subject matter eligible. See MPEP 2106.03.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims are rejected under 35 U.S.C. 103 as being unpatentable over YAMAOKA (US 2018/0207797 A1) in view of TSUNEKI (US 2020/0133226 A1).
Regarding Claim 1, YAMAOKA teaches a method for generating a control parameter used in a production apparatus including (¶ 8, Figs. 1, 3: A correction amount, which is a control parameter, is learned to control the vibration of a robot used in a production apparatus.)
a servomotor… and a driven object driven by the servomotor, (¶ 38, Fig. 1: A certain portion of a robot is a driven object that is driven by a servomotor.)
a control circuit for controlling the servomotor, (¶ 39: The control device 12 is a control circuit for controlling the servomotor.)
a memory for storing the control parameter used when the control circuit controls the servomotor… (¶ 39, 42: A storage unit 19 stores the correction parameters.)
the method comprising: acquiring measurement data indicating a position of the driven object from a sensor that measures the position of the driven object, (¶ 43-47: A sensor is used to acquire measurement data indicating a position of the driven object (i.e. a portion of the robot). Vibration data is calculated from the position data based on a difference between the actual position of the servomotor and the detected position of the driven object.)
after an arrival time at which the driven object reaches an admittable position based on a command for moving the position of the driven object to a predetermined target position, wherein the admittable position indicates a position where the driven object is evaluated as having reached the predetermined target position; (¶ 50-51, 112, Fig. 12: The correction amount is determined based on a convergence value determined from a plurality of metrics. A metric for determining that the vibration or position data converges is stabilization time. A stabilization time is the time for the vibration data to be within a certain tolerance of a final position, as illustrated in Fig. 12.)
While YAMAOKA teaches a learning unit for determining a vibration correction amount to be used in subsequent control operations for the servomotor (¶ 41-43), YAMAOKA does not teach generating, based on the measurement data, evaluation index data indicating vibration of the driven object occurring after the arrival time; and updating the control parameter based on the evaluation index data, using a machine learning model that learns a relationship between the evaluation index data and the control parameter.
However, TSUNEKI, which is directed to learning control parameters for controlling a servomotor, teaches generating, based on the measurement data, evaluation index data indicating vibration of the driven object occurring after the arrival time; (¶ 139-143: An evaluation function is generated that indicates vibration of the driven object by the servomotor since the function is based on positional error, i.e. vibration of the servomotor.)
and updating the control parameter based on the evaluation index data, using a machine learning model that learns a relationship between the evaluation index data and the control parameter. (¶ 138-139, 156-157, 161-162, 166: A machine learning unit uses reinforcement learning to learn the optimal parameters for reducing the positional error using the evaluation index data. A visualization of the evaluation index data is also provided, along with the generated control parameters (namely, coefficients a-b) in Fig. 9A.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the determination of a vibration correction amount using machine learning based on measured positional data of a servomotor after a stabilization time taught by YAMAOKA by using the evaluation index and the reinforcement learning techniques for determining a relationship between the control parameters and the evaluation index as taught by TSUNEKI. Since the references similarly use machine learning and are directed to optimizing the parameters for controlling a servomotor, the combination would have yielded predictable results. Furthermore, TSUNEKI teaches such an implementation would allow an operator to understand the functions being used by a learning unit to control a servomotor (¶ 19) and to control the vibration of the servomotor as desired (¶ 166).
Regarding Claim 2, YAMAOKA in view of TSUNEKI further teaches further comprising outputting, to the production apparatus, the updated control parameter for storage in the memory. (YAMAOKA, ¶ 41-42, 48: The vibration correction amount control parameter is updated with each iteration and the latest correction amount is stored in the storage unit 19 (Fig. 1).)
Regarding Claim 3, YAMAOKA in view of TSUNEKI further teaches wherein the evaluation index data indicates a sum of an area surrounded by i) a deviation waveform indicating vibration of the driven object occurring after the arrival time and ii) a reference axis. (TSUNEKI, ¶ 119-122, 139-143: Control coefficients are optimized based on a position error signal, which is a deviation waveform that indicates vibration of the driven object in the machining process. The error signal is integrated, which indicates a sum of an area surrounded by the position signal and a reference axis, such as time.)
The same motivation to combine discussed in the rejection of claim 1 applies to claim 3.
Regarding Claim 4, YAMAOKA in view of TSUNEKI further teaches wherein the evaluation index data indicates a variation degree of a deviation waveform indicating vibration of the driven object occurring after the arrival time. (TSUNEKI, ¶ 121, 139-143, 166: The evaluation index includes an absolute value of the position error signal, which is a deviation waveform indicating vibration of the driven object occurring after the stabilization time.)
