DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to the claimed amendment filed on January 15, 2025, in which claims 1-9 are presented for examination.
Information Disclosure Statement
The information disclosure statement filed on January 15, 2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
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.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
At Step 1:
With respect to subject matter eligibility under 35 USC 101, it is determined that the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
At Step 2A, Prong One:
The limitation “a coordinate data acquisition unit that externally acquires coordinate data representing operation coordinates of the drive device” in claim 1, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is, other than reciting “coordinate data acquisition unit”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the limitation “acquires”, in the context of these claims encompasses one can mentally, or manually with the aid of pen and paper externally acquires coordinate data representing operation coordinates of a drive device.
The limitation “a parameter generation unit that generates an operation parameter used to control the drive device by the controller” in claim 1, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is, other than reciting “parameter generation unit”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language “generates”, in the context of the claim encompasses one can manually with the aid of pen and paper generates an operation parameter to control the drive device.
The limitation “a state data acquisition unit that acquires state data representing a state of the drive device while the drive device operates in accordance with the operation parameter” in claim 1, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is, other than reciting “state data acquisition unit”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language “acquires”, in the context of the claim encompasses one can manually with the aid of pen and paper acquires state data representing a state of the drive device.
The limitation “an index data calculation unit that calculates index data based on the state data and serving as an index for determining whether the operation parameter is appropriate” in claim 1, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical process. For example, the language “calculates”, in the context of the claim encompasses one can calculates index data based on a mathematical algorithm.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea.
At Step 2A, Prong Two:
This judicial exception is not integrated into a practical application. The claim recites the following additional elements:
That the method is "implemented by a computing system" is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
“a parameter search unit that uses the sample data to search for an operation parameter estimated to be appropriate based on the index data” amount to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
“a parameter storage unit that stores the operation parameter and outputs to the controller an operate command including the operation parameter and a sample storage unit that stores sample data in which the operation parameter is associated with the index data” recites insignificant extra-solution activity such as mere outputting of the result and does not meaningfully limit the abstract idea. (See MPEP 2106.05 (g)).
“a model training unit that uses training data in which an operation parameter estimated by the parameter search unit to be appropriate is associated with the coordinate data to generate a trained model for estimating from the coordinate data an appropriate parameter which is an operation parameter suitable for performing an operation for the coordinate data” the above identified mental processes and the "acquires" above being performed "using the a model training unit" is at best generally linking the abstract idea to the particular field of use or technological environment of machine learning (see MPEP 2106.05(h), and/or akin to using machine learning as a mere tool (2106.05(f)).
At Step 2B:
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (model training) as a tool are carried over and do not provide significantly more.
With respect to the “uses training data….., and stores the operation parameter and outputs ……” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), " iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93" and "i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
With respect to “a model training unit that uses training data…….” identified as insignificant extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011) (citations omitted) ("[N]othing in claim 3 requires an infringer to use the Internet to obtain that data. The Internet is merely described as the source of the data. We have held that mere ‘[data-gathering] step[s] cannot make an otherwise nonstatutory claim statutory.’" 654 F.3d at 1375, 99 USPQ2d at 1694
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible. Accordingly, claim 1 is directed to an abstract idea.
The dependent claims 2-9 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 2 recites “a model storage unit that stores the trained model generated by the model training unit; and a parameter output unit that outputs the appropriate parameter by inputting the coordinate data acquired by the coordinate data acquisition unit to the trained model stored in the model storage unit”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing the trained model, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 3 recites “wherein the parameter search unit instructs the controller to control the drive device with the operation parameter estimated by the parameter search unit to be appropriate, the state data acquisition unit acquires search state data representing a state of the drive device when the drive device is operated with the operation parameter estimated by the parameter search unit to be appropriate, the index data calculation unit calculates the index data based on the search state data, the sample storage unit stores, as the training data, data in which an operation parameter determined to be appropriate based on the search state data is associated with the coordinate data, and the model training unit uses the training data stored in the sample storage unit to generate the trained model”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 4 recites “wherein the state data acquisition unit acquires search state data representing a state of the drive device when the drive device is operated with the operation parameter estimated by the parameter search unit to be appropriate, the index data calculation unit calculates two or more types of indices based on the search state data and sets a combination of the calculated two or more types of indices as the index data, and the parameter search unit uses a multi-objective optimization method to estimate an operation parameter determined to be appropriate based on each index constituting the index data”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 5 recites “wherein the parameter generation unit generates a first predetermined number of operation parameters set depending on a parameter search method of the parameter search unit”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 6 recites “wherein when a second predetermined number of training data set depending on a model training method of the model training unit is stored in the sample storage unit, the model training unit uses the second predetermined number of training data to generate the trained model”. This additional element is at best generally linking the abstract idea to the particular field of use or technological environment of machine learning (see MPEP 2106.05(h), and/or akin to using machine learning as a mere tool (2106.05(f)).
