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 papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The references listed in the Information Disclosure Statements filed on 09/15/2023 and 09/06/2024 have been considered by the examiner (see attached PTO-1449 forms).
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 1-8 and 13-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a load estimating device for a rolling bearing, comprising:
a vibration sensor configured to measure vibration of the rolling bearing during rotation;
a rotational speed sensor configured to measure a rotational speed of the rolling bearing during rotation;
a derivation portion configured to derive a vibration value of a predetermined vibration frequency using vibration information measured by the vibration sensor; and
an estimation portion configured to estimate a load applied to the rolling bearing,
the load corresponding to the rotational speed measured by the rotational speed sensor and the vibration value derived by the derivation portion,
using a table in which a correspondence relationship among the load applied to the rolling bearing, the vibration value of the predetermined vibration frequency, and the rotational speed is defined.
Claim 3 recites a load estimating device for a rolling bearing, comprising:
a vibration sensor configured to measure vibration of the rolling bearing during rotation;
a rotational speed sensor configured to measure a rotational speed of the rolling bearing during rotation;
a derivation portion configured to derive a vibration value of a predetermined vibration frequency using vibration information measured by the vibration sensor; and
an estimation portion configured to estimate a load applied to the rolling bearing, the load corresponding to the vibration value of the predetermined vibration frequency derived by the derivation portion and the rotational speed measured by the rotational speed sensor,
using a learned model obtained by performing learning processing using data including a pair of the load applied to the rolling bearing as well as the vibration value of the predetermined vibration frequency and the rotational speed of the rolling bearing as learning data and using the load applied to the rolling bearing as output data.
Claim 13 recites a load estimating method for a rolling bearing, comprising:
a first measurement step of measuring vibration of the rolling bearing during rotation;
a second measurement step of measuring a rotational speed of the rolling bearing during rotation;
a derivation step of deriving a vibration value of a predetermined vibration frequency using vibration information measured in the first measurement step; and
an estimation step of estimating a load applied to the rolling bearing, the load corresponding to the rotational speed measured in the second measurement step and the vibration value derived in the derivation step,
using a table in which a correspondence relationship among the load applied to the rolling bearing, the vibration value of the predetermined vibration frequency, and the rotational speed is defined.
Claim 14 recites a load estimating method for a rolling bearing, comprising:
a first measurement step of measuring vibration of the rolling bearing during rotation;
a second measurement step of measuring a rotational speed of the rolling bearing during rotation;
a derivation step of deriving a vibration value of a predetermined vibration frequency using vibration information measured in the first measurement step; and
an estimation step of estimating a load applied to the rolling bearing, the load corresponding to the vibration value of the predetermined vibration frequency derived in the derivation step and the rotational speed measured in the second measurement step,
using a learned model obtained by performing learning processing using data including a pair of the load applied to the rolling bearing as well as the vibration value of the predetermined vibration frequency and the rotational speed of the rolling bearing as learning data and using the load applied to the rolling bearing as output data.
Claim 15 recites a non-transitory computer-readable storage medium storing a computer program configured to cause a computer to function as:
a first acquisition portion configured to acquire vibration information of a rolling bearing during rotation;
a second acquisition portion configured to acquire a rotational speed of the rolling bearing during rotation;
a derivation portion configured to derive a vibration value of a predetermined vibration frequency using the vibration information; and
an estimation portion configured to estimate a load applied to the rolling bearing, the load corresponding to the rotational speed acquired by the second acquisition portion and the vibration value derived by the derivation portion,
using a table in which a correspondence relationship among the load applied to the rolling bearing, the vibration value of the predetermined vibration frequency, and the rotational speed is defined.
Claim 16 recites a non-transitory computer-readable storage medium storing a computer program configured to cause a computer to function as:
a first acquisition portion configured to acquire vibration information of a rolling bearing during rotation;
a second acquisition portion configured to acquire information on a rotational speed of the rolling bearing during rotation;
a derivation portion configured to derive a vibration value of a predetermined vibration frequency using the vibration information; and
an estimation portion configured to estimate a load applied to the rolling bearing, the load corresponding to the vibration value of the predetermined vibration frequency derived by the derivation portion and the rotational speed acquired by the second acquisition portion,
using a learned model obtained by performing learning processing using data including a pair of the load applied to the rolling bearing as well as the vibration value of the predetermined vibration frequency and the rotational speed of the rolling bearing as learning data and using the load applied to the rolling bearing as output data.
and thus grouped as Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations.
