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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment
In an October 12, 2023 amendment, claims 3-7 and 9-10 are amended, claim 12 is canceled and claims 1-11 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “acquisition part” in claims 1 and 10, “reliability determination part” in claims 1-3, “presentation device” in clams 1, 4, 8 and 11 and “reliability output part” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-5, 7-9 and 11 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Ahangar et al. in U.S. Patent Publication 2013/0331963 A1 (see IDS 2024 November 18).
Regarding claim 1, Ahangar et al. teaches:
A data processing system (Figs. 1 & 3) that outputs a diagnostic parameter used for deterioration diagnosis (Fig. 3) of at least one of a load (see “load profile”, [0017]-[0020]) or a servomotor in a servo system (see “servo motors”, [0039]; [0004], [0020], [0043]), the servo system including the load (Fig. 3), a servo amplifier (see “amplifiers”, [0046]), and the servomotor that applies power to the load in accordance with control of the servo amplifier, the diagnostic parameter including at least one of a control value to be used for controlling the servo system, a detection value output from a sensor that detects a state of the servo system, or a generation value generated from the control value or the detection value ([0005]; [0023]), the data processing system comprising:
an acquisition part that acquires the diagnostic parameter (see “operating parameters…can be based on actual monitored conditions. For example, a modified load profile can be identified based on operating condition information determined by monitoring equipment of the industrial automation device. The industrial automation device or associated VFD can include monitoring equipment, such as data loggers, sensors, sensor systems, or other monitoring equipment to identify operating conditions and environmental conditions of the industrial automation device while in actual use”, [0023]);
a reliability determination part that gives information regarding reliability of the diagnostic parameter to the diagnostic parameter (see “Reliability processing system 100 identifies (202) a modified load profile based on at least a base load profile and the at least one operating parameter for the industrial automation device. As discussed above, the at least one operating parameter is typically received as parameters 120 into graphical user interface 115. In some examples, further operating parameters can include default or predetermined values of operating parameters that are not modified by user input via parameters 120 but are still processed along with parameters 120 to determine a modified load profile”, [0017]; [0020], [0022]);
a presentation device (see Fig. 1, 115); and
a reliability output part that outputs, to the presentation device, the diagnostic parameter to which the information regarding the reliability (see Fig. 1, #121) is given by the reliability determination part (see “Reliability processing system 100 includes communication interface 111, processing system 112, memory 113, and user interface 114. In this example, user interface 114 presents graphical user interface 115. Graphical user interface 112 can receive user input for parameters 120 and present reliability information 121. In operation, processing system 112 is operatively linked to communication interface 111, memory 113, and user interface 114. Processing system 112 is capable of executing software stored in memory 113. When executing the software, processing system 112 drives reliability processing system 100 to operate as described herein”, [0015]; [0020]-[0022], [0024]).
Regarding claim 2, Ahangar et al. teaches comprising a receiver that receives a destination signal output from an operating device in accordance with an operation of a user on the operating device (see “graphical user interface”, [0036]; Figs. 4 & 7), wherein when the receiver receives the designation signal, the reliability determination part determines the reliability to be a value designated by the operation of the user on the operating device (see “Reliability processing system 100 generates (203) reliability information…”, [0020]). Regarding claim 4, Ahangar et al. teaches wherein the presentation device is a display device that displays a waveform and the reliability of the diagnostic parameter (see Figs. 3-4 & 7).
Regarding claim 5, Ahangar et al. teaches wherein the diagnostic parameter includes at least one of a value of a current supplied to the servomotor, a torque of the servomotor, a speed of the servomotor, and a value of a position of the servomotor (see “variable frequency alternating current (AC) power”, [0003]; see “a speed, torque,”, [0032]).
Regarding claim 7, Ahangar et al. teaches further comprising a diagnosis part that performs deterioration diagnosis of the servo system based on the diagnostic parameter (see “Known reliability relationships and equations are employed to modify the base load profile into the modified load profile, such as the Arrhenius equation, to alter the base load profile by the user-input operating conditions or other operating conditions. generates (203) reliability information for the industrial automation device based on the modified load profile…”, [0019]-[0020]).
Regarding claim 8, Ahangar et al. teaches further comprising a presentation output part that outputs a diagnosis result of the diagnosis part to the presentation device (see “Reliability processing system 100 presents (204) an indication of the reliability information via the graphical user interface.”, [0021]).
Regarding claim 9, Ahangar et al. teaches further comprising a receiver that receives a destination signal output from an operating device in accordance with an operation of a user on the operating device, wherein when the receiver receives the designation signal, the diagnosis part performs deterioration diagnosis of the servo system based on the diagnostic parameter designated by the operation of the user on the operating device (see “software drives reliability processing system 100 to receive user input for parameters 120, process parameters 120 along with load profile information, and present reliability information 121, among other operations…”, [0028]; [0033]-[0035]).
