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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Application 19027180
Co-pending application 19033502
1. An information processing apparatus that acquires original data collected and created over a prescribed period of time using a plurality of sensors mounted in a vehicle and calculates an indicator value that indicates how large damage accumulated in a drive motor is, the information processing apparatus comprising a processing device configured to execute processing, wherein: the original data includes, as feature amounts, data regarding a voltage applied to the drive motor; and
the processing device executes search processing that includes a first step of calculating, for each of the feature amounts, a relative frequency distribution in the original data in regard to the feature amounts included in the original data,
a second step of setting a plurality of time windows for cutting data corresponding to partial periods of the original data such that a period obtained by adding up periods of all the time windows is shorter than the prescribed period,
a third step of cutting data from the original data using the plurality of time windows,
a fourth step of calculating, for each of the feature amounts, the relative frequency distribution in extracted data obtained by connecting the data cut using the plurality of time windows, and
a fifth step of calculating an error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data, a trial from the second step to the fifth step being repeated with a setting of the plurality of time windows changed after execution of the first step to extract the extracted data with which the error is equal to or less than a threshold value in the search processing, and calculating the indicator value using the extracted data with which the error is equal to or less than the threshold value.
1. An information processing device that reduces an amount of data to be used for analysis by extracting a part of data from original data collected over a predetermined period using a plurality of sensors mounted on a vehicle, the information processing device comprising a
processing device that executes a process, wherein:
the processing device executes a search process including a first step of calculating relative frequency distribution in object data obtained by excluding data for an initial prescribed period from the original data about a plurality of feature quantities included in the original data for each feature quantity,
a second step of setting a plurality of time windows for cutting out data for a part of a period of the object data such that a period obtained by totaling periods of all the time windows is shorter than an object period obtained by excluding the initial prescribed period from the predetermined period,
a third step of cutting out data from the object data using the time windows,
a fourth step of calculating relative frequency distribution in extracted data obtained by combining the data cut out using the time windows for each feature quantity, and
a fifth step of calculating an error between the relative frequency distribution in the object data and the relative frequency distribution in the extracted data; andthe processing device extracts the extracted data whose error is equal to or less than a threshold by executing the first step and thereafter repeatedly making trials to execute the second step to the fifth step while changing settings of the time windows.
2. The information processing apparatus according to claim 1, wherein: the processing device executes clustering which is machine learning of categorizing data of each section obtained by sectioning the original data for each specific period into a prescribed number of clusters; and 2 the processing device sets the plurality of time windows such that a difference between a ratio of each cluster in the extracted data and a ratio of each cluster in entire original data is equal to or less than a threshold value in the second step.
2. The information processing device according to claim 1, wherein: the processing device executes clustering that is machine learning to classify data in sections obtained by dividing the object data for each certain period into a predetermined number of clusters; and the processing device sets the time windows in the second step such that a difference between a ratio of each cluster in the extracted data and a ratio of each cluster in the entire object data is equal to or less than a threshold
Claims 1-2 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2 of copending Application No. 16350273 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because each limitation of the claims 1-2 in the present application is encompassed in the claims 1-2 of copending Application No. 19033502 or is an obvious variation. Furthermore, applications below are obvious variation to the instant application and thus rejected under double patenting: 18/965,101, 19/070,982, 18/937,459, 18/815,857, 18979273, 18/973,692, 18937026, 18422831.
Appropriate action is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
1. An information processing apparatus that acquires original data collected and created over a prescribed period of time using a plurality of sensors mounted in a vehicle and calculates an indicator value that indicates how large damage accumulated in a drive motor is, the information processing apparatus comprising a processing device configured to execute processing, wherein: the original data includes, as feature amounts, data regarding a voltage applied to the drive motor; and the processing device executes search processing that includes a first step of calculating, for each of the feature amounts, a relative frequency distribution in the original data in regard to the feature amounts included in the original data, a second step of setting a plurality of time windows for cutting data corresponding to partial periods of the original data such that a period obtained by adding up periods of all the time windows is shorter than the prescribed period, a third step of cutting data from the original data using the plurality of time windows, a fourth step of calculating, for each of the feature amounts, the relative frequency distribution in extracted data obtained by connecting the data cut using the plurality of time windows, and a fifth step of calculating an error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data, a trial from the second step to the fifth step being repeated with a setting of the plurality of time windows changed after execution of the first step to extract the extracted data with which the error is equal to or less than a threshold value in the search processing, and calculating the indicator value using the extracted data with which the error is equal to or less than the threshold value.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be
analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of the steps 1 to 5, i.e. calculating... These limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “by the processing device”. That is, other than reciting “by processing device” nothing in the claim elements precludes the step from practically being performed mathematically. The claim recites a mathematical formula or calculation that is used for the search process. Thus, the claim recites a mathematical concept.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application 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. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of acquiring or collecting original data from a plurality of sensors mounted on the vehicle, electric motor, and processing device. The receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle data for use in the calculating steps), and amount to mere data gathering, which is a form of insignificant extra-solution activity. The “processing device” merely describes how to generally “apply” the otherwise mental judgements using a generic or general-purpose vehicle control environment, i.e. a computer. The processing device is recited at a high level of generality and is merely automates the calculating step.