These judicial exceptions are not integrated into a practical application because the additional elements, the data gathering step, (claims 1 and 3) “a vibration sensor configured to measure vibration of the rolling bearing during rotation; a rotational speed sensor configured to measure a rotational speed of the rolling bearing during rotation” (claims 13 and 14) “a first measurement step of measuring vibration of the rolling bearing during rotation; a second measurement step of measuring a rotational speed of the rolling bearing during rotation” (claims 15 and 16) “a first acquisition portion configured to acquire vibration information of a rolling bearing during rotation; a second acquisition portion configured to acquire a rotational speed of the rolling bearing during rotation” are mere data gathering that do not add a meaningful limitation to the method as they are insignificant extra-solution activity. Furthermore, the additional elements (claims 1, 3, 13-16 and 10) the “derivation portion, estimation portion and computer program configured to cause a computer” are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions amount to no more than using a computer as a tool to perform an abstract idea. All of which are considered not indicative of integration into a practical application (see “Federal Register / Vol. 84, No. 4/ Monday, January 7, 2019 / Notices” – page 55, second column).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of the data gathering steps are mere data collect steps which fall under insignificant extra solution activity and deemed insufficient to qualify as “significantly more” - see MPEP 2106.05(g). The additional elements of the “derivation portion, estimation portion and computer program configured to cause a computer” are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and deemed insufficient to qualify as “significantly more” see MPEP 2106.05(f).
Dependent claims 2 and 4-8 when analyzed as a whole are patent ineligible under 35 U.S.C. §101 because the dependent claims fail to establish that the claims are not directed to an abstract idea as they are directed mathematical concepts and/or mental processes and do not add significantly more to the abstract idea.
To note: Claims 9-12 are patent eligible with regards to the Patent Subject Matter Eligibility Guidance. The claims, taken as a whole amount to a practical application of the judicial exception see MPEP 2106.04(d) (a claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception).
Claim Rejections - 35 USC § 102
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 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 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.
Claims 1-4, 6-8 and 13-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by IWANAGA [US 2022/0213872 A1].
Regarding claim 1, IWANAGA teaches a load estimating device for a rolling bearing (main shaft bearing 60 – 0036-0038), comprising:
a vibration sensor configured to measure vibration of the rolling bearing during rotation (monitors vibrations – 0039) (sensor 70A is a vibration sensor that senses vibration of main shaft bearing 60 – 0042);
a rotational speed sensor configured to measure a rotational speed of the rolling bearing during rotation ( load data is a rotational speed (in rpm) of main shaft 20 – 0050) (sensors 70A-70H and measurement device – 0039, 0041, 0042);
a derivation portion configured to derive a vibration value of a predetermined vibration frequency using vibration information measured by the vibration sensor (calculates an effective value of vibration (hereinafter referred to as a “degree of vibration”) – 0042); and
an estimation portion configured to estimate a load applied to the rolling bearing, the load corresponding to the rotational speed measured by the rotational speed sensor and the vibration value derived by the derivation portion (generate a data set of the load data, the cumulative load data, and the measurement data - 0046),
using a table in which a correspondence relationship among the load applied to the rolling bearing, the vibration value of the predetermined vibration frequency, and the rotational speed is defined (interpolated three-dimensional data – 0047) (figures 5-8 – 0058, 0061, 0064) (figure 10, 0070-0071).
Regarding claim 2, IWANAGA teaches the table is defined such that the load applied to the rolling bearing increases as the vibration value of the predetermined vibration frequency increases (figures 5-8 – 0058, 0061, 0064) (figure 10, 0070-0071).
Regarding claim 3, IWANAGA teaches a load estimating device for a rolling bearing (main shaft bearing 60 – 0036-0038), comprising:
a vibration sensor configured to measure vibration of the rolling bearing during rotation (monitors vibrations – 0039) (sensor 70A is a vibration sensor that senses vibration of main shaft bearing 60 – 0042);
a rotational speed sensor configured to measure a rotational speed of the rolling bearing during rotation ( load data is a rotational speed (in rpm) of main shaft 20 – 0050) (sensors 70A-70H and measurement device – 0039, 0041, 0042);
a derivation portion configured to derive a vibration value of a predetermined vibration frequency using vibration information measured by the vibration sensor (calculates an effective value of vibration (hereinafter referred to as a “degree of vibration”) – 0042); and
an estimation portion configured to estimate a load applied to the rolling bearing, the load corresponding to the vibration value of the predetermined vibration frequency derived by the derivation portion and the rotational speed measured by the rotational speed sensor (generate a data set of the load data, the cumulative load data, and the measurement data - 0046),
using a learned model (profile representing a relationship between the rotational speed and the degree of vibration) obtained by performing learning processing using data including a pair of the load applied to the rolling bearing as well as the vibration value of the predetermined vibration frequency and the rotational speed of the rolling bearing as learning data and using the load applied to the rolling bearing as output data (interpolated three-dimensional data – 0047) (figures 5-8 – 0058, 0061, 0064) (figure 10, 0070-0071).
