DETAILED ACTION
Non-Final Rejection
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 .
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 1-4, 7-17, 19-20, and 22-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims 1-12 falls within one of the four statutory categories. See MPEP § 2106.03. For example, each of claims 1-4, 7-15 fall within category of process; Each of claim 16-17, 19-20, and 22-24 falls within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863).
Regarding Claims 1-4, 7-15
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of mitigating or preventing equipment performance deficiencies.
The abstract idea is set forth or described by the following italicized limitations:
1. A method of mitigating or preventing equipment performance deficiencies, the method comprising:
determining values of one or more parameters associated with equipment by monitoring the one or more parameters over a time period in which the equipment is in use;
determining, by a computing system processing the values of the one or more parameters using a classification model, a performance classification of the equipment;
mapping, by the computing system, the performance classification to a mitigating or preventative action; and
generating, by the computing system, an output indicative of the mitigating or preventative action..
The italicized limitations above represent mental steps (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment) . Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “determining values of one or more parameters [..]; determining processing the values of the one or more parameters [..];mapping the performance classification [..]; generating an output [..] ” are mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “, by the computing system”. This element amounts to mere use of a generic computer components, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a plurality of generic control system with computer component with software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea.
.
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 1 contains additional elements that are, i.e. , computing system,”, generic devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d))The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
.
Dependent Claims 2-4, 7-15
Dependent claims 2-4, 7-15 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-4, 7-15 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For Examples, claims 2-3 and 11-15: claims limitations are mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2).
For Examples, claims 7-10 and 15(monitoring the one or more parameters includes receiving, by the computing system, sensor readings generated by the one or more temperature sensors): these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For Examples, claim 4: This element amounts to mere use of a generic computer components with high level of generality (apply it) , which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
Regarding Claims 16-17, 19-20, and 22-24
Claims16-17, 19-20, and 22-24 contains language similar to claims 1-4, 7-15 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims16-17, 19-20, and 22-24 are also rejected under 35 U.S.C. § 101(abstract idea).
Claim Rejections - 35 USC § 102
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.
Claim(s)1-4, 7-9, 11-12, 16-17, 19-20 and 23-24 is/are rejected under 35 U.S.C. 102(a)(1)as being anticipated by Pihlaja et al. (US 2008/0082294).
Regarding Claims 1 and 16. Pihlaja teaches a method of mitigating or preventing equipment performance deficiencies, the method comprising([0048]-[0049],[0076], figs. 2, 5 & 17; workstation 54: fig.2):
determining values of one or more parameters associated with equipment by monitoring the one or more parameters over a time period in which the equipment is in use([0056], [0057] and data collection:90: fig. 2; pressure signal: fig.3-5; parameters :104, 116: fig.5);
determining, by a computing system (54: fig.2) processing the values of the one or more parameters using a classification model, a performance classification of the equipment(94: fig. 2; output of even detector 108 : fig. 5; [0084]-[0125], [0137]);
mapping, by the computing system, the performance classification to a mitigating or preventative action(120: fig. 5;, the indicator generated by the abnormal situation detector could be provided to one or more of a control block or routine, to a maintenance system, etc. For instance, the output of the abnormal situation detector 120 could be provided to the controller 52 which could shut down the stirred vessel 60 or the reactor unit 56 if one or more abnormal situations are detected: [00137]-[01140]; [0162]-[0165] and fig. 17); and
generating, by the computing system, an output indicative of the mitigating or preventative action (output of the abnormal situation detector 120 could be provided to the controller 52 which could shut down the stirred vessel 60 or the reactor unit 56 if one or more abnormal situations are detected. Rules development applications and configuration screens that may be used to create rules for detecting abnormal situations and/or, if desired, for generating alarms, alerts, or for taking some other action based on the detected existence of abnormal situations. Similar or different rules development applications may be used as well to develop the rules 454: [0013]; [0167]; abnormal situations indicator: figs. 5, 17).
Regarding Claims 2, 3 and 17. Pihlaja further teaches the classification model is configured to output, for a given set of parameter values, one of a plurality of available classifications, the plurality of available classifications including (i) a classification indicating that mitigating or preventative actions are not recommended, and (ii) one or more other classifications indicating that mitigating or preventative actions are recommended([0084], [0137] and fig. 5);
and determining the performance classification includes outputting, by the classification model, one of the one or more other classifications.([0084], [0137] and fig. 5)
the one or more other classifications include a plurality of classifications that each correspond to a different diagnosis or prediction associated with deficient performance of the equipment([0084], [0137] and fig. 5).
Regarding Claims 4 and 19. Pihlaja further teaches the classification model includes (a)a support vector machine (SVM) model, (b) a decision tree model, or (c) a neural network([0136]).
