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.
DETAILED ACTION
The following NON-FINAL Office action is in response to Applicant’s request for continued examination filed on 11/11/2025.
Status of Claims
Claims 1,16,26,29,32 were newly amended and Claim 30 previously canceled by Applicant.
Claims 1-29 and 31-32 are currently pending and have been rejected as follows.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/11/2025 has been entered.
Priority
Examiner noted Applicants claiming Priority from Provisional 63132661 filled 12/31/2020.
IDS
The information disclosure statement filed on 01/07/2026 and 02/13/2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Response to Arguments / Amendments
Applicant’s 11/11/2025 amendment necessitated new grounds of rejection in this action.
Response to Applicant’s rebuttal arguments of the Double Patenting Rejection
The three nonstatutory double patenting rejections in the previous act, namely:
I. over respective Claims 1,6, and Claims 19,21 and Claims 22, 25 of copending App# 17566164;
II. over respective Claim 1, Claim 19 and Claim 24 of copending App. # 17566121;
III. over Claims 1,29 and Claim 34 of copending App# 17566187.
are withdrawn in view of the abandonment of these three copending applications.
Response to Applicant’s rebuttal argument(s) of the 112(a) rejection
The 112(a) rejection in the prior act is maintained because Original Specification ¶ [0034] 5th sentence merely provides support for “recommending specific automation, operator tools or modernization to reduce impact of the biggest contributors of operator variability on the industrial operation” not necessarily for “implementing one or more actions specified by the selected one or more mapped solutions, the one or more actions including at least one of a software-based or hardware-based change to modify an operation of the at least one control system wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” as amended and argued above with respect to each of independent Claims 1,26,29.
Response to Applicant’s rebuttal argument(s) of the 112(b) rejection
The 112(b) reactions in the prior act are withdrawn in view of Applicant amending the claims in the same and similar manner as proposed by Final Act 08/13/2025 p.19-p.20
Response to Applicant’s rebuttal argument(s) of the 101 rejection
Applicant’s 101 rebuttal argument was fully considered but unpersuasive. Specifically Remarks 11/11/2025 p.13 ¶2 argues independent Claim 1 and similarly Claims 26,29 update software or hardware to change operation of a control system to compensate for at least a portion of one or more gaps in operation of the industrial operation, which does not recite abstract idea.
i. First, it is noted Original Specification ¶ [0034] 5th sentence merely recites “recommending specific automation, operator tools or modernization to reduce impact of the biggest contributors of operator variability on the industrial operation” not “implementing one or more actions specified by the selected one or more mapped solutions, the one or more actions including at least one of a software-based or hardware-based change to modify an operation of the at least one control system wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” as amended at Claims 1,26,29.
ii. Second, even assuming arguendo that the Original Specification would provide clear, deliberate and sufficient support for such limitation at independent Claims 1,26,29, the Examiner submits that, under the broadest reasonable interpretation test of MPEP 2111, the actual claimed language of said “implementing” limitation merely calls for “implementing one or more actions” among which “a software-based or hardware-based change” are merely two of the many actions [plural] as broadly covered by expression “one or more actions”. There is no requirement at said “implementing” limitation in said independent Claims 1,26,29 for “a software-based or hardware-based change” to be the action, among the plurality of covered “actions” to be implemented, and thus no required recitations of elements other than what has already been identified as abstract.
iii. Third, and equally important, even assuming in arguendo, without conceding, just for the sake of argument, that the “implementing” would require “at least one of a software-based or hardware-based change” [as an action] “to modify an operation of the at least one control system”, this would represent a mere automation as an example of a technological environment upon which the abstract mitigative processes of reducing operator variability or gaps are being performed. Yet, MPEP 2106.04(a)(2) III C #2 is clear that use of a computer or technological environment to aid or “compensate” for human gaps does not preclude the claims to recite, describe or set forth the abstract idea. In a similar vein, MPEP 2106.04(a)(2) III C #3 states that use of a computer as a tool to aid or “compensate” for human gaps also does not preclude the claims to recite, describe or set forth the abstract exception. Even when subsequently testing such level of automation, for software or hardware change under MPEP 2106.05(h), the Examiner discovers that narrowing the aforementioned mitigative abstract processes to a field of use or technological environment does not integrate the abstract exception into a practical application. More to the point, since there are no technological details in the claims for the “at least one of a software-based or hardware- based change to modify an operation of the at least one control system”, let alone in the Original Disclosure, the Examiner submits that under MPEP 2106.05(f)(3) such argued limitation represents a mere generality of the application of the abstract exception which again does not integrate said abstract exception into a practical application or provide significantly more than what was already found as abstract.
In conclusion, the Examiner submits that the Applicant’s rebuttal argument on the 101 rejection is unpersuasive because one or more of the following applies: i. the Original Disclosure does not support the amended and argued features, ii. a proper claim interpretation, under MPEP 2111, shows the argued claims1,26,29 do not necessarily require implementation of “software-based or hardware-based change” as the selected action and iii. there is a preponderance of legal evidence, for the argued software or hardware based change not to preclude the claims to recite, describe or set forth the abstract exception, or to integrate it into a practical application or provide significantly more than what was already identified as abstract. Thus, the claims remain ineligible.
Response to Applicant’s rebuttal arguments on the prior art rejection
Remarks 11/11/2025 p15 ¶2-p16¶1 argues Paradies US 6463441 B1 does not teach
* “analyzing the respective clusters to identify human operator variability for each of the distinct regimes of operation”.
* “the operator variability indicates different gaps in economic operation of the same industrial operation by different ones of the plurality of operators”.
The prior art argument was considered but is moot in view of new grounds of rejection because, the Examiner now relies on Asendorf et al US 20190347597 A1 to teach:
* “analyzing the respective clusters to identify human operator variability for each of the distinct regimes of operation”
(Asendorf ¶ [0029] 5th-8th sentences: [worker safety management system] SMS 6 utilize the environmental data to aid generating alerts or other instructions for PPE 13 and performing predictive analytics, such as determining any correlations between certain environmental conditions (e.g., heat, humidity, visibility) with abnormal worker behavior or increased safety events. [worker safety management system] SMS 6 utilize the environmental data to aid generating alerts or other instructions for PPE 13 and for performing predictive analytics, such as determining any correlations between certain environmental conditions (e.g., heat, humidity, visibility) with abnormal worker behavior or increased safety events. Example environmental conditions that may be sensed by sensing stations 21 include but are not limited to temperature, humidity, presence of various gasses, pressure, visibility, wind, ambient light, ambient noise, radiation, air quality, and the like. In other words, sensing stations 21 may include temperature sensors, moisture and/or humidity sensors, gas sensors, pressure sensors, light sensors, audio sensors, radiation sensors, and so forth. In some examples, sensing stations 21 may include camera configured to monitor movements of workers 10 while workers 10 are operating in the respective work environments 8.
Asendorf ¶ [0046] 3rd sentence: dashboards provide various insights regarding system 2, such as baseline (normal) operation across worker populations, identifications of any anomalous workers engaging in abnormal activities that expose the worker to risks, identifications of any geographic regions within work environments 8 for which unusually anomalous (e.g. high) safety events have been or are predicted to occur, identifications of any of work environments 8 exhibiting anomalous occurrences of safety events relative to other environments etc).
Asendorf ¶ [0056] 6th sentence: SMS 6 [worker safety management system] determine a safety metric for each worker in a cluster of workers 10, for each work environment in a cluster of work environments 8, or both. ¶ [0079] 3rd-4th sentences: CAS [cluster and analysis service] 68F transform work environment data using techniques such as one-hot encoding (e.g. when work environment data includes categorical variables), natural language processing techniques (when work environment data includes text data), data normalization (e.g., making all data zero mean and unit variance), among others.
* “the operator variability indicates different gaps in economic operation of the same industrial operation by different ones of the plurality of operators”.