Regarding claim 4, IWANAGA teaches as the predetermined vibration frequency, one or more of a theoretical frequency of the rolling bearing and a high-order vibration frequency thereof are used (figures 5-8 – 0058, 0061, 0064) (frequency for cumulative number - figure 10, 0070-0072, figure 12 - 0085, 0086).
Regarding claim 6, IWANAGA teaches the theoretical frequency is based on Zfc, Zfi, or 2fb (frequency for - 0086-0087).
Regarding claim 7, IWANAGA teaches the vibration value of the predetermined vibration frequency is an acceleration, a speed, or a displacement (figures 5 and 6 – 0058, 0059, 0061).
Regarding claim 8, IWANAGA teaches the rolling bearing is a rolling bearing configured to support a main shaft of a wind turbine generator (wind power generation facility – 0036, 0037).
Regarding claim 13, IWANAGA teaches a load estimating method for a rolling bearing (main shaft bearing 60 – 0036-0038), comprising:
a first measurement step of measuring vibration of the rolling bearing during rotation (monitors vibrations – 0039) (sensor 70A is a vibration sensor that senses vibration of main shaft bearing 60 – 0042);
a second measurement step of measuring a rotational speed of the rolling bearing during rotation ( load data is a rotational speed (in rpm) of main shaft 20 – 0050) (sensors 70A-70H and measurement device – 0039, 0041, 0042);
a derivation step of deriving a vibration value of a predetermined vibration frequency using vibration information measured in the first measurement step (calculates an effective value of vibration (hereinafter referred to as a “degree of vibration”) – 0042); and an estimation step of estimating a load applied to the rolling bearing, the load corresponding to the rotational speed measured in the second measurement step and the vibration value derived in the derivation step (generate a data set of the load data, the cumulative load data, and the measurement data - 0046),
using a table in which a correspondence relationship among the load applied to the rolling bearing, the vibration value of the predetermined vibration frequency, and the rotational speed is defined (interpolated three-dimensional data – 0047) (figures 5-8 – 0058, 0061, 0064) (figure 10, 0070-0071).
Regarding claim 14, IWANAGA teaches a load estimating method for a rolling bearing (main shaft bearing 60 – 0036-0038), comprising: a first measurement step of measuring vibration of the rolling bearing during rotation (monitors vibrations – 0039) (sensor 70A is a vibration sensor that senses vibration of main shaft bearing 60 – 0042); a second measurement step of measuring a rotational speed of the rolling bearing during rotation ( load data is a rotational speed (in rpm) of main shaft 20 – 0050) (sensors 70A-70H and measurement device – 0039, 0041, 0042); a derivation step of deriving a vibration value of a predetermined vibration frequency using vibration information measured in the first measurement step (calculates an effective value of vibration (hereinafter referred to as a “degree of vibration”) – 0042); and an estimation step of estimating a load applied to the rolling bearing, the load corresponding to the vibration value of the predetermined vibration frequency derived in the derivation step and the rotational speed measured in the second measurement step (generate a data set of the load data, the cumulative load data, and the measurement data - 0046),
using a learned model (profile representing a relationship between the rotational speed and the degree of vibration) obtained by performing learning processing using data including a pair of the load applied to the rolling bearing as well as the vibration value of the predetermined vibration frequency and the rotational speed of the rolling bearing as learning data and using the load applied to the rolling bearing as output data (interpolated three-dimensional data – 0047) (figures 5-8 – 0058, 0061, 0064) (figure 10, 0070-0071)
Regarding claim 15, IWANAGA teaches a non-transitory computer-readable storage medium storing a computer program configured to cause a computer to function as: a first acquisition portion configured to acquire vibration information of a rolling bearing during rotation (monitors vibrations – 0039) (sensor 70A is a vibration sensor that senses vibration of main shaft bearing 60 – 0042); a second acquisition portion configured to acquire a rotational speed of the rolling bearing during rotation ( load data is a rotational speed (in rpm) of main shaft 20 – 0050) (sensors 70A-70H and measurement device – 0039, 0041, 0042); a derivation portion configured to derive a vibration value of a predetermined vibration frequency using the vibration information (calculates an effective value of vibration (hereinafter referred to as a “degree of vibration”) – 0042); and an estimation portion configured to estimate a load applied to the rolling bearing, the load corresponding to the rotational speed acquired by the second acquisition portion and the vibration value derived by the derivation portion (generate a data set of the load data, the cumulative load data, and the measurement data - 0046),
using a table in which a correspondence relationship among the load applied to the rolling bearing, the vibration value of the predetermined vibration frequency, and the rotational speed is defined (interpolated three-dimensional data – 0047) (figures 5-8 – 0058, 0061, 0064) (figure 10, 0070-0071).