Claim 7 recites “a searching model construction unit that uses the sample data stored in the sample storage unit to generate a searching model for estimating the index data from the operation parameter; a searching model storage unit that stores the searching model; and a parameter estimation unit that searches for an operation parameter estimated to be appropriate based on the index data estimated through the searching model”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 8 recites “wherein the state data acquisition unit acquires torque data from the drive device, and the index data calculation unit sets a linear combination of a value in amplitude of vibration of torque calculated from the torque data and an attenuation rate in waveform of the torque as an index indicating residual vibration after the drive device is positioned”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 9 recites “wherein the model training unit generates a classification model that divides training data stored in the sample storage unit into a plurality of groups, and the model training unit generates a plurality of regression models that are respectively provided for the plurality of groups”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7 and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kawasaki JP 2021-107970 A (29 July 2021 (2021-07-29).
As to claim 1, Kawasaki discloses an information processing device for a drive device controlled by a controller ( see, page 4, par. [9], The learning model constructed by machine learning the training data in the learning device 3 is mounted on the robot control device 10 of the robot system 1, and is used for autonomous driving of the robot 11, wherein the learning model implemented in the robot control device 10 operates in the inference phase, estimates and outputs the operating force of the operator corresponding to the input surrounding environment data with respect to the input surrounding environment data), comprising:
a coordinate data acquisition unit that externally acquires coordinate data representing operation coordinates of the drive device (see page 3, par. [9], operating force detected by the operating force detection sensor 12a includes, for example, as shown in FIG. 3, the force and velocity components (force x and velocity x) on the x-axis in the coordinate system of the robot 11. Includes force and velocity components (force y and velocity y) on the y-axis, wherein the data related to the operating force detected by the operating force detection sensor 12a is collected by the data collecting device 13 as operating information);
a parameter generation unit that generates an operation parameter used to control the drive device by the controller (see page 3, par. [11]-page 4, par. [1], the collected data collected by the data collecting device 13 includes state information indicating the surrounding environment data of the robot 11, operation information reflecting the operating force of the operator corresponding to the surrounding environment data of the robot 11, and operation information. including. In other words, this collected data is a series of state information and operation information obtained when the operator continuously operates the operation device 12 to perform the work (or a part of the work) on the robot 11);
a parameter storage unit that stores the operation parameter and outputs to the
controller an operate command including the operation parameter (see page 10, par. [4], data evaluation unit 21 evaluates the input collected data using the data evaluation model 20, wherein the storage unit 22 stores the evaluated data, which is the collected data evaluated by the data evaluation unit 21. The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21);
a state data acquisition unit that acquires state data representing a state of the drive device while the drive device operates in accordance with the operation parameter (see page 9, par. [4], the robot 11 to perform a series of operations including the operation, wherein the state information and operation information at this time are acquired as collected data by the data collecting device 13. In the following, a case where the new operation part of the collected data corresponds to the data block between the two blocks to which the label (1) is attached in the operation information);
an index data calculation unit that calculates index data based on the state data and serving as an index for determining whether the operation parameter is appropriate (see page 6, par. [2], state information is sensor information, which is working state such as position, speed, force, moment, image detected by the motion sensor 11a, the force sensor 11b, and the camera 11c. This state information may include information calculated based on the sensor information)
a sample storage unit that stores sample data in which the operation parameter is associated with the index data (see page 10, par. [4], the storage unit 22 stores the evaluated data, which is the collected data evaluated by the data evaluation unit 21. The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21);
a parameter search unit that uses the sample data to search for an operation parameter estimated to be appropriate based on the index data (see, page 6, par. [4], when the collected data including the state information and the operation information associated with the time series information is input, the data evaluation model 20 of the present embodiment estimates and outputs the reference operation corresponding to the input state information, wherein the distance value between the input operation information and the estimated estimation reference operation is obtained, and the distance value (similarity) is output as an evaluation value, instead of the estimation reference operation, for example, information on the cluster to which the reference operation belongs may be output, wherein the comparison between the output estimation reference operation and the input operation information may be performed by the data evaluation unit 21 instead of the data evaluation model); and
a model training unit that uses training data in which an operation parameter estimated by the parameter search unit to be appropriate is associated with the coordinate data to generate a trained model for estimating from the coordinate data an appropriate parameter which is an operation parameter suitable for performing an operation for the coordinate data (see page 9, par. [8], the training data selection unit 25 is used to select training data for constructing a learning model used in the robot system 1 from the selected data stored in the storage unit 22. Training data is sorted in various ways according to the purpose, when it is desired to train the training model for a series of operations shown in FIG. 2, the training data selection unit 25 uses labels (1) to (4) of numerical values (1) to (4) from the selected data instructed to be adopted as training data. Selects the assigned data and outputs it as training data and when it is desired to additionally learn the training model regarding the reference operation for the work state C, the training data selection unit 25 assigns the label of the numerical value (3) from the selected data instructed to be adopted as the training data) and page 9, par.[3], the training data sorting device 2 presents a large amount of collected data to the operator so that it can be selected with mechanical evaluation information (label) added, as a result, the operator can efficiently select appropriate data and use it as training data for machine learning.)