Regarding Claim 7. Pihlaja further teaches monitoring the one or more parameters includes receiving, by the computing system, sensor readings generated by one or more sensor devices(82, 90: fig.2; [0056], [0057]).
Regarding Claim 8. Pihlaja further teaches the equipment includes the one or more sensor devices(82, 90: fig.2; [0056], [0057]).
.
Regarding Claim 9. Pihlaja further teaches the one or more sensor devices include one or both of (i) one or more temperature sensors, and (ii) one or more pressure sensors(82, 90: fig.2; [0056], [0057]).
.
Regarding Claims 11 and 23. Pihlaja further teaches mapping the performance classification to the mitigating or preventative action includes determining which action corresponds to the performance classification in a database containing known mitigating or preventative actions for known scenarios associated with the equipment([0163], [0165], [0166]).
Regarding Claims 12 and 24. Pihlaja further teaches generating the output indicative of the mitigating or preventative action includes presenting the output to a user via a display([0167]).
Regarding Claim 20. Pihlaja further teaches the equipment includes one or more sensor devices optionally including one or both of (i) one or more temperature sensors, and (ii) one or more pressure sensors(82, 90: fig.2; [0056], [0057]); and
monitoring the one or more parameters includes receiving sensor readings generated by the one or more sensor devices(82, 90: fig.2; [0056], [0057]).
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.
Claim(s) 10 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pihlaja in view of Hall et al. (US 20170234837)
Regarding Claims 10 and 22. Pihlaja silent about the sensor readings are generated by a plurality of sensor devices; and
determining the values of the one or more parameters includes generating the values by applying a dimension reduction technique to the sensor readings.
However, Hall teaches the sensor readings are generated by a plurality of sensor devices(fig. 12(b); [0111]); and
determining the values of the one or more parameters includes generating the values by applying a dimension reduction technique to the sensor readings(fig. 12(b); [0111]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Pihlaja, the sensor readings are generated by a plurality of sensor devices; and determining the values of the one or more parameters includes generating the values by applying a dimension reduction technique to the sensor readings, as taught by Hall, so as to find a projection that maximises the separation between classes.
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pihlaja in view of Ozeki(US 2020/0338677).
Regarding Claim 13. Pihlaja silent about prior to determining the values of the one or more parameters associated with the equipment:
training the classification model using (i) a plurality of sets of historical values of the one or more parameters and (ii) a plurality of respective labels.
However, Ozeki teaches prior to determining the values of the one or more parameters associated with the equipment: training the classification model using (i) a plurality of sets of historical values of the one or more parameters and (ii) a plurality of respective labels([0057]-[0060]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Pihlaja, prior to determining the values of the one or more parameters associated with the equipment: training the classification model using (i) a plurality of sets of historical values of the one or more parameters and (ii) a plurality of respective labels, as taught by Ozeki, so as to constructs a learned model.
Regarding Claim 14. Ozeki teaches further teaches after determining the performance classification of the equipment: receiving, by the computing system, a user-assigned label representing a manual classification for the values of the one or more parameters ([0057]-[0060]); and further training the classification model using (i) the values of the one or more parameters and (ii) the user-assigned label([0057]-[0060]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pihlaja in view of Kroyzer et al. (US 2016/0330225)
Regarding Claim 15. Pihlaja silent about the equipment includes a tank and one or more temperature sensors;
monitoring the one or more parameters includes receiving, by the computing system, sensor readings generated by the one or more temperature sensors;
the classification model is configured to output, for a given set of parameter values, one of a plurality of available classifications, the plurality of available classifications including (i) a classification indicating that mitigating or preventative actions are not recommended, and (ii) a plurality of other classifications that each correspond to a different diagnosis or prediction associated with deficient performance of the equipment;
the plurality of other classifications include one or more of (i) one or more classifications corresponding to temperature drop-out, (ii) one or more classifications corresponding to temperature oscillation, or (iii) one or more classifications corresponding to temperature overshoot; and
determining the performance classification includes the classification model outputting one of the plurality of other classifications.