Regarding claim 16, IWANAGA teaches a non-transitory computer-readable storage medium storing a computer program configured to cause a computer to function as: a first acquisition portion configured to acquire vibration information of a rolling bearing during rotation (monitors vibrations – 0039) (sensor 70A is a vibration sensor that senses vibration of main shaft bearing 60 – 0042); a second acquisition portion configured to acquire information on a rotational speed of the rolling bearing during rotation ( load data is a rotational speed (in rpm) of main shaft 20 – 0050) (sensors 70A-70H and measurement device – 0039, 0041, 0042); a derivation portion configured to derive a vibration value of a predetermined vibration frequency using the vibration information (calculates an effective value of vibration (hereinafter referred to as a “degree of vibration”) – 0042); and an estimation portion configured to estimate a load applied to the rolling bearing, the load corresponding to the vibration value of the predetermined vibration frequency derived by the derivation portion and the rotational speed acquired by the second acquisition portion (generate a data set of the load data, the cumulative load data, and the measurement data - 0046),
using a learned model (profile representing a relationship between the rotational speed and the degree of vibration) obtained by performing learning processing using data including a pair of the load applied to the rolling bearing as well as the vibration value of the predetermined vibration frequency and the rotational speed of the rolling bearing as learning data and using the load applied to the rolling bearing as output data (interpolated three-dimensional data – 0047) (figures 5-8 – 0058, 0061, 0064) (figure 10, 0070-0071).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 9, 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over IWANAGA as applied to claim 1 above, and further in view of Frydendal [US 2018/0187651 A1].
Regarding claim 9, while IWANAGA further teaches a control device for a mechanical device (wind power generation facility) including a rolling bearing (shaft bearing - 0038), the control device (figure 2 – 0036) comprising:
the load estimating device according to claim 1 (see claim 1 rejection), IWANAGA does not specifically disclose a controller configured to control at least one of torque around a shaft supported by the rolling bearing.
However, Frydendal teaches a controller configured to control at least one of torque around a shaft supported by the rolling bearing and rotation of the rolling bearing in accordance with load estimated (load signal) by the estimation portion (A wind turbine control system 8 in the form of a local controller is arranged in the wind turbine 1 and is configured to control the operation of the wind turbine 1 according to different operating modes – 0115) (determine the control action – 0125).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the teachings of IWANAGA to further include the control system capabilities as taught by Frydendal to allow for wind turbines to be operated in a safe mode that reduces the loads in severe load situations (Frydendal - 0006).
Regarding claim 10, IWANAGA in combination with Frydendal teaches the control device is configured to control a rotational frequency or a rotational speed of the rolling bearing (control algorithm allows 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or any other values of said predetermined power output, rotational speed – Frydendal - 0030).
Regarding claim 12, IWANAGA in combination with Frydendal teaches a storage portion configured to store information on a control history by the controller, wherein the controller is configured to set the torque around the shaft supported by the rolling bearing and a control amount of the rotation of the rolling bearing based on the control history (The data measured by the various sensors, the calculated/normalized damage rate, and/or the control signals may be transmitted to this remote controller for further analysis or storage– Frydendal - 0080).
Allowable Subject Matter
Claim 11 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.
Claim 5 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Relevant Prior Art / Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamashita et al. (US Patent Number 10,697,854 B2) discloses a rolling nearing fatigue state prediction device for predicting a fatigue sate of a rolling bearing;
Sakaguchi et al. (US Patent Number 10,519,935 B2) discloses a condition monitoring system for a wind turbine for diagnosing equipment failure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICKY GO whose telephone number is (571)270-3340. The examiner can normally be reached on Monday through Friday from 9:00 a.m. to 5:30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M. Vazquez can be reached on (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RICKY GO/Primary Examiner, Art Unit 2857