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 Statement filed on 02/28/2024 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 1-15 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 computer including one or more processors and one or more memory resources, wherein the one or more processors are configured to execute a step of acquiring time series data, and a step of determining, as an optimum value of an order of an autoregressive model for the time series data, an integer m - 1 in which, in an autoregressive model of an order m - 1, a peak of an autoregressive spectrum of the time series data is unimodal, and in an autoregressive model of an order m, a peak of the autoregressive spectrum of the time series data is bimodal…
Claim 7 recites a diagnosis system including a sensor and a computer, wherein the computer is configured to execute a step of acquiring time series data indicating a temporal change in a measurement result of the sensor; and a step of determining, as an optimum value of an order of an autoregressive model for the time series data, an integer m - 1 in which, in an autoregressive model of an order m - 1, a peak of an autoregressive spectrum of the time series data is unimodal, and in an autoregressive model of an order m, a peak of the autoregressive spectrum of the time series data is bimodal…
Claim 13 recites a diagnosis method executed by a computer including one or more processors and one or more memory resources, the diagnosis method comprising: a step of acquiring time series data; and a step of determining, as an optimum value of an order of an autoregressive model for the time series data, an integer m - 1 in which, in an autoregressive model of an order m - 1, a peak of an autoregressive spectrum of the time series data is unimodal, and in an autoregressive model of an order m, a peak of the autoregressive spectrum of the time series data is bimodal…
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, (claim 1) “step of acquiring time series data” (claim 7) “step of acquiring time series data indicating a temporal change in a measurement result of the sensor” (claim 13) “a step of acquiring time series data” 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, 7 and 13) the “computer and processors” 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 MPEP 2106.04(d)).
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 are considered extra-solution activity of pre-solution and post-solution activity 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 computer and processors 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 2-6, 8-12 and 14-15 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.
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 1-5, 7-11 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gersztenkorn [US Patent Number 6,594,585 B1].
Regarding claim 1, Gersztenkorn teaches a computer including one or more processors and one or more memory resources, wherein the one or more processors are configured to execute (general purpose programmable computer - C5L38-46)
a step of acquiring time series data (a time series 310 together with various Fourier transform amplitude spectra computed therefrom - C7L51-67), and
a step of determining, as an optimum value (coefficient of determination) of an order of an autoregressive model for the time series data, an integer m - 1 in which, in an autoregressive model of an order m – 1 (regression analysis … well fit by the regression model… - C12L18-56), a peak of an autoregressive spectrum of the time series data is unimodal, and in an autoregressive model of an order m, a peak of the autoregressive spectrum of the time series data is bimodal (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16).
Regarding claim 2, Gersztenkorn teaches the time series data includes a plurality of feature frequencies corresponding to the unimodal peak, and the one or more processors are configured to execute the step of determining the integer m - 1 as the optimum value of the order for each of the feature frequencies (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum an5, d, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16).
Regarding claim 3, Gersztenkorn teaches the one or more processors are configured to determine, in the step of determining the integer m - 1 as the optimum value of the order, whether the peak is unimodal or bimodal in a predetermined bandwidth predetermined for each of the feature frequencies (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16).
Regarding claim 4, Gersztenkorn teaches predetermined bandwidths for the feature frequencies do not overlap with each other (figure 3 – C7L51-67).
Regarding claim 5, Gersztenkorn teaches the time series data is vibration data indicating a temporal change in vibration of a diagnosis target (signal source (dynamite, vibrator, etc.) – 0C6L18-22).
Regarding claim 7, Gersztenkorn a teaches diagnosis system including a sensor (CMP gather between a shot and a receiver – C6L45-51) and a computer (general purpose programmable computer - C5L38-46), wherein the computer is configured to execute a step of acquiring time series data indicating a temporal change in a measurement result of the sensor (a time series 310 together with various Fourier transform amplitude spectra computed therefrom - C7L51-67); and
a step of determining, as an optimum value (coefficient of determination) of an order of an autoregressive model for the time series data, an integer m - 1 in which, in an autoregressive model of an order m – 1 (regression analysis … well fit by the regression model… - C12L18-56), a peak of an autoregressive spectrum of the time series data is unimodal, and in an autoregressive model of an order m, a peak of the autoregressive spectrum of the time series data is bimodal (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16).