The sensor and electric motor are claimed generically and is operating in its ordinary capacity and does not 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 exception. The additional limitation is no more than mere instructions to apply the exception using a computer (the sensor).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra- solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving steps and the displaying step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the sensors are all conventional sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC
v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Dependent claims 2-4 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application such as calculating… breakage .. setting plurality of time windows…. Therefore, dependent claims 2-4 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
More specifically, the phrase “how large the damage accumulated in a drive is”. The term “large” is a relative term which renders the claim indefinite. The term “large” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The phrase “feature amounts” is unclear as to whether the applicant meant the size of the data or the characteristic of the feature.
The phrase “relative frequency distribution” is unclear as to relative to what frequency.
The phrase “period obtained by adding up periods… is shorter than the prescribed period…” is unclear as to whether the added periods are sampled or down-sampled or there is another reason the period is shorter.
The term “trial” is unclear because it not clear what is meant by trial. Does the trial in this context mean sampling or training a model…
The phrase “fifth step being repeated with a setting of the plurality of time windows changed after execution of the first step to extract the extracted data with which the error is equal to or less than a threshold value” is unclear as to what will happen if the error is greater than the threshold. Furthermore, time windows is changing based on what parameter or condition.
Regarding claims 2-3, claims 2-3 are rejected under 112(b) for being dependent on claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over YANG (CN113255840A) in view of KATO (EP4040135A1).
Regarding claim 1, YANG discloses an information processing apparatus that acquires original data collected (abstract, “apparatus, system and storage medium, the method comprising: sensing raw data ”) and created over a prescribed period of time using a plurality of sensors (abstract, “sensing raw data of a process parameter by a sensor; segmenting the original data according to a preset time period”) and calculates an indicator value that indicates how large damage accumulated (page 10, “calculating a mean frequency (mean frequency). The characteristic data has correlation with the 'good or bad' … The CHANNEL-ET process can be subjected to fault detection and classification based on the characteristic data”), the information processing apparatus comprising a processing device configured to execute processing (“page 4, lines 19-21, “computer-readable storage medium, on which a computer program is stored, which when executed by a processor”, page 4, line 22, “The fault detection and classification method, device, system”), wherein: the original data includes, as feature amounts, data (abstract, line 23, “obtain feature data which is relevant to at least one characteristic of the product”); and the processing device executes search processing that includes a first step of calculating, for each of the feature amounts, a relative frequency distribution in the original data in regard to the feature amounts included in the original data (abstract, “a frequency domain algorithm and/or a statistical probability distribution algorithm to obtain feature data which is relevant to at least one characteristic of the product”), a second step of setting a plurality of time windows for cutting data (page 9, lines 9-11, “sense a plurality of time series of a plurality of process parameters (temperature, pressure, flow, etc.), a plurality of time series after slicing are obtained by slicing the plurality of time series”) a third step of cutting data from the original data (abstract, “segmenting the original data according to a preset time period”), a fourth step of calculating, for each of the feature amounts, the relative frequency distribution in extracted data obtained by connecting the data cut using the plurality of time windows (page 07, lines 11-14, “, the sliced data may be fourier-transformed to obtain a frequency spectrum or a power spectral density, and then at least one of a center-of-gravity frequency”, page 11, lines 31-34, “the feature extraction module 33 is specifically configured to apply a frequency domain algorithm and/or a statistical probability distribution algorithm to perform feature extraction on the segmented data”).
YANG does not explicitly disclose but, Kato teaches sensor mounted on a vehicle (page 04, line 1-2, “a current sensor … each inverter driving the motor, e.g. of an automotive vehicle,”); how large damage accumulated in a drive motor is (page 05, lines 4-7, “each failure or the like may return a frequency peak and the amplitude thereof may be used as an indicator for the severity of a possible damage/failure”); regarding a voltage applied to the drive motor (page 10, lines 23-26, “a measurement signal as recorded by a measurement unit (non-rotary sensor) 201. In this specific example, the y-axis shows an electric current amplitude in Ampere while the x-axis indicates the time in seconds .. electric motor”).