The same motivation to combine discussed in the rejection of claim 1 applies to claim 4.
Regarding Claim 5, YAMAOKA in view of TSUNEKI further teaches wherein the evaluation index data indicates an effective value of a deviation waveform indicating vibration of the driven object occurring after the arrival time. (TSUNEKI, ¶ 121, 139-143, 166: The evaluation index includes a weighted position error signal, which is an “effective value” of a deviation waveform indicating vibration of the driven object occurring after the stabilization time.)
The same motivation to combine discussed in the rejection of claim 1 applies to claim 5.
Regarding Claim 6, YAMAOKA in view of TSUNEKI further teaches wherein the production apparatus includes at least one of a mounting device, a processing device, and a machining device. (YAMAOKA, ¶ 42, 56, Fig. 1: The production apparatus includes a robot, which is a machining or processing device including end effectors, such as welding guns, lifting hands, or screwers.)
Regarding Claim 7, YAMAOKA in view of TSUNEKI further teaches a program for causing a computer of an information processing device to execute the method according to claim 1, the information processing device being connected to the production apparatus. (YAMAOKA, ¶ 41-42: A program is executed by the learning control processing unit, which is connected to the production apparatus comprising the robot.)
Regarding Claim 8, YAMAOKA in view of TSUNEKI further teaches a recording medium recording a program for causing a computer of an information processing device to execute the method according to claim 1, the information processing device being connected to the production apparatus. (YAMAOKA, ¶ 41-42: A program is executed by the learning control processing unit, which includes a storage unit for storing the program and is connected to the production apparatus comprising the robot.)
Regarding Claim 9, YAMAOKA teaches a device for generating a control parameter used in a production apparatus (¶ 8, Figs. 1, 3: A correction amount, which is a control parameter, is learned to control the vibration of a robot used in a production apparatus.)
including a servomotor… and a driven object driven by the servomotor, (¶ 38, Fig. 1: A certain portion of a robot is a driven object that is driven by a servomotor.)
a first control circuit for controlling the servomotor, (¶ 39: The control device 12 is a control circuit for controlling the servomotor.)
a memory for storing a control parameter used when the first control circuit controls the servomotor… (¶ 39, 42: A storage unit 19 stores the correction parameters.)
the device comprising: an input unit configured to acquire measurement data indicating a position of the driven object from a sensor that measures the position of the driven object (¶ 43-47: A sensor is used to acquire measurement data indicating a position of the driven object (i.e. a portion of the robot). Vibration data is calculated from the position data based on a difference between the actual position of the servomotor and the detected position of the driven object.)
after an arrival time at which the driven object reaches an admittable position based on a command for moving the position of the driven object to a predetermined target position, wherein the admittable position indicates a position where the driven object is evaluated as having reached the predetermined target position; (¶ 50-51, 112, Fig. 12: The correction amount is determined based on a convergence value determined from a plurality of metrics. A metric for determining that the vibration or position data converges is stabilization time. A stabilization time is the time for the vibration data to be within a certain tolerance of a final position, as illustrated in Fig. 12.)
While YAMAOKA teaches a learning unit for determining a vibration correction amount to be used in subsequent control operations for the servomotor (¶ 41-43), YAMAOKA does not teach and a second control circuit configured to generate, based on the measurement data, evaluation index data indicating vibration of the driven object occurring after the arrival time, and to update the control parameter based on the evaluation index data using a machine learning model that learns a relationship between the evaluation index data and the control parameter.
However, TSUNEKI, which is directed to learning control parameters for controlling a servomotor, teaches and a second control circuit configured to generate, based on the measurement data, evaluation index data indicating vibration of the driven object occurring after the arrival time, (¶ 139-143: An evaluation function is generated that indicates vibration of the driven object by the servomotor since the function is based on positional error, i.e. vibration of the servomotor.)
and to update the control parameter based on the evaluation index data using a machine learning model that learns a relationship between the evaluation index data and the control parameter. (¶ 138-139, 156-157, 161-162, 166: A machine learning unit uses reinforcement learning to learn the optimal parameters for reducing the positional error using the evaluation index data. A visualization of the evaluation index data is also provided, along with the generated control parameters (namely, coefficients a-b) in Fig. 9A.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the determination of a vibration correction amount using machine learning based on measured positional data of a servomotor after a stabilization time taught by YAMAOKA by using the evaluation index and the reinforcement learning techniques for determining a relationship between the control parameters and the evaluation index as taught by TSUNEKI. Since the references similarly use machine learning and are directed to optimizing the parameters for controlling a servomotor, the combination would have yielded predictable results. Furthermore, TSUNEKI teaches such an implementation would allow an operator to understand the functions being used by a learning unit to control a servomotor (¶ 19) and to control the vibration of the servomotor as desired (¶ 166).
