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 . 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.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
Claims 1-4, 7-10 are 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.
Claims Must Particularly Point Out and Distinctly Claim the Invention (MPEP 2173, MPEP 2111.04(II), MPEP 2143.03), therefore, term" only if" and “if” make scope of these claim broad as the limitations related to these terms are interpreted as optional and therefore not required.
Claim 1 recites “determining the content of the substance composition in the viscous material based on the signal detection waveform only if the detection state is the normal detection state”[emphasis added]. Based on the broadest reasonable interpretation (BRI) the claim is interpreted that if detection state is not normal detection state, the limitation “determining the content of the substance composition in the viscous material based on the signal detection waveform, wherein the substance composition in the viscous material is a composition of at least one impurity substance in the viscous material” is not required, in other words, the limitation determining the content of the substance composition in the viscous material based on the signal detection waveform, wherein the substance composition in the viscous material is a composition of at least one impurity substance in the viscous material” is required if only if the detection state is the normal detection state. Same issue is for limitation “wherein the waiting interval is a first waiting interval if the detection state is determined to be the abnormal detection state, and the waiting interval is a second waiting interval if the detection state is determined to be the normal detection state”[emphasis added].
Claims 4 and 8 have the same issue and are rejected for the same reason and interpreted in a similar way.
Examiner recommends to amend “if” and “only if” with terms such as “in response to” to overcome the rejection.
Remaining Claims are rejected because of their dependency to a rejected claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hu, US 20040239344 A1 in view of Braun, US5592395A.
Claim 1
Hu in figs.1-9 teaches:
An automatic detection method for detecting a content of a substance composition in a viscous material, comprising:
performing a detecting process (using 16 and 17 in fig.1) for the viscous material (e.g., ¶0001 fluids such as oil) in a preset detection period (any measurement period is read on a preset period) in order to obtain a signal detection value (electrical impedance values from 16, e.g., ¶0086) corresponding to the viscous material (oil), and generating a signal detection waveform (e.g., figs.3-4);
determining a detection state (fig.9) corresponding to the signal detection waveform according to the signal detection waveform (steps 42-43), the detection state comprising a normal detection state (44: NO to 45) and an abnormal detection state (44: YES to 48; and
determining the content of the substance composition (Contamination in 55,51) in the viscous material (oil) based on the signal detection waveform (44) only if the detection state is the normal detection state (one peak),
wherein the substance composition (soot contamination 53 or sulfur 51 ) in the viscous material (oil) is a composition of at least one impurity substance in the viscous material (oil).
Hu teaches the automatic method (e.g., ¶0123) but does not specifically teach wherein the automatic detection method further comprises: determining a waiting interval between a next detection and a current detection according to the detection state; wherein the waiting interval is a first waiting interval if the detection state is determined to be the abnormal detection state, and the waiting interval is a second waiting interval if the detection state is determined to be the normal detection state, and wherein the first waiting interval is smaller than the second waiting interval.
In the similar field of endeavor, Braun teaches wherein the automatic detection method (e.g., col.3 L.19 using control unit 5) further comprises: determining a waiting interval (service time interval: col.2 L.55-60) between a next detection and a current detection according to the detection state (e.g., Col.2 L.48-53, COL.3 l.3-10); wherein the waiting interval is a first waiting interval (unit specific interval) if the detection state is determined to be the abnormal detection state (col.2 L.61-67:unit specific interval after monitoring abnormality by 7), and the waiting interval is a second waiting interval if the detection state is determined to be the normal detection state (service time interval before detection of abnormality by 7), and wherein the first waiting interval is smaller than the second waiting interval (e.g., col. 3 L.15-17: maximum possible service life of the operating fluid).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Braun‘s waiting interval for Hu‘s automatic detection method. One of ordinary skill in the art would have been motivated to make this modification in order to automating effective oil change (Braun col.1 L.45-54).