(Asendorf ¶ [0029] 5th-8th sentences above and ¶ [0101] 2nd sentence: compare the single worker 10 to other workers within the SAME work environment 8, (i.e. humidity, temperature, pressure, etc. per ¶ [0029] 5th-8th sentences) to other workers across the workers 10 company, and/or across multiple companies. ¶ [0046] 3rd sentence: dashboards provide insights regarding system 2, such as baseline (normal) operation across worker populations, identifications of any anomalous workers engaging in abnormal activities that may potentially expose the worker to risks, identifications of any geographic regions within work environments 8 for which unusually anomalous (e.g. high) safety events have been or are predicted to occur, identifications of any of work environments 8 exhibiting anomalous occurrences of safety events relative to other environments, etc. Specifically, per ¶ [0056] 2nd-4th sentences: [worker safety management system] SMS 6 determine a difference [or gap] between performance by a target entity with respect to safety events and performance of the cluster that includes the target entity with respect to safety events. In other words, [worker safety management system] SMS 6 determine differences in safety performance of a target entity relative to a cluster of similar entities. In some examples, [worker safety management system] SMS 6 determine difference between performance by the target entity relative to the performance of the cluster by determining the performance for each entity in a cluster of entities that includes the target entity.
Asendorf ¶ [0061] 3rd sentence: [worker safety management system] SMS 6 output a rank, with respect to the difference, of the target entity against the cluster of entities that includes the entity, an absolute difference [or gap], a relative difference [or gap], a visualization, or other indication of the difference [or gap]. Similarly, ¶ [0086], ¶ [0098] 5th, 7th sentences: CAS 68F determine whether a difference [or gap] between performance of a target entity relative to performance of the cluster of entities that include the target entity satisfies a threshold difference (e.g., one standard deviation [or gap]). Similarly, CAS 68F may determine whether a difference between efficiency of target worker 10A relative to efficiency of the cluster of workers including target worker 10A is greater than a threshold efficiency. ¶ [0124] 4th-5th sentences: indicates the performance of a target entity with respect to safety and indicates the performance (e.g., average performance) of a cluster of entities that includes the target entity, such that the graphical further indicates the difference [or gap] between the performance of the target entity and the performance of the cluster of similar entities. For example, the graphical user interface may indicate the number of safety events experienced by each respective entity in the cluster (e.g., including the target entity) and an average number of safety events experienced by the entities in the cluster).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-29 and 31-32 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1,26, 29 are independent and have been amended to each recite:
- “implementing one or more actions specified by the selected one or more mapped solutions, the one or more actions including at least one of a software-based or hardware-based change to modify an operation of the at least one control system wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps”
- Original Specification ¶ [0034] 5th sentence merely provides support for “recommending specific automation, operator tools or modernization to reduce impact of the biggest contributors of operator variability on the industrial operation” not necessarily for
- “implementing one or more actions specified by the selected one or more mapped solutions, the one or more actions including at least one of a software-based or hardware-based change to modify an operation of the at least one control system wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” as amended and argued above with respect to each of independent Claims 1,26,29. Thus the Specification does not show clear, deliberate and sufficient support to show Applicant had possession for the newly added matter at the “implementing” limitation. Examiner reminds: “One shows possession of the invention by describing the invention, with all its claimed limitations, not that which makes it obvious”. Lockwood v Am Airlines Inc 107 F.3d 1565,41 USPQ2d 1961 Fed Cir 1997
Claims 2-25 are dependent and rejected upon rejected parent independent Claim 1.
Claims 27,28 are dependent and rejected upon rejected parent independent Claim 26.
Claims 31,32 are dependent and rejected upon rejected parent independent Claim 29.
Examiner recommends that Applicant amend said claims based on support in the Original Specification without leaning. Clarification and/or correction is/are required.
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 1-29 and 31-32 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 pre-AIA the applicant regards as the invention.
Claims 1,26,29 are independent and have been amended to each recite, among others:
- “analyzing the respective clusters to identify human operator variability for each of the distinct regimes of operation in managing the industrial operation, wherein the operator variability indicates different gaps in economic operation of the same industrial operation by different ones of the plurality of operators”, rendering said claims vague and indefinite because it is unclear if subsequently recited the same industrial operation relates back to an industrial operation antecedently recited at preamble of each of said claims and then throughout the claims body.
Claims 1,26,29 are recommended to be amended to each recite, among others:
- “analyzing the respective clusters to identify human operator variability for each of the distinct regimes of operation in managing the industrial operation, wherein the operator variability indicates different gaps in economic operation of the industrial operation by different ones of the plurality of operators”,
Claims 2-25 are dependent and rejected upon rejected parent independent Claim 1.
Claims 27,28 are dependent and rejected upon rejected parent independent Claim 26.
Claims 31,32 are dependent and rejected upon rejected parent independent Claim 29.
Clarifications and/or corrections are required.
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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 1-29,31,32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. Examiner points to MPEP 2106.04(a) last ¶: …examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible, if a claim limitation(s) is determined to fall within multiple groupings… Here, Claims 1,26,29 summarize the invention at their preamble as: “improving productivity of an industrial operation based on operator variation analysis for a plurality of operators of the industrial operation, each of the operators of the plurality of operators corresponding to a human who interacts with at least one control system associated with the industrial operation”. Yet, this “improvement” corresponds to a claim drafting attempt at improving abstract and fundamental managerial or assessment practices in “industrial operation” clarified at independent Claims 1,26,29 as “different measured or quantified gaps for addressing a root cause of the respective different measured or quantified gaps”, (independent Claims 1,26,29), then clarified as “wherein selecting the one or more mapped solutions includes analyzing the one or more gaps to determine if relevant characteristics associated with the one or more gaps justify the selected one or more mapped solutions for addressing the one or more gaps for the industrial operation” (dependent Claims 15,27,31) “wherein the one or more actions are taken upon determining that the relevant characteristics associated with the one or more gaps justify the selected one or more mapped solutions” (dependent Claims 16,32 and similarly at dependent Claim 28), including “communicating information relating to the selected one or more mapped solutions”, (dependent Claim 17) “wherein the information includes predicted economic benefits by implementing the selected one or more mapped solutions” (dependent Claim 18). Each and all of these correspond to building blocks of abstract and entrepreneurial operation management, tested in light of MPEP 2106.04(a)(2) II A ¶2, as read in light of Original Specification ¶ [0002],
¶ [0004], ¶ [0006] and found to fall within the broad abstract grouping of “Certain Methods of Organizing Human Activities” tested in light of MPEP 2106.04(a)(2) II. For example, the fundamental practice or principle of “measuring or quantifying one or more gaps” or risks [MPEP 2106.04(a)(2) II A, ¶1] “due” “to operator variability” (Claims 1,26,29), in the “economic operation of the same industrial operation by different ones of the plurality of operators” (Claims 1,26,29) with respect to abstract, fundamental practices or principles [MPEP 2106.04(a)(2) A] of “productivity” of work, which are set forth with respect to the “industrial operation” (Claims 1,26,29) as read in light of Original Specification ¶ [0004] 4th sentence.
Also here, the fundamental practice or principle of “measuring or quantifying one or more gaps” or risks [MPEP 2106.04(a)(2) II A, ¶1] “due” [here] “to operator variability” (independent Claims 1,26,29), in an “industrial operation” (independent Claims 1,26,29) relate to equally abstract “economic operation” (independent Claims 1,26,29) and “predicted economic benefits” (dependent Claim 18), akin to profitability as read in light of Original Specification ¶ [0004] 4th sentence, ¶ [0020] 2nd sentence, ¶ [0090] 2nd-3rd sentence, ¶ [0091] last sentence. Such fundamental practices or principles also provide risk-benefit analysis or justif[ication] (dependent Claims 15,16,27,28,31,32) including risk eliminat[ion], remov[al], compensat[ion] or mitigation actions on “multiple non-human root factors”; “compensate[…] for at least a portion of the one or more gaps” (independent Claims 1,26,29) by considering “input data” “using the respective one or more types of data selected” and possible operat[ion] “of the at least one control system” (independent Claims 1,26,29), assuming its solution or action is the one actually implement[ed] among the “one or more actions specified by the selected one or more mapped solutions” (independent Claims 1,26,29). Yet, MPEP 2106.04(a)(2) II C it clear: considering historical usage information [akin here to “downtime” (dependent Claims 7,20) and “non-steady state” (dependent Claim 20), “steady state” “that does not change or changes only negligibly over a particular period of time” (independent Claims 1,26,29), “time series data of event data in the steady state process data” (dependent Claim 6), “time series” (dependent Claim 21), “timestamps” (dependent Claim 22)] while inputting data1 and automation by providing information to a person without interfering with the person’s primary activity2 [akin here to compensate[…] for the operator’s gaps] set forth the abstract idea. MPEP 2106.04(a)(2) II ¶6, 4th sentence is also clear that certain activity with a computer may still fall within the abstract exception. It would then follow that here, general recitation of “each of the operators of the plurality of operators corresponding to a human who interacts with at least one control system associated with the industrial operation” at the preamble of each of independent Claims 1,26,29, and general recitation of automation as “wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” (independent Claims 1,26,29) would also not preclude the claims from reciting, describing or setting forth the abstract idea.