Regarding Claim 10, YAMAOKA in view of TSUNEKI further teaches further comprising an output unit configured to output, to the production apparatus, the updated control parameter for storage in the memory. (YAMAOKA, ¶ 41-42, 48: The vibration correction amount control parameter is updated with each iteration and the latest correction amount is stored in the storage unit 19 (Fig. 1).)
Regarding Claim 11, YAMAOKA teaches a method for generating a control parameter used in a production apparatus (¶ 8, Figs. 1, 3: A correction amount, which is a control parameter, is learned to control the vibration of a robot used in a production apparatus.)
including a servomotor… and a driven object driven by the servomotor, (¶ 38, Fig. 1: A certain portion of a robot is a driven object that is driven by a servomotor.)
a control circuit for controlling the servomotor, (¶ 39: The control device 12 is a control circuit for controlling the servomotor.)
a memory for storing a control parameter used when the control circuit controls the servomotor, (¶ 39, 42: A storage unit 19 stores the correction parameters.)
the method comprising: acquiring, from a sensor provided with a processing part that measures a position of the driven object… (¶ 43-47: A sensor is used to acquire measurement data indicating a position of the driven object (i.e. a portion of the robot). Vibration data is calculated from the position data based on a difference between the actual position of the servomotor and the detected position of the driven object.)
occurring after an arrival time at which the driven object reaches an admittable position based on a command for moving the position of the driven object to a predetermined target position, wherein the admittable position indicates a position where the driven object is evaluated as having reached the predetermined target position; (¶ 50-51, 112, Fig. 12: The correction amount is determined based on a convergence value determined from a plurality of metrics. A metric for determining that the vibration or position data converges is stabilization time. A stabilization time is the time for the vibration data to be within a certain tolerance of a final position, as illustrated in Fig. 12.)
While YAMAOKA teaches a learning unit for determining a vibration correction amount to be used in subsequent control operations for the servomotor (¶ 41-43), YAMAOKA does not teach evaluation index data that indicates vibration of the driven object… and updating the control parameter based on the evaluation index data, using a machine learning model that learns a relationship between the evaluation index data and the control parameter.
However, TSUNEKI, which is directed to learning control parameters for controlling a servomotor, teaches evaluation index data that indicates vibration of the driven object… (¶ 139-143: An evaluation function is generated that indicates vibration of the driven object by the servomotor since the function is based on positional error, i.e. vibration of the servomotor.)
and updating the control parameter based on the evaluation index data, using a machine learning model that learns a relationship between the evaluation index data and the control parameter. (¶ 138-139, 156-157, 161-162, 166: A machine learning unit uses reinforcement learning to learn the optimal parameters for reducing the positional error using the evaluation index data. A visualization of the evaluation index data is also provided, along with the generated control parameters (namely, coefficients a-b) in Fig. 9A.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the determination of a vibration correction amount using machine learning based on measured positional data of a servomotor after a stabilization time taught by YAMAOKA by using the evaluation index and the reinforcement learning techniques for determining a relationship between the control parameters and the evaluation index as taught by TSUNEKI. Since the references similarly use machine learning and are directed to optimizing the parameters for controlling a servomotor, the combination would have yielded predictable results. Furthermore, TSUNEKI teaches such an implementation would allow an operator to understand the functions being used by a learning unit to control a servomotor (¶ 19) and to control the vibration of the servomotor as desired (¶ 166).
Regarding Claim 12, YAMAOKA in view of TSUNEKI further teaches further comprising outputting, to the production apparatus, the updated control parameter for storage in the memory. (YAMAOKA, ¶ 41-42, 48: The vibration correction amount control parameter is updated with each iteration and the latest correction amount is stored in the storage unit 19 (Fig. 1).)
Regarding Claim 13, YAMAOKA in view of TSUNEKI further teaches wherein the evaluation index data indicates a sum of an area surrounded by i) a deviation waveform indicating vibration of the driven object occurring after the arrival time and ii) a reference axis. (TSUNEKI, ¶ 119-122, 139-143: Control coefficients are optimized based on a position error signal, which is a deviation waveform that indicates vibration of the driven object in the machining process. The error signal is integrated, which indicates a sum of an area surrounded by the position signal and a reference axis, such as the time axis.)
The same motivation to combine discussed in the rejection of claim 11 applies to claim 13.
Regarding Claim 14, YAMAOKA in view of TSUNEKI further teaches wherein the evaluation index data indicates a variation degree of a deviation waveform indicating vibration of the driven object occurring after the arrival time. (TSUNEKI, ¶ 121, 139-143, 166: The evaluation index includes an absolute value of the position error signal, which is a deviation waveform indicating vibration of the driven object occurring after the stabilization time.)