Regarding claim 8, Gersztenkorn teaches the time series data includes a plurality of feature frequencies corresponding to the unimodal peak, and the computer is configured to execute the step of determining the integer m - 1 as the optimum value of the order for each of the feature frequencies (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16)
Regarding claim 9, Gersztenkorn teaches the computer is configured to determine, in the step of determining the integer m - 1 as the optimum value of the order, whether the peak is unimodal or bimodal in a predetermined bandwidth predetermined for each of the feature frequencies (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16)
Regarding claim 10, Gersztenkorn teaches predetermined bandwidths for the feature frequencies do not overlap with each other (figure 3 – C7L51-67).
Regarding claim 11, Gersztenkorn teaches the sensor is a vibration sensor configured to measure vibration of a diagnosis target, and the time series data is vibration data indicating a temporal change in the vibration (signal source (dynamite, vibrator, etc.) – 0C6L18-22).
Regarding claim 13, Gersztenkorn teaches a diagnosis method executed by a computer including one or more processors and one or more memory resources (general purpose programmable computer - C5L38-46), the diagnosis method comprising: a step of acquiring time series data (a time series 310 together with various Fourier transform amplitude spectra computed therefrom - C7L51-67); and a step of determining, as an optimum value (coefficient of determination) of an order of an autoregressive model for the time series data, an integer m - 1 in which 1 (regression analysis … well fit by the regression model… - C12L18-56), in an autoregressive model of an order m - 1, a peak of an autoregressive spectrum of the time series data is unimodal, and in an autoregressive model of an order m, a peak of the autoregressive spectrum of the time series data is bimodal (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16).
Regarding claim 14, Gersztenkorn teaches the time series data includes a plurality of feature frequencies corresponding to the unimodal peak, and the step of determining the integer m - 1 as the optimum value of the order is executed for each of the feature frequencies (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16).
Regarding claim 15, Gersztenkorn teaches determining, in the step of determining the integer m - 1 as the optimum value of the order, whether the peak is unimodal or bimodal in a predetermined bandwidth predetermined for each of the feature frequencies (This coefficient estimate reflects (depending on which particular coefficient is chosen) some feature related to the overall shape of the spectrum and, hence, the frequency content of the seismic trace from which it was calculated. For example, frequency spectra that are front (i.e., low frequency) loaded can be differentiated from those that are rear (i.e., high frequency) loaded because those spectra have different overall shapes, which shapes will be reflected in the estimated coefficient values. Further, spectra that are unimodal in shape can be differentiated from those that are bimodal – C4L4-16).
Allowable Subject Matter
Claims 6 and 12 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.
The following is an examiner’s statement of reasons for allowance:
Claim 6 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 because the closest prior art, Gersztenkorn [US Patent Number 6,594,585 B1], fails to anticipate or render obvious the computer is configured to acquire, in the step of acquiring the time series data, first vibration data as the time series data when the diagnosis target is in a stopped state and second vibration data as the time series data when the diagnosis target is in an operation state, execute a step of detecting a first peak which is a peak of an autoregressive spectrum of the first vibration data in the autoregressive model of the order m, execute a step of detecting a second peak which is a peak of an autoregressive spectrum of the second vibration data in the autoregressive model of the order m, and determine the integer m - 1 as the optimum value of the order of the autoregressive model for the time series data when the second peak is not included in the first peak, in combination with all other limitations in the claim(s) as defined by applicant.
Claim 12 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 because the closest prior art, Gersztenkorn [US Patent Number 6,594,585 B1], fails to anticipate or render obvious the computer is configured to acquire, in the step of acquiring the time series data, first vibration data as the time series data when the diagnosis target is in a stopped state and second vibration data as the time series data when the diagnosis target is in an operation state, execute a step of detecting a first peak which is a peak of an autoregressive spectrum of the first vibration data in the autoregressive model of the order m, execute a step of detecting a second peak which is a peak of an autoregressive spectrum of the second vibration data in the autoregressive model of the order m, and determine the integer m - 1 as the optimum value of the order of the autoregressive model for the time series data when the second peak is not included in the first peak, in combination with all other limitations in the claim(s) as defined by applicant.
Relevant Prior Art / Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Koashi (US Patent Number 6,154,708) discloses a system and method for processing and correcting spectra data;
Leuthardt et al. (US Patent Application Publication 2012/0022392 A1) discloses a system for detection of distinct narrowband, task-evoked power changes in multiple independent frequency bands for use in determining an intended cognitive task.
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
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