Claim 3
Hu in view of Braun teaches the automatic detection method according to claim 1,
Hu in fig.9 teaches wherein the determining a detection state corresponding to the signal detection waveform according to the signal detection waveform comprises: determining a number of valleys (step 44) in the signal detection; and determining the detection state based on the number of valleys (if two peak controller determines the water content and if one peak it determines the contamination).
Claim 10
Hu in view of Braun teaches the method of claim 1, Hu teaches an automatic detection (e.g., ¶0123) device for detecting a content of a substance composition (e.g., soot and sulfur 51,53 of fig.9) in a viscous material (oil), wherein the automatic detection device (fig.1: 16,17) realizes a detection of the content of the viscous material (soot and sulfur) according to the automatic detection method for detecting a content of a substance composition in the viscous material according to claim 1.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Hu, US 20040239344 A1in view of Braun, US5592395A and Cella, US 20190041843 A1.
Claim 2
Hu in view of Braun teaches the automatic detection method according to claim 1, but the combination does not necessarily teach wherein the performing a detecting process for the viscous material in a preset detection period in order to obtain a signal detection value corresponding to the viscous material and generating a signal detection waveform comprises:
performing a detection at a preset detection interval in the preset detection period by using a sensor detection circuit to obtain a plurality of signal detection values corresponding to the viscous material, wherein the plurality of signal detection values are values of the same type recorded at different points in time; and performing a fitting process based on the plurality of signal detection values to obtain the signal detection waveform. However, performing a fitting process that is comprising to obtain a plurality of signal detection values corresponding to a material, wherein the plurality of signal detection values are values of the same type recorded at different points in time; and performing a fitting process based on the plurality of signal detection values to obtain the signal detection is a routine and well known signal processing, and for example In the similar field of endeavor, Cella teaches obtain a plurality of signal detection values corresponding to a material, wherein the plurality of signal detection values are values of the same type recorded at different points in time (sensor measurements over time: e.g., figs.25-30); and performing a fitting process based on the plurality of signal detection values to obtain the signal detection (¶0259) and therefore It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Cella‘s signal processing for the modified Hu‘s method wherein the performing a detecting process for the viscous material in a preset detection period in order to obtain a signal detection value corresponding to the viscous material and generating a signal detection waveform comprises: performing a detection at a preset detection interval in the preset detection period by using a sensor detection circuit to obtain a plurality of signal detection values corresponding to the viscous material, wherein the plurality of signal detection values are values of the same type recorded at different points in time; and performing a fitting process based on the plurality of signal detection values to obtain the signal detection waveform . One of ordinary skill in the art knows using machine learning and would have been motivated to make this modification in order to use this method to make a learning model to auto analyses of materials with the same pattern (see Zhu’s method), and based on MPEP 2143 (C), courts have ruled that Use of known technique to improve similar devices (methods, or products) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hu, US 20040239344 A1 in view of Braun, US5592395A and Cella-2, US 11092955 B2.
Claim 7
Hu in view of Braun teaches the automatic detection method according to claim 1, the modification does not specifically teach wherein the viscous material is a viscous material arranged on a target component of a fan, and a sensor detection circuit receives the viscous material on the target component of the fan, and wherein, in the first waiting interval, continuing to receive the viscous material by the sensor detection circuit; and in the second waiting interval, blowing the viscous material received by the sensor detection circuit away from the sensor detection circuit and then continuing to receive the viscous material. However, first of all as cited above, the method can be used for lubrication oil of any type of device such as a target component of a fan. For example, In the similar field of endeavor, Cella-2 teaches an analysis method for fan (e.eg, col.79 L.48 and col.82 L.18), secondly it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the modified Rocco‘s method for a target component of a fan and wherein, blowing the viscous material received by the sensor detection circuit away from the sensor detection circuit and then continuing to receive the viscous material for better determination and validation of the abnormal state. Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Cella‘s fan for the modified Hu‘s automatic detection method wherein the viscous material is a viscous material arranged on a target component of a fan, and a sensor detection circuit receives the viscous material on the target component of the fan, and wherein, in the first waiting interval, continuing to receive the viscous material by the sensor detection circuit; and in the second waiting interval, blowing the viscous material received by the sensor detection circuit away from the sensor detection circuit and then continuing to receive the viscous material. One of ordinary skill in the art would have been motivated to make this modification in order to use the method for applications including fans, based on MPEP 2143 (C), courts have ruled that Use of known technique to improve similar devices (methods, or products) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Hu, US 20040239344 A1 in view of Braun, US5592395A and Young, US 20180299375 A1.