As to claim 2, Kawasaki discloses “a model storage unit that stores the trained model generated by the model training unit; and a parameter output unit that outputs the appropriate parameter by inputting the coordinate data acquired by the coordinate data acquisition unit to the trained model stored in the model storage unit” (see page 9, par.[3], the training data sorting device 2 presents a large amount of collected data to the operator so that it can be selected with mechanical evaluation information (label) added, as a result, the operator can efficiently select appropriate data and use it as training data for machine learning; page 10, par. [4], the storage unit 22 stores the evaluated data, which is the collected data evaluated by the data evaluation unit 21. The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21).
As to claim 3, Kawasaki discloses “wherein the parameter search unit instructs the controller to control the drive device with the operation parameter estimated by the parameter search unit to be appropriate, the state data acquisition unit acquires search state data representing a state of the drive device when the drive device is operated with the operation parameter estimated by the parameter search unit to be appropriate (see, page 6, par. [4], when the collected data including the state information and the operation information associated with the time series information is input, the data evaluation model estimates and outputs the reference operation corresponding to the input state information, wherein the distance value between the input operation information and the estimated of the estimation reference operation reference operation is obtained, and the distance value is output as an evaluation value, instead of the estimation reference operation, for example, information on the cluster to which the reference operation belongs may be output, wherein the comparison between the output estimation reference operation and the input operation information may be performed by the data evaluation unit 21 instead of the data evaluation model; and page 9, par.[3], the training data sorting device 2 presents a large amount of collected data to the operator so that it can be selected with mechanical evaluation information (label) added, as a result, the operator can efficiently select appropriate data and use it as training data for machine learning),
the index data calculation unit calculates the index data based on the search state data, the sample storage unit stores, as the training data, data in which an operation parameter determined to be appropriate based on the search state data is associated with the coordinate data, and the model training unit uses the training data stored in the sample storage unit to generate the trained model (page 9, par. [3], the training data sorting device 2 of the present embodiment may present a large amount of collected data to the operator so that it can be selected with mechanical evaluation information (label) added and as a result, the operator can efficiently select appropriate data and use it as training data for machine learning).
As to claim 4, Kawasaki discloses “wherein the state data acquisition unit acquires search state data representing a state of the drive device when the drive device is operated with the operation parameter estimated by the parameter search unit to be appropriate, the index data calculation unit calculates two or more types of indices based on the search state data and sets a combination of the calculated two or more types of indices as the index data (see page 9, par.[3], the training data sorting device 2 presents a large amount of collected data to the operator so that it can be selected with mechanical evaluation information (label) added, as a result, the operator can efficiently select appropriate data and use it as training data for machine learning), and
the parameter search unit uses a multi-objective optimization method to estimate an operation parameter determined to be appropriate based on each index constituting the index data (see page 10, par. [7], the collected data is evaluated for each appropriate unit, so that it becomes easy to grasp a series of operations as if the basic operations are arranged in an appropriate order by using this evaluation result, the selection of training data becomes more accurate and by using the part corresponding to the basic operation as the unit for selecting the training data, machine learning can be performed while efficiently using the collected data).
