DETAILED ACTION
This is the First Office Action on the Merits and is directed towards claims 1-5 as originally presented and filed on 12/09/2024.
Notice of Pre-AIA or AIA Status
Priority is claimed as set forth below, accordingly the earliest effective filing date is March 15, 2024 (20240315).
The present application, effectively filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
This application claims priority to Japanese Patent Application No. 2024-040811 filed on March 15, 2024 (20240315).
Information Disclosure Statement
As required by M.P.E.P. 609 [R-07.2015], Applicant's 12/09/2024 submission(s) of Information Disclosure Statement(s) is/are acknowledged by the Examiner and the reference(s) cited therein has/have been considered in the examination of the claim(s) now pending. A copy of the submitted PTOL-1449(s) initialed and dated by the Examiner is/are attached to the instant Office action.
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 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.
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 1-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20170284068 A1 NAKAMURA; Teruo et al. (hereinafter Nakamura) in view of US 20130297141 A1 to YUN; Un-Il et al. (hereinafter Yun) and further in view of US 20190292944 A1 to MASON; John R et al. (hereinafter Mason).
Regarding claim 1 Nakamura teaches in for example the Figure(s) reproduced immediately below:
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and associated descriptive texts an information processing device that acquires original data collected and prepared over a predetermined period using a sensor mounted on a vehicle (as shown in the figures above, an information processing device connotes “computer for server 104” in for example Figs. 1, 2, 5, etc. above, using inter alia sensors 101 A-C on vehicle 501, as explained in for example para:
“[0037] The respective oil property sensors 101A, 101B, and 101C sense (measure) at least one oil property about the oil used for the operation of the hydraulic excavator 501 (all kinds of oil used in the hydraulic excavator, such as the hydraulic operating fluids of the hydraulic actuators and the engine oil, can be the target) according to the specifications thereof. Sensor signals of the respective oil property sensors 101A, 101B, and 101C are processed as appropriate and are input and stored into the computer 110 for the work machine and the computer 104 for the server as information that indicates the physical quantity of the oil property (referred to as oil property information or sensor information). Although the setting places of only the three oil property sensors 101A, 101B, and 101C are described here for simplification of description, the hydraulic excavator 501 is provided with oil property sensors besides these three sensors and there is no particular limit to the number of sensors. In the following, the plural oil property sensors provided in the hydraulic excavator 501, typified by the three oil property sensors 101A, 101B, and 101C, will be often referred to as a sensor group 101.”),
and that extracts data to be used to calculate a damage rate of an electric oil pump from the original data (see abnormality part identifying section 206 in Fig. 5 as explained in for example para:
“[0077] By the way, in the flowchart shown in FIG. 8, the configuration is made in such a manner that the discrimination processing of the abnormality level is repeated until the abnormality level of all pieces of sensor information is determined. If the abnormality level of all pieces of sensor information is acquired in this manner, it becomes possible to know what determination is made on which kind of sensor information. Furthermore, the computer 104 for the server may include the abnormal part identifying section 206 (see FIG. 5) that executes processing of identifying a part in which an abnormality exists based on the setting place of the sensor that has output the sensor information (oil property information) as the basis of a determination that oil analysis involving oil extraction is necessary if the determination is made in the second processing. The inclusion of the abnormal part identifying section 206 can identify the part that should be checked at the time of occurrence of an abnormality. This can achieve efficiency improvement and speeding-up relating to maintenance services through efficiency improvement of check work itself and enabling replacement parts or the like to be prepared in advance according to the degree of abnormality level, and so forth. Thus, it becomes possible to shorten downtime of the work machine as much as possible.”),
the information processing device comprising a processing device that executes a process, wherein:
the original data include data on a rotational speed of the electric oil pump as a feature quantity (as set forth in para:
“[0034] FIG. 4 is a configuration diagram of an oil system of the engine 601 in the hydraulic excavator 501. Engine oil is used for lubrication of the inside of the engine 601 and cooling of the engine 601. In FIG. 4, an oil pump 702 is driven in accordance with the revolution of the engine 601. The oil pump 702 sucks the engine oil from an oil pan 703 and sends the engine oil to an oil cooler 704. The engine oil cooled in the oil cooler 704 through heat exchange with cooling water in a water jacket 705 is returned to the oil pan 703 after foreign matters are removed by an oil filter 706.”);
the processing device executes a search process including a first step of calculating relative frequency distribution in the original data about the feature quantity included in the original data for each feature quantity (as shown in Fig. 6 and explained in for example para:
“[0046] FIG. 6 is a diagram showing time change in each kind of sensor information when the sensor 101A is measuring the dielectric constant (sensor information A) and the sensor 101B is measuring the viscosity (sensor information B) and the sensor 101C is measuring the density (sensor information C). In the diagram, the value of 30% of the threshold SA (warning determination value) relating to the dielectric constant is represented as SA30 and the value of 50% (abnormality determination value) is represented as SA50. The value of 30% of the threshold SB (warning determination value) relating to the viscosity is represented as SB30 and the value of 50% (abnormality determination value) is represented as SB50. The value of 30% of the threshold SC (warning determination value) relating to the density is represented as SC30 and the value of 50% (abnormality determination value) is represented as SC50.”),
a second step of setting a plurality of time windows for cutting out data for a part of a period of the original data such that a period obtained by totaling periods of all the time windows is shorter than the predetermined period (as shown in Fig. 6 and explained above),