However, Kroyzer teaches the equipment includes a tank and one or more temperature sensors(55,58,61,66, 250: fig. 4-5, [0074], fig. 6, [0045]);
monitoring the one or more parameters includes receiving, by the computing system, sensor readings generated by the one or more temperature sensors(55,58,61,66, 250: fig. 4-5, [0074], fig. 6, [0045]);
the classification model is configured to output, for a given set of parameter values, one of a plurality of available classifications, the plurality of available classifications including (i) a classification indicating that mitigating or preventative actions are not recommended, and (ii) a plurality of other classifications that each correspond to a different diagnosis or prediction associated with deficient performance of the equipment(55,58,61,66, 250: fig. 4-5, [0074], fig. 6, [0045]);
the plurality of other classifications include one or more of (i) one or more classifications corresponding to temperature drop-out, (ii) one or more classifications corresponding to temperature oscillation, or (iii) one or more classifications corresponding to temperature overshoot(55,58,61,66, 250: fig. 4-5, [0074], fig. 6, [0045]); and
determining the performance classification includes the classification model outputting one of the plurality of other classifications (55,58,61,66, 250: fig. 4-5, [0074], fig. 6, [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Pihlaja, the equipment includes a tank and one or more temperature sensors; monitoring the one or more parameters includes receiving, by the computing system, sensor readings generated by the one or more temperature sensors; the classification model is configured to output, for a given set of parameter values, one of a plurality of available classifications, the plurality of available classifications including (i) a classification indicating that mitigating or preventative actions are not recommended, and (ii) a plurality of other classifications that each correspond to a different diagnosis or prediction associated with deficient performance of the equipment; the plurality of other classifications include one or more of (i) one or more classifications corresponding to temperature drop-out, (ii) one or more classifications corresponding to temperature oscillation, or (iii) one or more classifications corresponding to temperature overshoot; and determining the performance classification includes the classification model outputting one of the plurality of other classifications, as taught by Kroyzer, so as to detect a intrusion or anomalies in industrial control systems and able to prevent an insider from manipulating the system to cause damage.
Examiner notes
Ozeki(US 2020/0338677) and Kroyzer et al. (US 2016/0330225) also teaches all the limitation of claims 1 and 16 as Ozeki teaches “a method of mitigating or preventing equipment performance deficiencies (see fig. 7), the method comprising:
a. determining at step S 11 values of one or more parameters associated with equipment by monitoring the one or more parameters over a time period in which the equipment is in use (see parameters in par. [0054], [0055] and fig. 1 and par. [0095] and fig. 7);
b. determining at steps S 12 and S 13, by a computing system 403 processing the values of the one or more parameters using a classification model, a performance classification of the equipment (see par. [0096] and fig. 7, outputs of network par. [0054] and fig. 2 and par. [0074], [0079], [0085] and [0089] and fig. 1 );
c. mapping at steps S14-S16, by the computing system, the performance classification to a mitigating or preventative action (see association between parameter ranges and actions, par. [0099]-[0101] and fig. 3-7); and
d. generating at step S17, by the computing system, an output indicative of the mitigating or preventative action (see par. [0076], [0082], [0087],[0091] and fig. 1 ).” And Kroyzer teaches “a method of mitigating or preventing equipment 412 performance deficiencies (see fig. 4),
the method comprising: a. determining values of one or more parameters associated with equipment 412 by monitoring by detector 18 the one or more parameters over a time period in which the equipment is in use (see par. [0061], [0066] and fig. 4 and 5);
b. determining, by a computing system 116 processing the values of the one or more parameters using a classification model, a performance classification of the equipment (see par. [0041] and fig. 2);
c. mapping, by the computing system, the performance classification to a mitigating or preventative action (see par. [0086]); and
d. generating, by the computing system, an output indicative of the mitigating or preventative action (see par. [0085]).”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Wang et al. (US 2016/0220225) disclose An arrhythmia detection device is provided, which includes: a monitoring probe attached to a subject to be examined; a monitoring unit coupled with the monitoring probe; a first displaying unit which displays ECG parameters obtained by the monitoring unit; an ultrasound probe attached onto a body surface of the subject; an ultrasound imaging unit coupled with the ultrasound probe; an arrhythmia triggering unit which triggers the ultrasound imaging unit to scan the heart of the subject when the monitoring unit detects an arrhythmia; and a second displaying unit which displays the images and/or the parameters of the heart obtained by the ultrasound imaging unit.
b) Nakase (US 2019/0362188) disclose enerating a first learning model by conducting machine learning using, as teacher data, a predetermined number of pieces of non-defective product data extracted from product data; determining, for each of a plurality of pieces of product data to be determined after the first learning model is generated, whether each product is non-defective or defective in accordance with the first learning model; grouping the pieces of product data determined to be defective, such that these pieces of product data are classified according to defect type; collectively associating type labels indicative of defect types with the defective product data according to defect type group; and generating a second learning model by conducting machine learning using, as teacher data, the defective product data with which the type labels are associated and the non-defective product data.
c) Aljalifa et al. (US 2024/0045414) disclose A method of diagnosing or predicting performance of equipment includes determining values of one or more parameters associated with the equipment by monitoring the one or more parameters over a time period in which the equipment is in use. The method also includes determining, by processing the values of the one or more parameters using a classification model, a performance classification of the equipment, mapping the performance classification to a mitigating or preventative action, and generating an output indicative of the mitigating or preventative action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMAD K ISLAM/Primary Examiner, Art Unit 2857