Claim 8
Hu in view of Braun teaches the automatic detection method according to claim 1, the combination does not specifically teach wherein the determining a content of the substance composition in the viscous material based on the signal detection waveform only if the detection state is the normal detection state comprises: selecting a valley with a smallest valley value in the signal detection waveform as a target valley, and determining a detection valley value based on the valley value of the target valley, wherein the valley value is a signal value corresponding to the valley; determining a detection environment parameter of the viscous material, the detection environment parameter comprising at least one of a detection temperature and a detection humidity; and inputting the detection environment parameter and the detection valley value into a machine learning model, and processing the detection environment parameter and the detection valley value by the machine learning model to obtain the content of the substance composition in the viscous material. However, this is obvious over method steps of using a machine learning modeling, for example In the similar field of endeavor, Young teaches selecting a target valley (e.g., figs.38-39), and determining a detection valley value based on the valley value of the target valley, wherein the valley value is a signal value corresponding to the valley; determining a detection environment parameter of the viscous material (temperature e.g., ¶0161), the detection environment parameter comprising at least one of a detection temperature (¶0161); and inputting the detection environment parameter and the detection valley value into a machine learning model (e.g., ¶0401), and processing the detection environment parameter and the detection valley value by the machine learning model to obtain the viscous material conditions(e.g., Abstract: Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.). Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Young‘s machine learning models for the modified Hu‘s method wherein the determining a content of the substance composition in the viscous material based on the signal detection waveform only if the detection state is the normal detection state comprises: selecting a valley with a smallest valley value in the signal detection waveform as a target valley, and determining a detection valley value based on the valley value of the target valley, wherein the valley value is a signal value corresponding to the valley; determining a detection environment parameter of the viscous material, the detection environment parameter comprising at least one of a detection temperature and a detection humidity; and inputting the detection environment parameter and the detection valley value into a machine learning model, and processing the detection environment parameter and the detection valley value by the machine learning model to obtain the content of the substance composition in the viscous material. One of ordinary skill in the art would have been motivated to make this modification in order to analyze the fluid conditions ( Young, Abstract), based on MPEP 2143 (C), courts have ruled that Use of known technique to improve similar devices (methods, or products) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claim 9
Hu in view of Braun and Young teach the automatic detection method according to claim 8, Young teaches wherein the determining a detection valley value based on a valley value of the target valley comprises :acquiring a preset number of additional signal detection waveforms with the normal detection state(different samples for any ML such as samples figs.38-39); determining a valley with the smallest valley value in each of the preset number of signal detection waveforms as the target valley; and weighting and averaging the valley values of the target valleys of the preset number of additional signal detection waveforms to obtain the detection valley value (e.g., ¶0466), for the same reason and motivation as cited above for claim 8.
Claims 1 and 10 are also rejected under 35 U.S.C. 103 as being unpatentable over Rocco1, US20200264135A1 in view of Braun, US5592395A.
Claim 1
Rocco teaches:
An automatic detection method for detecting a content of a substance composition in a viscous material, comprising:
performing a detecting process (300 using 124 in fig.5) for the viscous material (oil debris, metals in oil e.g., ¶0001) in a preset detection period (detection duration reads on this) in order to obtain a signal detection value (signals from monitor sensor e.g., ¶0005) corresponding to the viscous material (oil debris), and generating a signal detection waveform (e.g., collecting I and Q channel data from an oil debris monitor sensor, step 308-314: fig.8);
determining a detection state (symmetric or not symmetric: e.g., ¶0006 and step 704 in fig.22) corresponding to the signal detection waveform (I,Q fig.6) according to the signal detection waveform (I and Q fig.6), the detection state comprising a normal detection state (704,218 symmetric I,Q) and an abnormal detection state (704,706); and
determining the content of the substance (218 in fig.22) composition in the viscous material (oil debris) based on the signal detection waveform (704) only if the detection state is the normal detection state (704 Yes for symmetric 1,Q),
wherein the substance composition (particles debris 218, e.g., ¶0084-¶0086) in the viscous material (oil debris) is a composition of at least one impurity substance in the viscous material (e.g., ¶0084-¶0086); and
wherein the automatic detection method (e.g., ¶087,500,600,700).