a third step of cutting out data from the original data using the time windows (as shown in Fig. 6 and explained above),
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 (as shown in Fig. 6 and explained above),
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 (see for example para:
“[0092] The change amount determination values used by the change rank determining section 205 for the ranking are decided based on the oil analysis information of each oil property (sensor information of the second level) that is provided from the oil analysis company and is stored in the data storage device 210 of the computer 104 for the server, i.e. the track record value of the past change amount. The present embodiment uses a first change amount determination value for determining whether or not the change amount calculated in S901 is equivalent to the past change amount and a second change amount determination value for determining that the degree of abnormality level of oil is predicted to early reach the “abnormality determination” because the change amount calculated in S901 is very large. A change in a step function manner or a rapid change in a quadratic function manner or exponential function manner often appears in the sensor information due to any abnormality such as entry of water into oil or entry of dust or the like. The second change amount determination value is set to sense this kind of change. As a result, the second change amount determination value is set to a value larger than the first change amount determination value. Furthermore, an error in measurement of each sensor often appears in the change amount. Thus, in deciding the first change amount determination value and the second change amount determination value, it is preferable to decide them in such a manner that an erroneous determination is not made even when this kind of error occurs.”);
and the processing device extracts the extracted data whose error is equal to or less than a threshold by executing the search process that repeatedly makes trials to execute the second step to the fifth step while changing settings of the time windows after executing the first step (as shown in Fig. 6 and explained above).
While Nakamura appears to teach the invention as claimed and explained above Nakamura does not appear to expressly disclose that the “data to be used to calculate a damage rate of an electric oil pump”,
and the processing device extracts the extracted data whose error is equal to or less than a threshold by executing the search process that repeatedly makes trials to execute the second step to the fifth step while changing settings of the time windows after executing the first step.
In analogous art Yun teaches in for example, the figures below:
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And associated descriptive texts the data to be used to calculate a damage rate of an electric oil pump (in the figures above as well as para;
“[0008] For example, methods of monitoring or diagnosing the state of a vehicle presented in a vehicle maintenance system function to continuously observe vehicle data, such as the travel distance, oil pressure over time, and battery voltage of each vehicle, monitor the state of the vehicle which is approaching a time point (threshold) at which the abnormal state of the corresponding device statistically occurs, and notify a vehicle driver or a vehicle management system of the monitored state. FIG. 1 is a graph applied to a method in which a module for diagnosing the state of a vehicle monitors the state of a battery voltage. Referring to FIG. 1, `A` denotes an area indicative of a state which the battery voltage is approaching that of a time point `B` at which an abnormality statistically occurs in the battery while the module for diagnosing the state of the vehicle is continuously observing the battery voltage.”),
and the processing device extracts the extracted data whose error is equal to or less than a threshold by executing the search process that repeatedly makes trials to execute the second step to the fifth step while changing settings of the time windows after executing the first step (as shown in the figures above especially Fig. 7 and 8 steps S420 and S612 as explained in for example, paras:
“[0071] Referring to FIG. 7, the vehicle abnormal state monitoring apparatus using the clustering technique extracts at least one piece of data from each cluster at step S410, calculates a difference between the maximum value and the minimum value of individual attributes constituting the extracted at least one piece of data, and decides on the representative attribute of the corresponding cluster by determining whether the calculated difference falls within a preset threshold range at step S420.
[0076] The vehicle abnormal state monitoring apparatus using the clustering technique determines whether the state feature of the cluster found at step S611 corresponds to a normal state at step S612.”).
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 clustering disclosed in Yun with the periodic checks taught in Nakamura with a reasonable expectation of success because it would have “ensured” as taught by Yun Para(s) :
“[0077] The vehicle abnormal state monitoring apparatus using the clustering technique is configured to, if it is determined that the state feature corresponds to the normal state, terminate the monitoring of the state of the vehicle, whereas if it is determined that the state feature corresponds to an abnormal state, notify a vehicle driver of a danger due to the vehicle being in an abnormal state or having a high probability of being in the abnormal state at step S613.”.
The combination of Nakamura does not appear to expressly disclose however in analogous art Mason teaches in for example, the figures below:
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And associated descriptive texts determining damage to an electric oil pump in for example paras:
“[0048] an electric pump arranged to supply the bearings of the gearbox with oil once activated;
[0165] At step 502, oil is provided to the gearbox through a primary oil system 50 driven by a core 11 of the engine 10.
[0166] At step 504, windmilling conditions and/or failure of the primary oil system 50 and/or engine shutdown is detected.
[0167] At step 506, in response to the detected condition or failure, an electric pump 61 of an auxiliary oil system 60 is activated, to provide oil to the gearbox 30.”)
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 electric oil pump disclosed in Mason with the oil pump taught in the combination of Nakamura with a reasonable expectation of success because it would have provided an art level equivalent for a known component.