Also, such fundamental practices or principles can also be viewed as result of equally abstract mathematical processes [MPEP 2106.04(a)(2) I] described or set forth by “clustering” (independent Claims 1,26,29), “best stationary data clustering techniques” (dependent Claims 11,12), “autoregressive integrated moving average (ARIMA) model” (dependent Claim 13), “bounds of each steady state cluster” (dependent Claim 14), “statistically significant” “data” and “correlations between one or more metrics” (dependent Claim 23), “outlier detection” and “rules” (dependent Claim 10), “time series” (dependent Claim 6) used in equally abstract mental processes grouping, through computer aided observation, evaluation, judgment and notification [MPEP 2106.04(a)(2) III ¶2, 2106.04(a)(2) III C #1,#2,#3] and comparable to the combination of collecting information, analyzing it and displaying certain results of the collection and analysis, as in Electric Power Group v. Alstom, S.A., 830 F.3d 1350,1353-54,119 USPQ2d 1739,1741-42 (Fed Cir 2016) cited by MPEP 2106.04(a)(2) III. A.
Here, the notification of certain results of the evaluation, judgment or analysis is set forth by “communicating information relating to the selected one or more mapped solutions” (dependent Claim 17), “communicated via a report, text, email or audibly” (dependent Claim 19), while the preceding observation is set forth as an identif[ication] of “steady state process data”, (independent Claims 1,26, 29), “non-steady state process data and downtime data in addition to the steady state process data” (dependent Claim 20), “distinct products or distinct regimes of operation”, ”operator variability” (independent Claims 1,26,29), “the distinct products correspond to products produced by the industrial operation” (dependent Claim 2), “the distinct regimes of operation occur due to physical differences in the industrial operation” (dependent Claim 3) “downtime data” (dependent Claim 7), “abnormal periods of operation” (dependent Claim 8), “points with high prediction error” (dependent Claim 13).
Such notification follows from the equally abstract evaluation for judgment, set forth here by a risk-benefit analysis or justif[ication], which, as identified above, include risk eliminat[ion], remov[al], compensat[ion] or mitigation actions to eliminate multiple non-human root differences”; “compensate for at least a portion of the one or more gaps” (independent Claims 1,26,29), while considering “input data” “using the respective one or more types of data selected” followed by a judgment in “operation” “of at least one control system”, assuming its corresponding action or solution is the one that was actually selected for implementation (independent Claims 1,26,29).
Also here, said evaluation and judgment are achieved through equally abstract mathematical relationships expressed in words [MPEP 2106.04(a) I], comparable and, not meaningfully different, than organizing of information and manipulating of such information by mathematical correlations similar to the legal findings of Digitech Image Techs, LLC v. Electronics for Imaging, Inc., 758 F.3d 1344,1350,111 USPQ2d 1717,1721,Fed Cir 2014 as cited and articulated by MPEP 2106.04(a)(2) I A. Specifically, in Digitech, the patentee claimed generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The Court explained that such claims were directed to an abstract idea because they described organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula in catalytic chemical conversion of hydrocarbons, in the petrochemical and oil-refining fields. 758 F.3d at 1350,111 USPQ2d at 1721. Similarly, MPEP 2106.04 I ¶3 cites Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971, and Flook, 437 U.S. at 589-90, 198 USPQ at 197 to argue that narrow laws that may have limited applications are still patent ineligible.
Here, narrowing the abstract exception of “managing the industrial operation” to limited applications of “the identified distinct products or distinct regimes of operation”; (independent Claims 1,26,29), that “occur due to physical differences in the industrial operation” (dependent Claim 3), “due to non-human root causes” (dependent Claim 4), includ[ing] “equipment, process, or ambient causes” (dependent Claim 5), with further consideration for “downtime data from the steady state process data” (dependent Claim 7), “abnormal periods of operation from the steady state process data” (dependent Claim 8); “abnormal periods of operation correspond to periods of significantly reduced production rates or periods in which the product or products produced are of off specification quality” (dependent Claim 9), “non-steady state” (dependent Claim 20) and “steady state” “that does not change or changes only negligibly over a particular period of time”, “operator variation analysis”, (independent Claims 1,26,29), would similarly be patent ineligible when tested per MPEP 2106.04 I ¶3 above.
Also, as stated by MPEP 2106.04(a)(2) III. C: #1. Performing a mental process on generic computer, #2. Performing mental process in a computer environment, #3. Using a computer as a tool to perform a mental process do not preclude the claims to recite, describe or set forth the abstract exception. MPEP 2106.04(a)(2) II similarly states that certain activity between a person and computer may still fall within the abstract grouping of organizing of human activity.
It would then follow that here, recitations of “the information is communicated via a report, text, email or audibly” at dependent Claim 19, even if interpreted as computer operated, would not prelude the claim from describing or setting forth the abstract idea.
It would also follow that here, similar recitations of a computer environment (#2) or tool (#3) in “at least one of a software-based or hardware- based change to modify an operation of the at least one control system” [to] “compensate […] for at least a portion of the one or more gaps” at Claims 1,26,29, assuming its action or solution is the one actually selected among the recited “one or more actions”, would similarly not preclude the claims from reciting, describing or setting forth the abstract exception. In an abundance of caution, the degree of automation or involvement of computer elements, in the claimed language, will be more granularly investigated below. For now, it is clear, that given the preponderance of legal evidence above, the character as a whole of the claims remains undeniably abstract as directed to managing the industrial operation through implementation of selected solutions’ actions to address gaps associated with human performance or operator variability, with any purported improvement being entrepreneurial and abstract rather than technological. Step 2A prong one.
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the additional, computer-based elements are/is found, per MPEP 2106.05(f)(2), to merely apply the above abstract idea. Here, the additional elements are “at least one” “memory device” and “processor” “configured to” execute the abstract functions of Claims 26-28, and possibly newly amended computerized implementation of “at least one of a software-based or hardware-based change to modify an operation of the at least one control system” to “compensate […] for at least a portion of the one or more gaps” at newly amended independent Claims 1,26,29, assuming said action is the one actually selected to be implemented among the “one or more actions specified by the selected one or more mapped solutions” at newly amended independent Claims 1,26,29. Specifically, here, the additional computer based elements or machinery, when tested per MPEP 2106.05(f)(2) represent mere application of computer components with computerized capabilities to perform economic tasks [here identified above] and other tasks to receive [here “input”], store [here “record”] and transmit [here “communicating”] data3 to apply the aforementioned business method [here identified above], through mathematical algorithms4 [here “clustering” at Claims 1,26,29, “ARIMA model”5 at dependent Claim 13] on general-purpose computer [here “memory” and “processor” at Claim 26]. Additional, computer-based elements are “sensor devices or sensing systems” (dependent Claims 24,25), “to measure output(s) of the at least one piece of industrial equipment” (dependent Claim 24), and “to visually or audibly monitor the operators” (dependent Claim 25), which when tested per MPEP 2106.05(f)(2) represent computerized or machinery functions to monitor audit log data executed on a general-purpose computer6. Here, this is reflected by recitation of “wherein at least one of the sensor devices or sensing systems is coupled to at least one piece of industrial equipment associated with the industrial operation and configured to measure output(s) of the at least one piece of industrial equipment” at dependent Claim 24. The same “apply it” rationale pertains to requiring use of software to tailor information [here “solution” at Claims 1,15-18,26-29,31,32] and provide it to user on a computer7. All these functions, even if assumed as computerized, do not integrate the abstract idea into a practical application. see MPEP 2106.05(f). In fact, MPEP 2106.05(f)(2) cites “Fairwarning Ip, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 U.S.P.Q.2d 1293 (Fed. Cir. 2016), where FairWarning contend[ed] that its system allowed for compilation and combination of disparate information sources and that the patented method made it possible to generate a full picture of user's activity, identity, frequency of activity, and the like in a computer environment. Yet, the Federal Circuit responded: “the mere combination of data sources, however, does not make the claims patent eligible. As we have explained, "merely selecting information, by content or source, for collection, analysis, and [announcement] does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas." Elec. Power, 830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318, at *4. It would then similarly follow that here, the capabilities of the “memory” and “processor” to “process input data received from one or more data sources” would similarly represent mere applications of the abstract concepts by computer related elements, which would similarly not integrate the abstract idea into a practical application. Step 2A Prong two.