The same motivation to combine discussed in the rejection of claim 11 applies to claim 14.
Regarding Claim 15, YAMAOKA in view of TSUNEKI further teaches wherein the evaluation index data indicates an effective value of a deviation waveform indicating vibration of the driven object occurring after the arrival time. (TSUNEKI, ¶ 121, 139-143, 166: The evaluation index includes a weighted position error signal, which is an “effective value” of a deviation waveform indicating vibration of the driven object occurring after the stabilization time.)
The same motivation to combine discussed in the rejection of claim 11 applies to claim 15.
Regarding Claim 16, YAMAOKA in view of TSUNEKI further teaches wherein the production apparatus includes at least one of a mounting device, a processing device, and a machining device. (YAMAOKA, ¶ 42, 56, Fig. 1: The production apparatus includes a robot, which is a machining or processing device including end effectors, such as welding guns, lifting hands, or screwers.)
Regarding Claim 19, YAMAOKA teaches a device for generating a control parameter used in a production apparatus (¶ 8, Figs. 1, 3: A correction amount, which is a control parameter, is learned to control the vibration of a robot used in a production apparatus.)
including a servomotor… and a driven object driven by the servomotor (¶ 38, Fig. 1: A certain portion of a robot is a driven object that is driven by a servomotor.)
a first control circuit for controlling the servomotor, (¶ 39: The control device 12 is a control circuit for controlling the servomotor.)
a memory for storing a control parameter used when the first control circuit controls the servomotor… (¶ 39, 42: A storage unit 19 stores the correction parameters.)
the device comprising: an input unit configured to acquire, from a sensor provided with a processing part that measures a position of the driven object… (¶ 43-47: A sensor is used to acquire measurement data indicating a position of the driven object (i.e. a portion of the robot). Vibration data is calculated from the position data based on a difference between the actual position of the servomotor and the detected position of the driven object.)
occurred after an arrival time at which the driven object reaches an admittable position based on a command for moving the position of the driven object to a predetermined target position, wherein the admittable position indicates a position where the driven object is evaluated as having reached the predetermined target position; (¶ 50-51, 112, Fig. 12: The correction amount is determined based on a convergence value determined from a plurality of metrics. A metric for determining that the vibration or position data converges is stabilization time. A stabilization time is the time for the vibration data to be within a certain tolerance of a final position, as illustrated in Fig. 12.)
While YAMAOKA teaches a learning unit for determining a vibration correction amount to be used in subsequent control operations for the servomotor (¶ 41-43), YAMAOKA does not teach evaluation index data that indicates vibration of the driven object… and a second control circuit configured to update the control parameter based on the evaluation index data, using a machine learning model that learns a relationship between the evaluation index data and the control parameter.
However, TSUNEKI, which is directed to learning control parameters for controlling a servomotor, teaches evaluation index data that indicates vibration of the driven object… (¶ 139-143: An evaluation function is generated that indicates vibration of the driven object by the servomotor since the function is based on positional error, i.e. vibration of the servomotor.)
and a second control circuit configured to update the control parameter based on the evaluation index data, using a machine learning model that learns a relationship between the evaluation index data and the control parameter. (¶ 138-139, 156-157, 161-162, 166: A machine learning unit uses reinforcement learning to learn the optimal parameters for reducing the positional error using the evaluation index data. A visualization of the evaluation index data is also provided, along with the generated control parameters (namely, coefficients a-b) in Fig. 9A.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the determination of a vibration correction amount using machine learning based on measured positional data of a servomotor after a stabilization time taught by YAMAOKA by using the evaluation index and the reinforcement learning techniques for determining a relationship between the control parameters and the evaluation index as taught by TSUNEKI. Since the references similarly use machine learning and are directed to optimizing the parameters for controlling a servomotor, the combination would have yielded predictable results. Furthermore, TSUNEKI teaches such an implementation would allow an operator to understand the functions being used by a learning unit to control a servomotor (¶ 19) and to control the vibration of the servomotor as desired (¶ 166).
Regarding Claim 20, YAMAOKA in view of TSUNEKI further teaches further comprising an output unit configured to output, to the production apparatus, the updated control parameter for storage in the memory. (YAMAOKA, ¶ 41-42, 48: The vibration correction amount control parameter is updated with each iteration and the latest correction amount is stored in the storage unit 19 (Fig. 1).)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI RAFAT OKASHA whose telephone number is (571)272-0675. The examiner can normally be reached M-F 10-6 EST.
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/RAMI R OKASHA/Primary Examiner, Art Unit 2118
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