As to claim 5, Kawasaki discloses “wherein the parameter generation unit generates a first predetermined number of operation parameters set depending on a parameter search method of the parameter search unit (see page 5, par. [3], collected data machine-learned by the data evaluation model 20 is classified into a plurality of groups by using, for example, a known clustering method such as an NN method, a K-means method, or a self-organizing map. Clustering is a method of learning the law of distribution from a large number of data and automatically acquiring a plurality of clusters, which are a group of data having similar characteristics to each other. The number of clusters to which the collected data is classified can be determined as appropriate. The collected data may be classified by using an automatic classification method other than clustering.)
As to claim 6, Kawasaki discloses “wherein when a second predetermined number of training data set depending on a model training method of the model training unit is stored in the sample storage unit, the model training unit uses the second predetermined number of training data to generate the trained model” (see page 9, par. [8], the training data selection unit 25 is used to select training data for constructing a learning model used in the robot system 1 from the selected data stored in the storage unit 22, wherein training data is sorted in various ways, when it is desired to train the training model for a series of operations, the training data selection unit 25 uses labels (1) to (4) of numerical values (1) to (4) from the selected data instructed to be adopted as training data. Selects the assigned data and outputs it as training data, when it is desired to additionally learn the training model regarding the reference operation for the work state C, the training data selection unit 25 assigns the label of the numerical value (3) from the
selected data instructed to be adopted as the training data. The block of the obtained data part is extracted and output as training data).
As to claim 7, Kawasaki discloses “wherein the parameter search unit includes: a searching model construction unit that uses the sample data stored in the sample storage unit to generate a searching model for estimating the index data from the operation parameter;
a searching model storage unit that stores the searching model (see page 10, par. [4], the storage unit 22 stores the evaluated data, which is the collected data evaluated by the data evaluation unit 21. The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21); and
a parameter estimation unit that searches for an operation parameter estimated to be appropriate based on the index data estimated through the searching model (see page 10, par. [8], the collected data is evaluated for each appropriate unit, so that it becomes easy to grasp a series of operations as if the basic operations are arranged in an appropriate order, by using this evaluation result, the selection of training data becomes more accurate, and by using the part corresponding to the basic operation as the unit for selecting the training data, machine learning can be performed while efficiently using the collected data).
As to claim 9, Kawasaki discloses “wherein the model training unit generates a classification model that divides training data stored in the sample storage unit into a plurality of groups, and the model training unit generates a plurality of regression models that are respectively provided for the plurality of groups” (see page 12, par. [6], data evaluation model of the training data sorting device 2 evaluates the collected data, wherein the data evaluation model 20 may be used to evaluate the output of the learning model constructed by machine learning the training data selected by the training data selection device 2).
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.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kawasaki JP 2021-107970 A (29 July 2021 (2021-07-29) in view of Sakiyama et al., (hereinafter “Sakiyama”) US 20200278646
As to claim 8, Kawasaki discloses the invention as claimed. However, Kawasaki fails to disclose the claimed “wherein the state data acquisition unit acquires torque data from the drive device, and the index data calculation unit sets a linear combination of a value in amplitude of vibration of torque calculated from the torque data and an attenuation rate in waveform of the torque as an index indicating residual vibration after the drive device is positioned”.
Meanwhile, Sakiyama discloses the claimed “wherein the state data acquisition unit acquires torque data from the drive device, and the index data calculation unit sets a linear combination of a value in amplitude of vibration of torque calculated from the torque data and an attenuation rate in waveform of the torque as an index indicating residual vibration after the drive device is positioned” (see par. [0054], data acquisition unit 415 repeatedly acquires the speed data and the torque data and saves the data in the data storage 416 in association with the acquisition time. For example, the data acquisition unit 415 reads the speed command value by the interface program of the interface storage 413, and acquires this as the speed data, wherein the data acquisition unit 415 reads the torque command value by the interface program of the interface storage 413, and acquires this as torque data, by using the interface program to acquire the speed data and torque data, unnecessary access to another storage area of the control data storage, the operation program storage can be prevented and the data acquisition unit 415 may acquire the current speed as speed data and may acquire the drive current as torque data).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kawasaki to acquire torque data from the drive device, in order to balance loads across multiple drives in a system, thereby allowing for real-time monitoring and data analysis, which in turn prevents catastrophic failures, reduces downtime, and informs essential maintenance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20120259824 (involved in maintaining index data in database of computing device
US 20180075074 (training data generation apparatus comprises a data entry unit that inputs the bilingual data corresponding to original text data, where machine-translation unit performs a machine-translation process corresponding to the original text data, and thus enables to provide efficient data for training and acquires a high-precision tag series).
See PTO-892
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 December 18, 2025