Also, when tested, per MPEP 2106.05(h), the narrowing of the abstract idea [here identified above] to a technological environment or field of use [i.e. “industrial equipment” at dependent Claim 24] does not integrate the abstract idea into a practical application. For example, according to MPEP 2106.05(h) iv., merely specifying that the abstract idea of monitoring audit log data relates to transactions or activities that executed in a computer environment represents a narrowing of the abstract idea to a field of use or technological environment. It will then follow that an analogous monitoring process represented by “sensor devices or sensing systems associated with the industrial operation” (dependent Claim 23) “coupled to at least one piece of industrial equipment associated with the industrial operation and configured to measure output(s) of the at least one piece of industrial equipment” (dependent Claim 24) “to visually or audibly monitor the operators” (dependent Claim 25) would similarly represent a narrowing of the abstract exception to a field of use or technological environment, when analogously tested per MPEP 2106.05(h). Further, MPEP 2106.05(h)(vi) also cites “Elec Power Grp, LLC v Alstom, SA, 830 F3d 1350,119 USPQ.2d 1739 Fed Cir 2016, Court Opinion 08/01/2016” to state that limiting the abstract combination of collecting information analyzing it and displaying certain results of the collection and analysis to data related to the to a particular technological environment, also does not integrate the abstract idea into a practical application.
Here, the collecting information is reflected by “input[ted]” “data” and associated “state(s)” and “regimes of operation” at Claims 1,3,6-8,10,11,20,26,29,30, and “time series or alarm event data collected from at least one industrial process associated with the industrial operation” at dependent Claim 21, “data is received in digital form and includes one or more timestamps” at dependent Claim 22, “data is received from one or more sensor devices or sensing systems associated with the industrial operation” at dependent Claim 23 etc..
Also, here, the analyzing of information is reflected in the “correlation, clustering analyzing, outlier detection” recitations. Also here, the certain results of the collection and analysis is reflected by the preponderantly recited “solutions”.
It would then follow that here, the narrowing of such collecting analysis and display to a particular technological environment is reflected here by recitations of “human who interacts with at least one control system associated with the industrial operation” at preamble of independent Claims 1,26,29, and “equipment” at dependent Claims 5,24 and by “the input data is received in digital form” at dependent Claim 22. It would also follow that here the narrowing of the combination of data collection, analysis, and display to certain results of the collection and analysis to data related to a generally recited “industrial operation” (Claims 1-4,9,15,16,21,23,24,26-32), “including at least one of a software-based or hardware- based change to modify an operation of the at least one control system, wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” (independent Claims 1,26, 29), as well as, “data received in digital form and includes one or more timestamps” (dependent Claim 22), “sensor devices or sensing systems is coupled to at least one piece of industrial equipment associated with industrial operation” “to measure output(s) of the at least one piece of industrial equipment” (dependent Claim 24) “sensor devices or sensing systems configured to visually and/or audibly monitor operators” (dependent Claim 25), would similarly not integrate the abstract idea into a practical application. Based on the MPEP 2106.05(h)(vi) test above, such narrowing, does not integrate the abstract idea into a practical application. Indeed, looking closer at “Elec Power Grp” supra, the Examiner discovers that even requiring monitoring technical parameters on computerized SCADA, [supervisory control and data acquisition], deriving a composite indicator of reliability from a combination of real time data streams measurements and dynamic stability metrics to display their result as concurrent visualization of two or more information as humanly comprehensible amount of information useful for users, did not save the claims from ineligibility. “Elec Power Grp., LLC v. Alstom, SA, 830 F3d 1350, 119 USPQ.2d 1739 Fed Cir 2016, Court Opinion 08/01/2016”)”. It then logically follows that here, the narrowing the claims to “sensing devices or systems” (dependent Claims 23-25) as read in light of Original Specification ¶ [0013] 2nd sentence, would similarly represent computerized functions that organize, track and display narrower results of the data, which would equally not render the claims patent eligible.
As per recitation of “wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” (independent Claims 1,26,29), the Examiner submits that even if such change or modif[ying] would be the one select[ed] to be implement[ed] among the “one or more actions”, it would still not render the claims eligible. This is because, when more granularly tested per MPEP 2106.05(a), such limitation is representative of mere automation of manual processes8 including accelerating a process of analyzing audit log data when the increased speed comes solely from capabilities of a general-purpose computer9, which according to MPEP 2106.05(a) do not provide technological improvements. Rather, as stated by MPEP 2106.05(f)(2) (iii), a process for monitoring audit log data executed on a general-purpose computer where the increased speed in the process comes from the capabilities of the general-purpose computer10, represents mere invocation of computers or machinery as a tool, which do not integrate the abstract idea into a practical application. The same principles apply to the capabilities of the additional computer-based elements, as tested per MPEP 2106.05(f)(2) v to tailor information and provide it to user on a generic computer11.
Similarly, MPEP 2106.05(f)(3) finds that the generality of application of the judicial exception, is an example of applying the abstract idea which would also not integrate said abstract idea into a practical application. Here, given the high generality at the “wherein”12 limitation of
“wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” (independent Claims 1,19,24), the Examiner reasons that said limitation could equally be construed as a case of general application of abstract idea [MPEP 2106.05(f)(3)] to a “control system”, in a manner analogous to, narrowing the abstract idea to a field of use or technological environment as in MPEP 2106.05(h) above, without providing the technological details of an actual technological solution as required by MPEP 2106.05(f)(1), and for these reasons, not integrating said abstract exception into a practical application.
Therefore here, no matter which of MPEP 2106.05(f)(1),(f)(2),(f)(3) and/or (h) test is employed, it is clear that the additional computer-based elements or machinery do not integrate the abstract idea into a practical application because they would mere represent an invocation of machinery to apply an abstract processes or existing process, [MPEP 2106.05(f)] and/or narrowing the abstract process to a technological environment, [MPEP 2106.05(h)].
Thus, the Examiner submits that there is a preponderance of legal evidence that no additional, computer-based elements integrate the abstract exception into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea and/or narrow it to a field of use or technological environment. Specifically, Examiner follows MPEP 2106.05 (d) II guidelines and carries over the findings tested per MPEP 2106.05 (f) and/or (h) to submit that the additional computer-based elements also do not provide significantly more. For these reasons, said computer-based additional elements also do not provide significantly more than the abstract idea itself, as a sufficient option for evidence without having to rely on the conventionality test. Yet even assuming arguendo, that the separate conventionality test would be applied, and further evidence would still be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would further point to MPEP 2106.05(d) as follows:
MPEP 2106.05(d) I. 2.a: Examiner points to the following citations in the Applicant’s own Original Disclosure with respect to the recited additional computer-based elements:
* Original Specification reciting at high level of generality ¶ [0013] “the one or more data sources from which the input data is received may include one or more sensor devices or sensing systems. In accordance with some embodiments of this disclosure, at least one of the sensor devices or sensing systems (e.g., a distributed control system (DCS), a supervisory control and data acquisition (SCADA) system, etc.) is coupled to industrial equipment associated with the industrial operation”. This SCADA finding is relevant when considered in light of “Elec Power Grp, LLC v Alstom, SA, 830 F3d 1350,119 USPQ.2d 1739 Fed Cir 2016, Court Opinion 08/01/2016” to corroborate that the claims requiring monitoring technical parameters on computerized SCADA, [supervisory control and data acquisition], deriving a composite indicator of reliability from a combination of real time data streams measurements and dynamic stability metrics to display their result as concurrent visualization of two or more information as humanly comprehensible amount of information useful for users, did not save the claims from ineligibility. “Elec Power Grp., LLC v. Alstom, SA, 830 F3d 1350, 119 USPQ.2d 1739 Fed Cir 2016, Court Opinion 08/01/2016”.
* Original Specification ¶ [0021] 2nd-3rd sentences “The determined one or more best stationary data clustering techniques may include, for example, one or more of: BIRCH, Spectral Clustering, K-Means, Gaussian Mixture, and Affinity Propagation in some instances. It is understood that many other data clustering techniques may be applied, as will be apparent to one of ordinary skill in the art”.