Rocco does not specifically teach wherein the automatic detection method further comprises:
determining a waiting interval between a next detection and a current detection according to the detection state; wherein the waiting interval is a first waiting interval if the detection state is determined to be the abnormal detection state, and the waiting interval is a second waiting interval if the detection state is determined to be the normal detection state, and wherein the first waiting interval is smaller than the second waiting interval.
In the similar field of endeavor, Braun teaches wherein the automatic detection method (e.g., col.3 L.19 using control unit 5) further comprises: determining a waiting interval (service time interval: col.2 L.55-60) between a next detection and a current detection according to the detection state (e.g., Col.2 L.48-53, COL.3 l.3-10); wherein the waiting interval is a first waiting interval (unit specific interval) if the detection state is determined to be the abnormal detection state (col.2 L.61-67:unit specific interval after monitoring abnormality by 7), and the waiting interval is a second waiting interval if the detection state is determined to be the normal detection state (service time interval before detection of abnormality by 7), and wherein the first waiting interval is smaller than the second waiting interval (e.g., col. 3 L.15-17: maximum possible service life of the operating fluid).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Braun‘s waiting interval for Rocco‘s automatic detection method. One of ordinary skill in the art would have been motivated to make this modification in order to automating effective oil change (Braun col.1 L.45-54).
Claim 10
Rocco in view of Braun teaches the method of claim 1, Rocco teaches an automatic detection (e.g., ¶087,500,600,700) device for detecting a content of a substance composition (e.g., Fe particles in fig.22 704) in a viscous material (oil debris ¶0005), wherein the automatic detection device 110 realizes a detection of the content of the viscous material (Fe) according to the automatic detection method for detecting a content of a substance composition in the viscous material according to claim 1.
Allowable subject matter
Claim 4 comprises allowable subject matter, however depends on a rejected claim and also has 112 b issues, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and also amended to overcome 112 b rejection.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 4: The prior art, alone or in combination, fails to anticipate or render obvious an assembly comprising an automatic detection method for detecting a content of a substance composition in a viscous material in the case where three or more valleys exist in the signal detection waveform, sorting the valley values of the valleys from smallest to largest to obtain a valley sequence, calculating an absolute value of a difference between a first valley and a second valley in the valley sequence, comparing the absolute value of the difference with a preset additional threshold, determining the detection state as the normal detection state if the absolute value of the difference is greater than or equal to the preset additional threshold; and determining the detection state as the abnormal detection state if the absolute value of the difference is less than the preset additional threshold, in conjunction with the remaining claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Szyman, US9645111B2
Szyman teaches Applying AC signals to a viscous material, create a waveform of impedance across frequencies and analyses peaks and inflection points to study the viscous material.
Yaita , US 11635444 B2
Yaita teaches interpolation of time series data points and produces fitting behavior by producing curves.
Zhu, Shan, et al. "Equivalent circuit model recognition of electrochemical impedance spectroscopy via machine learning." Journal of Electroanalytical Chemistry 855 (2019): 113627. (Year: 2019)
Zhu teaches machine learning to classify electric signals spectra into predefined classes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fatemeh E. Nia whose telephone number is (469)295-9187. The examiner can normally be reached 9:00 am to 4:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kristina DeHerrera can be reached at (303) 297-4237. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FATEMEH ESFANDIARI NIA/Examiner, Art Unit 2855
1 Prior art of record