Regarding claim 2 and the limitation 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 original 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 an entirety of the original data is equal to or less than a threshold (see the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference wherein it is understood that Fig. 5 of Yun connotes the claimed limitations).
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Regarding claim 3 and the limitation the information processing device according to claim 1, wherein the processing device terminates the search process when one piece of the extracted data whose error is equal to or less than the threshold is successfully extracted,
and calculates the damage rate using the extracted data whose error is equal to or less than the threshold (see the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference wherein it is understood that Yun Teaches clustering and all the references teach detecting abnormalities and alerting or alarming the operator etc.).
Regarding claim 4 and the limitation the information processing device according to claim 1, wherein:
the feature quantity further includes data on a temperature of the electric oil pump and a discharge pressure of the electric oil pump (see Yun “Engine oil temperature 9” and Mason “a gearbox oil pressure sensor,”);
and the processing device calculates the damage rate based on at least one of the rotational speed,
the discharge pressure of the electric oil pump,
and the temperature of the electric oil pump (see the teachings of each reference with regard to the various methods of determining damage to the oil pump in obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference).
Regarding claim 5 and the limitation the information processing device according to claim 4, wherein the processing device calculates the damage rate using the extracted data whose error is equal to or less than the threshold,
and makes a notification that occurrence of a failure has been predicted when the damage rate is equal to or more than a predetermined value (see for example the obviousness to combine and the rejection of corresponding parts of claims 4 and 2 and 1 above incorporated herein by reference.
each reference with regard to the notifications generated to show the oil pump has been damaged, for example see Yun para:
“[0077] The vehicle abnormal state monitoring apparatus using the clustering technique is configured to, if it is determined that the state feature corresponds to the normal state, terminate the monitoring of the state of the vehicle, whereas if it is determined that the state feature corresponds to an abnormal state, notify a vehicle driver of a danger due to the vehicle being in an abnormal state or having a high probability of being in the abnormal state at step S613.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as teaching, inter alia, the state of the art of detecting oil pump failures at the time of the invention. For example:
US 20150066259 A1 to Thompson; Scott James teaches, inter alia an Engine Oil Maintenance Monitor For A Hybrid Electric Vehicle in for example the ABSTRACT, Figures and/or Paragraphs below:
“A hybrid vehicle is provided with an engine having a crankshaft and an electric machine coupled to the crankshaft. The hybrid vehicle also includes a pump and a controller. The pump is driven by rotation of the crankshaft and coupled to the engine by a fluid circuit. The controller is configured to control the electric machine in response to a wheel torque request to drive the crankshaft with the engine off to provide lubricant to the engine.”.
US 20140297045 A1 to Apostolides; John K. teaches, inter alia an OIL SYSTEM in for example the ABSTRACT, Figures and/or Paragraphs below:
“[0101] In general, it will be apparent to one of ordinary skill in the art that various embodiments described herein, or components or parts thereof, may be implemented in many different embodiments of software, firmware, and/or hardware, or modules thereof. The software code or specialized control hardware used to implement some of the present embodiments is not limiting of the present invention. For example, the embodiments described hereinabove may be implemented in computer software using any suitable computer programming language such as .NET, SQL, MySQL, or HTML using, for example, conventional or object-oriented techniques. Programming languages for computer software and other computer-implemented instructions may be translated into machine language by a compiler or an assembler before execution and/or may be translated directly at run time by an interpreter. Examples of assembly languages include ARM, MIPS, and x86; examples of high level languages include Ada, BASIC, C, C++, C#, COBOL, Fortran, Java, Lisp, Pascal, Object Pascal; and examples of scripting languages include Bourne script, JavaScript, Python, Ruby, PHP, and Perl. Various embodiments may be employed in a Lotus Notes environment, for example. Such software may be stored on any type of suitable computer-readable medium or media such as, for example, a magnetic or optical storage medium. Thus, the operation and behavior of the embodiments are described without specific reference to the actual software code or specialized hardware components. The absence of such specific references is feasible because it is clearly understood that artisans of ordinary skill would be able to design software and control hardware to implement the embodiments of the present invention based on the description herein with only a reasonable effort and without undue experimentation.).”.
US 20190338792 A1 to HAYASHI; Toshikazu et al. teaches, inter alia A METHOD AND SYSTEM FOR DIAGNOSING ABNORMALITY OF HYDRAULIC DEVICE in for example the ABSTRACT, Figures and/or Paragraphs below:
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“An abnormality diagnosis method is targeted at a hydraulic device which includes a hydraulic pump and a driven device driven by the hydraulic pump. The method includes calculating a frequency distribution with regard to a deviation between a normal value of an output parameter corresponding to an operation condition and an actual measurement value of the output parameter using a prediction model, and determining the presence of an abnormality if an average of the deviation exceeds a threshold. If the presence of the abnormality is determined, a factor of the abnormality is estimated based on the range of the deviation where a waveform peak of the frequency distribution exists.”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LAWSON GREENE JR whose telephone number is (571)272-6876. The examiner can normally be reached on MON-THUR 7-5:30PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached on (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL L GREENE/Primary Examiner, Art Unit 3665 20260207