* Original Specification ¶ [0025]-¶ [0026] reciting at high level “The one or more systems or devices on which the above method (and/or other systems and methods disclosed herein) is implemented may include at least one processor and at least one memory device. As used herein, the term “processor” is used to describe an electronic circuit that performs a function, an operation, or a sequence of operations. The function, operation, or sequence of operations can be hard coded into the electronic circuit or soft coded by way of instructions held in a memory device. A processor can perform the function, operation, or sequence of operations using digital values or using analog signals. In some embodiments, the processor can be embodied, for example, in a specially programmed microprocessor, a digital signal processor (DSP), or an application specific integrated circuit (ASIC), which can be an analog ASIC or a digital ASIC. Additionally, in some embodiments the processor can be embodied in configurable hardware such as field programmable gate arrays (FPGAs) or programmable logic arrays (PLAs). In some embodiments, the processor can also be embodied in a microprocessor with associated program memory. Furthermore, in some embodiments the processor can be embodied in a discrete electronic circuit, which can be an analog circuit, a digital circuit or a combination of an analog circuit and a digital circuit. The processor may be coupled to at least one memory device, with the processor and the at least one memory device configured to implement the above-discussed method. The at least one memory device may include a local memory device (e.g., EEPROM) and/or a remote memory device (e.g., cloud-based storage), for example”.
* Original Specification ¶ [0066] 3rd sentence: “…many routine program elements, such as initialization of loops and variables and the use of temporary variables are not shown”
* Original Specification ¶ [0136] “Having described preferred embodiments, which serve to illustrate various concepts, structures and techniques that are the subject of this patent, it will now become apparent to those of ordinary skill in the art that other embodiments incorporating these concepts, structures and techniques may be used. Additionally, elements of different embodiments described herein may be combined to form other embodiments not specifically set forth above”.
Even if assumed as computer implemented, the conventionality of the “integrated moving average (ARIMA) model” at Claim 13 would be further corroborated by at least the following:
* US 20190325328 A1 entitled Detection and use of anomalies in an industrial environment reciting at ¶ [0053] 1st sentence: “one that may use similar conventional time-series predictive algorithms such as Autoregressive Integrated Moving Average (ARIMA)”
* US 5735546 A entitled Method For Controlling A Manufacturing Process Utilizing Control Charts reciting at column 2 lines 55-59: Step 3: “Using the previous points on the chart to forecast the next expected point. For example, conventional auto-regression or moving averages” [ARIMA] “techniques can accomplish this prediction, including confidence intervals for the prediction”.
* US 20160171422 A1 ¶ [0017] 2nd sentence: “WFMS 115 uses generic forecasting techniques such as moving averages or autoregressive integrated moving average (ARIMA) models to predict, for any future day, the number of contacts the center can expect”.
* US 10802849 B1 column 4 lines 4-7: “Conventionally, Autoregressive Integrated Moving Average (ARIMA) has been widely used to perform forecasting using univariate time series data”.
- Additionally, if necessary –
MPEP 2106.05(d)(II) the following computer functions are listed as well‐understood, routine, and conventional functions performed by additional computer components:
* gather statistics13 / arranging a hierarchy of groups, sorting information14. These are reflected here by “processor” capabilities at claim 26 to “selecting to be clustered one or more types of data in the steady state process data” “for each of the identified distinct products or distinct regimes of operation” and “clustering the input data into corresponding clusters using the respective one or more types of data for each of the identified distinct products or distinct regimes of operation using data clustering techniques”;
* electronic recordkeeping15; here, assuming to be computer implemented, this would be reflected by recitations of: “the distinct regimes of operation are recorded in time series data of event data in the steady state process data” at dependent Claim 6.
* performing repetitive calculations16 and eliminating less restrictive information17. Here, assuming these would be computer implemented, they would be reflected by recitations of “clustering the one or more types of data for each of the identified distinct products or distinct regimes of operation using the determined best stationary data clustering techniques” (dependent Claim 11); “wherein gross clusters are created for the type(s) of data using the determined one or more best stationary data clustering techniques” (dependent Claim 12) “building an autoregressive integrated moving average (ARIMA) model for each steady state cluster associated with the clustered one or more types of data “and” “identifying points with high prediction error in the ARIMA model” (dependent Claim 13) “wherein the identified points are used to confirm bounds of each steady state cluster” (dependent Claim 14).
In conclusion, Claims 1-29, 31, and 32 although directed to statutory categories (“methods” or processes and “system” or machine) they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Thus, the Claims 1-29, 31, and 32 are not patent eligible.
Rejections under 35 § U.S.C. 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 of this title, 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.
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.
The factual inquiries 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.
Claims 1-10,15-22,26-29,31,32 are rejected under 35 U.S.C. 103 as being unpatentable over:
Mark W. Paradies US 6463441 B1 hereinafter Paradies, in view of
Pack; Robert US 20230053175 A1 hereinafter Pack, and in further view of
Asendorf et al US 20190347597 A1 hereinafter Asendorf. As per,
Claims 1,26,29 Paradies teaches or suggests “A method for improving productivity of an industrial operation based on operator variation analysis for a plurality of operators of the industrial operation, each of the operators of the plurality of operators corresponding to a human who interacts with at least one control system associated with the industrial operation, the method comprising” (independent Claim 1) / “A system for improving productivity of an industrial operation based on operator variation analysis for a plurality of operators of an industrial operation, each of the operators of the plurality of operators corresponding to a human who interacts with at least one control system associated with the industrial operation, the system comprising: at least one processor; at least one memory device coupled to the at least one processor, the at least one processor and the at least one memory device configured to” (independent Claim 26) / “A method for improving productivity of an industrial operation based on operator variation analysis for a plurality of operators of the industrial operation, each of the operators of the plurality of operators corresponding to a human who interacts with at least one control system associated with the industrial operation, the method comprising” (independent Claim 29) (Paradies column 5 lines 12-35: system includes information input device 10 connected to communication port of processor 20. The processor 30 is connected to memory device 30, such as hard disk drive, that stores executable software modules and other info in digital format. display device 40, such as computer monitor, is connected to output port of processor 20. The display device 40 provides textual and graphical displays that prompt a user for input information, and that provide analysis results to the user. An information retrieval device 50, such as floppy disk or CD-ROM drive, is connected to input port of processor 20. The information retrieval device 50 provides access to digital info from information storage device 60. The information storage device 60 is a storage medium, such as floppy disk or CD-ROM, that stores executable software modules, relational databases, and other info that comprise the system software. column 6 lines 12-22: the structure in Fig.2 emphasizes most powerful advantages of the invention: the links between causes and corrective actions occur at root cause level, not at higher levels in the causal database structure. Corrective actions based on the root causes have a much higher probability of preventing recurrences of an incident than do corrective actions upon less knowledge, represented by higher levels in database structure. Thus, by providing links between root causes and corrective action, the invention provides suggestions for most effective corrective action. column 9 lines 6-7: Fig.13 represents questions relating to Team Performance. see Fig.13, Fig.51 noting under Team performance: Was coordination required between team members? -> Was lack of agreement about the who/what/when/where of task performance? column 16 lines 8-14: processor 20 generates Corrective Actions Status Tracking Report box on display device 40, in Fig. 43 used for narrowing the scope of the report by selecting responsible departments, and persons)
- “” (independent Claims 1,26,29), “” (independent Claims 1,26)
- “ (Claims1,26,29)
- “clustering the input data into clusters using the respective one or more types of data selected to eliminate multiple non-human root differences, ” (independent Claims 1,26,29); (Paradies column 10 lines 41-43: corrective Action Helper is database of suggested corrective actions linked to root causes on Root Cause level of causal information database. column 18 lines 15-17,36-41: as described causal information database provides relationship between the multiple causal analysis levels and causal factor categories. Through execution instructions provided by causal analysis module 1140, processor 20 determines suggested corrective actions by following the links [for clustering] between corrective action helper module 1160 and causal information database module 1130. column 5 lines 41-54: in Fig.2, the top and broadest analysis level is Difficulties level that includes the causal categories: human performance, equipment, etc. Each of Difficulties level categories branch [or cluster] into basic cause categories that reside on next lower level: Basic Cause level. The Basic Cause level categories branch [or cluster] into near root cause categories that reside on the next lower level [on the clusters]: the Near Root Cause level. Finally, each of Near Root Cause level categories branch into root causes that reside on Root Cause level. Thus, the database progresses from causal categories at top level having broadest scope to lower level causal categories having narrower scope. column 6 lines 12-22: The structure in Fig.2 emphasizes most powerful advantage [ technique] of the invention: the links between causes and corrective actions occur at root cause level, not at higher levels in the causal database structure. Corrective actions on root causes have much higher probability preventing recurrences of incident than do corrective actions based upon less knowledge, as represented by the higher levels in the database structure. Thus, by providing links between root causes and corrective action, the invention provides suggestions for most effective corrective action. column 8 lines 15-20: Since contract officer falls asleep causal factor does not fall into any of other 3 categories [i.e equipment], those categories are eliminated from consideration. As Fig.8 indicates, an eliminated category is preferably indicated by a crossed-out block. See for example Fig.8 crossing-out or elimination non-human equipment cause while indicating human performance root cause 380. Indeed, per column 16 line 62-column 17 line 1: invention includes powerful features for determining root causes of incidents and suggesting corrective actions for root causes related to human performance difficulties. Yet, for causal factors that are equipment-related, the invention also analyzes and suggests corrective action for root causes related to equipment difficulties).