Kato further teaches using the plurality of time windows, corresponding to partial periods of the original data (page 7, lines 13-15, “a size (or width or length in the x-axis direction) of a particular window of the plurality of windows may be a time that is predetermined or that is adapted dynamically with respect to a rotational speed of the rotating machine”); such that a period obtained by adding up periods of all the time windows is shorter than the prescribed period (page 07, lines 40-42, page 8, lines1-2, “data points which are within the first/last 10% or less of the window size/width… the windows may be arranged to overlap each other, to be perfectly sequential or a small gap may be between the windows… Data points in said possible gap may be considered to between windows as well as data points which are in an overlapping area of two window”);
and a fifth step of calculating an error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data (page 13, lines 13-20, “By analyzing the overall resampling/resampled signal 331 … applying a FFT and preferably also an order analysis, a single distribution pattern 333 is calculated… Based on the single distribution pattern 333, a diagnosis of the state of the rotational machine can be executed …in case the distribution pattern 333 differs from a predetermined reference pattern in percentage that is higher than a given threshold or includes additional not expected peaks, the rotational machine may be considered to be in a failure state”), a trial from the second step to the fifth step being repeated with a setting of the plurality of time windows changed after execution of the first step to extract the extracted data with which the error is equal to or less than a threshold value in the search processing, and calculating the indicator value using the extracted data with which the error is equal to or less than the threshold value (page 05, lines 1-7, “each sub-section of the sampled signal of each window may be converted into its frequency components, i.e. frequency spectra…the machine to be analyzed/diagnosed may return a characteristic frequency peak in the frequency spectrum having the frequency on the x-axis. Further, each failure or the like may return a frequency peak and the amplitude thereof may be used as an indicator for the severity of a possible damage/failure..”, page 06, lines 1-4, “noise level may be used as an indicator for said iteration, which may mean that the final N value is found when a minimum noise or a noise level below a threshold is reached in the FFT-transformed spectrum”).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the fault classification device disclosed in YANG with the motor state detection taught in Kato with a reasonable expectation of success because it would have targeted enabling a more accurate diagnosis of machines which are operated at a variable operational speed.
Regarding claim 2, Kato discloses wherein: the processing device executes clustering which is machine learning of categorizing data of each section obtained by sectioning the original data for each specific period into a prescribed number of clusters; and the processing device sets the plurality of time windows such that a difference between a ratio of each cluster in the extracted data and a ratio of each cluster in entire original data is equal to or less than a threshold value in the second step (page 07, lines 21-31, “the window size/length of a sub-section divided by a window may be adapted such that at a lower frequency the window width/size may be shorter and for a high frequency the window may be set wider/greater size”, “The width of the window can also be adapted automatically by starting with a predefined value for each of the windows, either being equal for each window or a different start width is preset for each window, and an trained machine learning unit or a trained Al may adapt the window size(s)/width(s) such as to receive an optimized output”, page 12, lines 32-37, “The data of the signal 301 in each window 303, 305, 307, and 309 are resampled according to the following formula (2), as indicated by arrow 335 wherein "Pm" is a number of resampled points in a particular window, "am" is an N-th harmonic frequency in a particular window, "A" is a maximum N-th harmonics frequency overall windows, and "PO" is a number of original data points in a particular window. Thus, the ratio of "am" and "A" serves as a resampling indicator. Pm=am/A*PO ”).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the fault classification device disclosed in YANG with the motor state detection taught in Kato with a reasonable expectation of success because it would have targeted enabling a more accurate diagnosis of machines which are operated at a variable operational speed.
Regarding claim 3, YANG discloses wherein the original data includes, as the feature amounts, data regarding a temperature of the drive motor and data regarding an atmospheric pressure (page 09, line 9-12, “, a plurality of sensors sense a plurality of time series of a plurality of process parameters (temperature, pressure, flow, etc.), a plurality of time series after slicing are obtained by slicing the plurality of time series”).
Regarding claim 4, Kato further teaches wherein the processing device calculates a degree of breakage indicating a proportion of accumulated damage with respect to damage that leads to breakage as the indicator value (page 05, lines 5-7, “regard it is noted that each part/component of the machine to be analyzed/diagnosed may return a characteristic frequency peak in the frequency spectrum having the frequency on the x-axis. Further, each failure or the like may return a frequency peak and the amplitude thereof may be used as an indicator for the severity of a possible damage/failure.”).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the fault classification device disclosed in YANG with the motor state detection taught in Kato with a reasonable expectation of success because it would have targeted enabling a more accurate diagnosis of machines which are operated at a variable operational speed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nidd (US 10971000) discloses The invention relates to a computer-implemented method for processing events. The method provides a database comprising original event objects stored in association with canonical event objects. The method executes a learning algorithm on the associated original and canonical event objects for generating a trained ML program adapted to transform an original event object of any one of the one or more original data formats into a canonical event object having the canonical data format and uses the trained machine learning program for automatically transforming original event objects generated by an active IT-monitoring system into canonical event objects processable by an event handling system (abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REDHWAN K MAWARI whose telephone number is (571)270-1535. The examiner can normally be reached mon-Fri 8-5.
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/REDHWAN K MAWARI/ Primary Examiner, Art Unit 3664