Regarding claim 11, Ahangar et al. teaches:
A data processing method (Figs. 1-2) for outputting a diagnostic parameter used for deterioration diagnosis (Fig. 3) of at least one of a load (see “load profile”, [0017]-[0020]) and a servomotor in a servo system (see “servo motors”, [0039]; [0004], [0020], [0043]), the servo system including the load (Fig. 3), a servo amplifier (see “amplifiers”, [0046]), and the servomotor that applies power to the load in accordance with control of the servo amplifier, the diagnostic parameter including at least one of a control value used for controlling the servo system, a detection value output from a sensor that detects a state of the servo system, and a generation value generated from the control value or the detection value ([0005]; [0023]), the data processing method comprising:
acquiring the diagnostic parameter (see “operating parameters…can be based on actual monitored conditions. For example, a modified load profile can be identified based on operating condition information determined by monitoring equipment of the industrial automation device. The industrial automation device or associated VFD can include monitoring equipment, such as data loggers, sensors, sensor systems, or other monitoring equipment to identify operating conditions and environmental conditions of the industrial automation device while in actual use”, [0023]);
giving information regarding reliability of the diagnostic parameter to the diagnostic parameter (Figs. 2-6); and
outputting the diagnostic parameter to which the information regarding the reliability is given to a presentation device (see “Reliability processing system 100 includes communication interface 111, processing system 112, memory 113, and user interface 114. In this example, user interface 114 presents graphical user interface 115. Graphical user interface 112 can receive user input for parameters 120 and present reliability information 121. In operation, processing system 112 is operatively linked to communication interface 111, memory 113, and user interface 114. Processing system 112 is capable of executing software stored in memory 113. When executing the software, processing system 112 drives reliability processing system 100 to operate as described herein”, [0015]; [0020]-[0022], [0024]).
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(s) 3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ahangar et al. in U.S. Patent Publication 2013/0331963 A1 (see IDS 2024 November 18) as applied to claim 1 above, and further in view of Kitamura et al. in U.S. Patent Publication 2018/275631 (see IDS 2023 November 10) .
Regarding claim 3, Ahangar et al. teaches the limitations as indicated above. Ahangar et al. differs from the claimed invention in that it does not expressly teach wherein the reliability determination part determines the reliability by using a trained model generated by machine learning.
Kitamura et al. teaches “a control system, a control device, and a control method that detect an abnormality occurring in a control object” ([0002]) and “FIG. 4A-4B are diagrams schematically illustrating a degree of deviation in the abnormality detection using the control system 1” ([0088]). Further, Kitamura et al. teaches the reliability determination part determines the reliability by using a trained model generated by machine learning (see “the group of feature quantities is clustered by the machine learning, the group of feature quantities is classified into a group of feature quantities a (feature quantities group a) of normal data (learning data)”, [0089]; [0099], [0101]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kitamura et al. with Ahangar et al. and Ahangar et al. with a reasonable expectation that it would facilitate efficiently determining the functional status of the system, thereby improving the reliability determination of the system.
Regarding claim 10, Ahangar et al. teaches the limitations as indicated above. Ahangar et al. differs from the claimed invention in that it does not expressly teach the acquisition part acquires, as input values, the control value or the detection value, the data processing system further includes a generator that generates the diagnostic parameter by performing at least extraction processing on the input values acquired by the acquisition part, the input values are values that vary with time, and the generator extracts, as the generation value, a value of a partial period among the input values.
Kitamura et al. teaches “a control system, a control device, and a control method that detect an abnormality occurring in a control object” ([0002]) and “FIG. 4A-4B are diagrams schematically illustrating a degree of deviation in the abnormality detection using the control system 1” ([0088]). Further, Kitamura et al. teaches the acquisition part acquires, as input values, the control value or the detection value, the data processing system further includes a generator that generates the diagnostic parameter by performing at least extraction processing on the input values acquired by the acquisition part, the input values are values that vary with time, and the generator extracts, as the generation value, a value of a partial period among the input values (see “the candidate list of abnormality detection parameters…creates a feature quantity on the basis of the abnormality detection parameters extracted by the support device…Specifically, the control device 100 creates a feature quantity on the basis of the types of the elements (such as an average value, a standard deviation, a degree of skewness, a degree of kurtosis, a maximum value, and a minimum value) and the number of elements (the number of dimensions of a feature vector) defined by the first parameter…executes the machine learning engine 140 and measures an execution time…acquires a degree of deviation using the machine learning engine 140 on the basis of the k value which is defined by the second parameter…all the abnormality detection parameters included in the candidate list are not used (NO in S208), the support device 200 extracts one set of abnormality detection parameters again (S204)”, [0204]-[0207]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kitamura et al. with Ahangar et al. and Ahangar et al. with a reasonable expectation that it would facilitate efficiently determining the functional status of the system, thereby improving the reliability determination of the system.
Allowable Subject Matter
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 6, neither Ahangar et al. nor Kitamura et al. do not expressly teach a generator that generates the generation value by performing at least one of processing of converting at least one of the control value and the detection value into a statistic and statistical analysis, in combination with all other limitations as claimed by Applicant.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Toshihiro et al. in U.S. Patent Publication 2024/0192673 teaches “Accuracy of deterioration diagnosis is improved. Data processing system (1) of servo system (7) includes acquisition part (2) and generator (3). Acquisition part (2) acquires, as input values, at least one of a control value and a detection value. Generator (3) generates a diagnostic parameter by performing at least extraction processing on the input value acquired by acquisition part (2). The diagnostic parameter is used for deterioration diagnosis of object (OB1) of at least one of load (73) and servomotor (72) in servo system (7). ” (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MISCHITA HENSON whose telephone number is (571)270-3944. The examiner can normally be reached Monday-Thursday 9am-6pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MI'SCHITA' HENSON/ Primary Examiner, Art Unit 2857