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Paradies Fig.8 in support of rejection arguments
- “analyzing the respective clusters to identify human operator variability in managing the industrial operation, ”; (independent Claims 1,26,29) (Paradies column 8 lines 13-20: When a particular category is selected, the corresponding category block is highlighted in Fig.8. Since the contract officer falls asleep causal factor does not fall into any of the other 3 categories, those categories are eliminated from further consideration. As Fig.8 indicates, an eliminated category is indicated by crossed out block. With continued reference to Fig.8, below the Human Performance Difficulty block 380 is a down-arrow button 390. When processor 20 receives a signal from input device 10 indicating activation of button 390, processor 20 generates the Human Performance Troubleshooting Guide screen in Fig.9. This screen is the first of several screens that present question blocks to user to illicit info related to the particular incident and selected causal factor. The preferred question blocks in Fig.9 are related to “Individual Performance” issues. If user decides that the answer to a particular question is yes, the user simply clicks on the word yes to the right of question block. For example, question block 400 in Fig.9 poses question: “Was there excessive fatigue, impairment, personal problems, or inattentiveness?” An affirmative answer indicates that a root cause may lie under a Human Engineering or Immediate Supervision basic cause category).
- “measuring or quantifying one or more gaps in an operation of the industrial operation based on the operator variability (independent Claims 1,26,29) “the one or more gaps representing improvement potential of productivity of the industrial operation during common process events or abnormal operation if the operator variability is removed”; (independent Claims 1,26)
(Paradies column 1 lines 8-12: invention is directed to identifying human action representing an underlining cause of an incident. For example, at column 16 line 62-column 17 line 4: the invention includes powerful features for determining root causes of incidents and suggesting corrective actions for root causes related to human performance difficulties. For example, at column 8 lines 35-36: Was there excessive fatigue, impairment, personal problems, or inattentiveness? To this end at column 10 lines 36-48: one of most powerful features provided by the invention is to help determine corrective action to prevent future occurrences of similar incidents. Corrective Action Helper is a database of suggested corrective [or improvement] actions linked to root causes on Root Cause level of the causal information database. Preferably, each root cause contained in causal information database, such as each root cause listed under Near Root Cause level category blocks 500-530 in Fig.19, is linked to corresponding corrective action in Corrective Action Helper database. For example, at Fig.47 Corrective action 10: implement new monitoring overtime and supervisory requirements so that the odds on the job are greatly reduced because according to column 12 lines 60-61: scheduling needs improvement. Other example is disclosed at column 12 lines 17-30: to prevent recurrence of Temp Water Treatment Unit Fish Kill incident, the operator may be given additional task of generating reports of measured values of cooling water pressure and temperature of the treatment unit's resin. This corrective action is applicable to wide variety of industrial monitoring situations where operator must remain alert. This suggested corrective action is not obvious solution, even to people who routinely do this trouble-shooting. As Fig.24 indicates, the invention provides multiple suggestions for actions implemented in concert. For example, at Annotated Fig.24: 1(a) Provide an alarm to alert the worker. 1(b) Provide automate monitoring and response system to replace human monitoring and response. Paradies provides additional details at column 6 lines 12-22: the structure in Fig.2 emphasizes the invention most powerful advantages: the links between causes and corrective actions occur at root cause level that have much higher probability of preventing recurrences of an incident than corrective actions based upon less knowledge, as represented by higher levels in the database structure. Thus, by providing links between root causes & corrective action, the invention provides suggestions for most effective corrective action. For example, at column 7 lines 1-10, the failure of the hose caused a loss of cooling water to the unit, which should have been immediately secured. However, the contract operator had fallen asleep. Also, the automatic shutdown features had been bypassed by the contractor who owns the equipment. The continued operation of the unit without cooling water caused the unit's resin to overheat and degrade. This allowed high-temperature, low-pH water to enter the plant's outfall, thus causing the death of about 100 fish in the downstream section of the river at the plant boundary. Then, at column 8 lines 35-36: “Was there excessive fatigue, impairment, personal problems, or inattentiveness?” An affirmative answer indicates that a root cause may lie under a Human Engineering or Immediate Supervision basic cause category. Finally, column 9 lines 33-38: This screen illustrates Near Root Cause level categories under Procedures category. These categories are represented by procedures “not-used/not-followed” block 470, a procedures “wrong” block 480, and a procedures “followed incorrectly” block 490. Below each of the Near Root Cause level category blocks 470-490 is a list of potential root causes. These root causes lie on the Root Cause level of the causal information database)
- “for one or more of the identified distinct products or distinct regimes of operation, automatically selecting one or more mapped solutions from a variety of solutions mapped to different measured or quantified gaps for addressing a root cause of the respective different measured or quantified gaps, the one or more mapped solutions being selected based on the one or more gaps” (independent Claims 1,26,29);
(Paradies column 18 lines 15-17,36-41: as described the causal information database provides relationship between the multiple causal analysis levels and causal factor categories. Through execution instructions by causal analysis module 1140, processor 20 determines suggested corrective actions by following the links between corrective action helper module 1160 and causal information database module 1130. Specifically column 5 lines 55-63 causal information database links each root cause (RC1,RC2, RC3) on Root Cause level to a suggested corrective action. Some root causes are linked to a single corrective action, some to more than one. Some root causes are linked to a common corrective action. Preferably the corrective actions reside in distinct corrective action database related to the causal information database by way of links that associate the two. column 10 lines 41-48: corrective action helper is database of suggested corrective actions linked [interpreted as mapped] to root causes on the Root Cause level of the causal information database. Preferably, each root cause contained in the causal information database, such as each root cause listed under Near Root Cause level category blocks 500-530 in Fig.19, is linked [or mapped] to a corresponding corrective action in the Corrective Action Helper database. column 10 lines 57-60: in Fig.21 Corrective Action Helper database provides [or selects] suggestions and ideas for eliminating the root cause of the incident. similarly, column 12 lines 1-3 the Corrective Action Helper provides [or selects] additional help in the form of suggested actions that the user may take to resolve the problem
Paradies column 10 lines 57-65: in Fig.21, the Corrective Action Helper database provides suggestions and ideas for eliminating the root cause of the incident. These suggestions and ideas provide the user with a framework for thinking about solutions to the specific problem. By thinking within the framework provided by the Corrective Action Helper database, the user can stay focused on eliminating the specific root cause of the specific causal factor.
Paradies column 12 lines 15-30: in Fig.24, one of the suggested corrective actions (d) is to provide other noncompeting tasks for the operator to perform while the operator is monitoring a critical parameter. For example, to prevent recurrence of the Temp Water Treatment Unit Fish Kill incident, the operator may be given an additional task of generating reports of measured values of the cooling water pressure and the temperature of treatment unit's resin. This corrective action is applicable to a wide variety of industrial monitoring situations where the operator must remain alert, but where perhaps the operator's continuous and full attention is not needed for the task. Further, this suggested corrective action is not an obvious solution, even to people who routinely do this sort of trouble-shooting. As Fig.24 indicates, the invention typically provides multiple suggestions for actions that may be implemented separately or in concert) “and”
- “implementing one or more actions specified by the selected one or more mapped solutions, the one or more actions including at least one of a software-based or hardware-based change to modify an operation of the at least one control system, wherein the software-based or hardware-based change compensates for at least a portion of the one or more gaps” (independent Claims 1,26,29) (Paradies column 1 lines 36-43: Implementation of effective corrective action is the preferred means to achieve dramatic, lasting reductions in the numbers of repeat incidents. Thus, to prevent occurrence of a costly incident, the underlying cause of the incident should first be identified, then a specific corrective action is implemented directed to eliminating the specific underlying cause. To this end at column 12 lines 11-30: processor 20 accesses Corrective Action Helper database and retrieves corrective action suggestions linked [or mapped] to this root cause. Preferably, processor 20 then generates a screen in Fig.24 to display the linked corrective actions. A in Fig.24, one of suggested corrective actions [1(a) through] 1(e)... This corrective action is applicable to a wide variety of industrial monitoring situations where operator must remain alert. As Fig.24 indicates, the invention typically provides multiple suggestions for actions that may be implemented separately or in concert. See for example at Annotated Fig. 24
1(a) Provide an alarm to alert the worker.
1(b) Provide automate monitoring and response [or change] system to replace human monitoring and response)… keep the worker informed to what the automation is doing [interpreted as hardware based change] and why it is doing it.
1(e) provide false signals [changes] to keep he worker involved
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Paradies Annotated Fig.24 from original Fig.24 in support of rejection arguments
* While *
Paradies Fig. 46 still discloses Initial Conditions: Plant in operation at steady state before the Event identified as: Cooling water hose ruptured.
Paradies does not explicitly recite, as claimed:
- “processing input data received from one or more data sources to identify steady state process data relating to the industrial operation and distinct products or distinct regimes of operation associated with the steady state process data” (independent Claims 1,26,29), “the steady state process data corresponding to process data that does not change or changes only negligibly over a particular period of time” (independent Claims 1,26)
- “for each of the identified distinct products or distinct regimes of operation, selecting to be clustered one or more types of data in the steady state process data identified, wherein the one or more types of data are selected based on multiple non-human factors” (Claims1,26,29)
- “wherein each of the clusters corresponds to a different one of the distinct regimes of operation, wherein each of the distinct regimes of operation corresponds to a different set of operating conditions used to operate the industrial operation in a steady state” (Claims 1,26,29)
- “analyzing the respective clusters to identify human operator variability for each of the distinct regimes of operation in managing the industrial operation in managing the industrial operation, wherein the operator variability indicates different gaps in economic operation of the same industrial operation by different ones of the plurality of operators” (Claims 1,26,29)
* However *
Pack in analogous art of industrial operation teaches or suggests:
- “processing input data received from one or more data sources to identify steady state process data relating to the industrial operation and distinct products or distinct regimes of operation associated with the steady state process data” (independent Claims 1,26,29), “the steady state process data corresponding to process data that does not change or changes only negligibly over a particular period of time” (independent Claims 1,26)
(Pack ¶ [0036] 5th sentence, ¶ [0038] 1st sentence: receiving input parameters with details at ¶ [0249] 3rd-4th sentences, and emphasis on ¶ [0253] performing stationary test on observable physico-chemical quantities and metadata related to at least one process network operation parameter from database. ¶ [0196] 3rd sentence: apply stationary test has the advantage to ensure that the system to be monitored is currently in a stationary state. ¶ [0256] For monitoring and or controlling a plant or a plant network, the steady state is of major interest. Consequently, the time series data related to a steady state of the production process needs to be determined. ¶ [0257] This may be done by stationary or event analysis. Stationary data are related to a steady state of a production process This allows to inspect the time series data and classify data in stationary…states and be labeled accordingly. Specifically, several segments of a process may be analyzed independently, and labels aggregated to plant level. As one part of the process may in fact be stationary... Depending on the targeted least one process network operation parameter, constraints on stationarity may be relaxed for certain parts of the process. The time series data will then be separated according to their label. The data sets for each label may then be further separated into respective training and test data sets. ¶ [0258] Since the balance equations require a stationary system only stationary data points are useful. Here all those data clusters which were recorded under stationary operating conditions are filtered. Any anomalies shut down periods or other non-stationary segments in the data set are removed. The stationarity test include time series analysis based on volatility or activity to detect outlier and anomalies based on dynamics in time constants derived from the historical data set. Such stationarity analysis allows to reduce the historical data set to data that represents normal operating conditions in the process network. This restricts the historical data to stationary and/ or cyclic stationary operating conditions).
- “for each of the identified distinct products or distinct regimes of operation, selecting to be clustered one or more types of data in the steady state process data identified” (Pack ¶ [0196] 2nd sentence: Applying a stationary test has the advantage that only stationary states are considered. ¶ [0256] For monitoring and/or controlling a plant or plant network, steady state is of major interest. Consequently, the time series data related to a steady state of the production process needs to be determined. ¶ [0257] 1st-2nd,8th sentences: This may be done by stationary or event analysis. Stationary data are related to a steady state of a production process. The data sets for each label may then be further separated into respective training and test data sets. Specifically, at ¶ [0258] 1st-5th sentences: Since the balance equations require a stationary system only stationary data points are useful. Here all those data clusters which recorded under stationary operating conditions are filtered. Any anomalies shut down periods or other non-stationary segments in the data set are removed. The stationarity test include time series analysis based on volatility or activity to detect outlier & anomalies based on dynamics in time constants derived from the historical data set. Such stationarity analysis allows to reduce the historical data set to such data that represents normal operating conditions in the process network. This restricts the historical data to stationary and/or cyclic stationary operating condition), “wherein the one or more types of data are selected based on multiple non-human factors” (Pack ¶ [0132] 1st-3rd sentences: The at least one process network operation parameter refers to an operating parameter intended to be monitored. This process network operation parameter may be any observable selected physical quantity in the collapsed graph structure or any performance metric derived from these selected observable physico-chemical quantities. The at least one process network operation parameter, may reflect the operation parameter at a specific point in time, this may be the current one, and at a specific point in the process network, e.g. a specific unit operation. ¶ [0036] 1st-2nd sentences: Unit operations represent [non-human] reactors, pumps, heat exchangers, cristallers, and other known pieces of equipment installed in a plant. Unit operations further comprise transports, transports define connections between plants, transports may comprise (pipes, ships, trucks, trains, for lifts, or any means moving matter between unit operations) (independent Claims1,26,29)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Paradies’ methods/system to have included Pack’s teachings to have provided a more rigorous monitoring and/or controlling of assets at chemical processes of chemical plant (Pack ¶ [0276] in view of MPEP 2143 G,C,D and/or F) such as the one disclosed by Paradies supra. Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar industrial operation field of endeavor. In such combination each element merely would have performed same analytical, managerial or operational function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Paradies in view of Pack, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
* Further still *
Asendorf in analogous art of industrial operation teaches or suggests:
- “wherein each of the clusters corresponds to a different one of the distinct regimes of operation, wherein each of the distinct regimes of operation corresponds to a different set of operating conditions used to operate the industrial operation in a steady state” (Claims 1,26,29);
(Asendorf ¶ [0043] 2nd sentence: underlying analytics engine of SMS 6 may apply historical data and models to the inbound streams to compute assertions, such as identified anomalies or predicted occurrences of safety events based on conditions patterns of workers 10. ¶ [0029] 5th -8th sentences and ¶ [0046] 3rd sentence noting the operating conditions correspond to temperature, heat, humidity, visibility, pressure, etc. ¶ [0096] 2nd sentence: CAS 68F determine a quantity of time utilized by workers at different work environments to perform a given task, such as install an article of equipment or decontaminate a work environment, among others)
- “analyzing the respective clusters to identify human operator variability for each of the distinct regimes of operation in managing the industrial operation in managing the industrial operation” (Asendorf ¶ [0029] 5th-8th sentences [worker safety management system] SMS 6 utilize the environmental data to aid generating alerts or other instructions for PPE 13 and for performing predictive analytics, such as determining any correlations between certain environmental conditions (e.g., heat, humidity, visibility) with abnormal worker behavior or increased safety events. [worker safety management system] SMS 6 utilize the environmental data to aid generating alerts or other instructions for PPE 13 and for performing predictive analytics, such as determining any correlations between certain environmental conditions (e.g., heat, humidity, visibility) with abnormal worker behavior or increased safety events. Example environmental conditions that may be sensed by sensing stations 21 include but are not limited to temperature, humidity, presence of various gasses, pressure, visibility, wind, ambient light, ambient noise, radiation, air quality, and the like. In other words, sensing stations 21 may include temperature sensors, moisture and/or humidity sensors, gas sensors, pressure sensors, light sensors, audio sensors, radiation sensors, and so forth. In some examples, sensing stations 21 may include camera configured to monitor movements of workers 10 while workers 10 are operating in the respective work environments 8. ¶ [0046] 3rd sentence: dashboards provide various insights regarding system 2, such as baseline (normal) operation across worker populations, identifications of any anomalous workers engaging in abnormal activities that may potentially expose the worker to risks, identifications of any geographic regions within work environments 8 for which unusually anomalous (e.g. high) safety events have been or are predicted to occur, identifications of any of work environments 8 exhibiting anomalous occurrences of safety events relative to other environments, and the like. ¶ [0056] 6th sentence: SMS 6 [worker safety management system] determine a safety metric for each worker in a cluster of workers 10, for each work environment in a cluster of work environments 8, or both. ¶ [0079] 3rd-4th sentences: CAS [cluster and analysis service] 68F transform work environment data using techniques such as one-hot encoding (e.g. when work environment data includes categorical variables), natural language processing techniques (when work environment data includes text data), data normalization (e.g., making all data zero mean and unit variance), among others) “wherein the operator variability indicates different gaps in economic operation of the same industrial operation by different ones of the plurality of operators” (Asendorf ¶ [0029] 5th-8th sentences above and ¶ [0101] 2nd sentence: compare the single worker 10 to other workers within the SAME work environment 8, (i.e. humidity, temperature, pressure, etc. per ¶ [0029] 5th-8th sentences) to other workers across the workers 10 company, and/or across multiple companies. ¶ [0046] 3rd sentence: dashboards provide various insights regarding system 2, such as baseline (normal) operation across worker populations, identifications of any anomalous workers engaging in abnormal activities that may potentially expose the worker to risks, identifications of any geographic regions within work environments 8 for which unusually anomalous (e.g. high) safety events have been or are predicted to occur, identifications of any of work environments 8 exhibiting anomalous occurrences of safety events relative to other environments, and the like. Specifically, per ¶ [0056] 2nd-4th sentences: [worker safety management system] SMS 6 determine a difference [or gap] between performance by a target entity with respect to safety events and performance of the cluster that includes the target entity with respect to safety events. In other words, [worker safety management system] SMS 6 determine differences in safety performance of a target entity relative to a cluster of similar entities. In some examples, [worker safety management system] SMS 6 may determine the difference between performance by the target entity relative to the performance of the cluster by determining the performance for each entity in a cluster of entities that includes the target entity. ¶ [0061] 3rd sentence: [worker safety management system] SMS 6 output a rank, with respect to the difference, of the target entity against the cluster of entities that includes the entity, an absolute difference [or gap], a relative difference [or gap], a visualization, or other indication of the difference [or gap]. Similarly, ¶ [0086], ¶ [0098] 5th, 7th sentences: CAS 68F determine whether a difference [or gap] between performance of a target entity relative to performance of the cluster of entities that include the target entity satisfies a threshold difference (e.g., one standard deviation [or gap]). Similarly, CAS 68F may determine whether a difference between efficiency of target worker 10A relative to efficiency of the cluster of workers including target worker 10A is greater than a threshold efficiency. ¶ [0124] 4th-5th sentences: indicates the performance of a target entity with respect to safety and indicates the performance (e.g., average performance) of a cluster of entities that includes the target entity, such that the graphical further indicates the difference [or gap] between the performance of the target entity and the performance of the cluster of similar entities. For example, the graphical user interface may indicate the number of safety events experienced by each respective entity in the cluster (e.g., including the target entity) and an average number of safety events experienced by the entities in the cluster).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Paradies / Pack methods/system to have further included Asendorf’s teachings or suggestions in order to have further improved worker safety (Asendorf ¶ [0006], ¶ [0062] in view of MPEP 2143 G, C, D and/or F). The predictability of such modification would have been further corroborated by the broad level of skills of one of ordinary skills in the art as further corroborated by Asendorf ¶ [0154], ¶ [0156].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar industrial operation field of endeavor. In such combination each element merely would have performed same analytical, managerial or operational function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Paradies/Pack in view of Asendorf, the to be combined elements would have fitted together, like puzzle pieces in logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 2 Paradies / Pack / Asendorf teaches all the limitations in claim 1 above. Furthermore,
Paradies teaches: “the distinct products correspond to products produced by the industrial operation” (column 7 lines 7-10: water treatment product of high-temperature, low-pH water)
Pack also teaches: “wherein the distinct products correspond to products produced by the industrial operation” (Pack ¶ [0274] 1st, 5th, 6th sentences: Chemical plants produce a product via chemical processes transforming the feedstock via intermediate products to the product. These upstream products may then be provided to chemical plants to derive downstream products such as polyethylene or polypropylene, which again serve as feedstock for chemical plants deriving further downstream products. Chemical plants may be used to manufacture discrete products). Rationales to have modified/combined Paradies/Pack/Asendorf were presented above.
Claim 3 Paradies / Pack / Asendorf teaches all the limitations in claim 1 above. Furthermore,
Paradies teaches “wherein the distinct regimes of operation occur due to physical differences in the industrial operation” (Paradies column 6 line 65-column 7 line 3, column 7 lines 5-10: At about 4:20 am a cooling water hose ruptured on temporary water treatment unit at a chemical manufacturing plant. The failure of the hose caused a loss of cooling water to the unit, which should have been immediately secured. The continued operations of the unit without cooling water caused the unit’s resin to overheat and degraded. This allowed high-temperature, low-PH water to enter the plant’s outfall, thus causing death of 100 fish in downstream section of the river at the plant boundary The hose, which had been bent around a comer, ruptured due to dry rot when pressurized to about 100 psig. Fig. 46: About 40 minutes after hose ruptured, plant personnel looking into a report of water on a roadway adjacent to the manufacturing building found the ruptured hose and sleeping operator and had the operator secure the water unit, thus stopping the release of high temperature, low pH water).
Pack also teaches: “wherein the distinct regimes of operation occur due to physical differences in the industrial operation” (Pack ¶ [0257] 3rd -5th sentences: inspect the time series data and classify data in stationary and non-stationary, the non-stationary data relate to ramp-up states, ramp down states, on/off states or error states and be labeled accordingly. Specifically, several segments of a process may be analyzed independently, and labels aggregated to plant level. As one part of the process may in fact be stationary, while other are not).
Rationales to have modified/combined Paradies/Pack/Asendorf were presented above.
Claim 4 Paradies / Pack / Asendorf teaches all the limitations in claim 3 above. Furthermore,
Paradies teaches: “wherein the physical differences in the industrial operation are due to non-human root causes” (Paradies column 7 lines 22-26: cooling water hose ruptured was 4 inch diameter fire hose. The hose, bent around a corner, apparently ruptured due to dry rot when pressurized to 100 psig. Also Fig.51, column 17 lines 29-33 noting repeat failure of equipment)
Pack also teaches “wherein the physical differences in the industrial operation are due to non-human root causes” (Pack ¶ [0036] 1st sentence: Unit operations may represent columns, reactors, pumps, heat exchangers, cristallers, and other known pieces of equipment that may be installed in a plant. ¶ [0073] These changes in the process network may be sensor failures, or other errors). Rationales of modified/combined Paradies/Pack/Asendorf were presented above.
Claim 5 Paradies / Pack / Asendorf teaches all the limitations in claim 4 above. Furthermore,
Paradies teaches: “wherein the non-human root causes include equipment, process, or ambient root causes” (Paradies column 7 lines 22-26: cooling water hose ruptured was 4 inch diameter fire hose. The hose, bent around a corner, apparently ruptured due to dry rot when pressurized to 100 psig. Also Fig.51, column 17 lines 29-33 noting repeat failure of equipment)
Pack also teaches “wherein the non-human root causes include equipment, process, or ambient root causes” (Pack ¶ [0036] 1st sentence: Unit operations represent columns, reactors, pumps, heat exchangers, cristallers, and other known pieces of equipment that may be installed in a plant. ¶ [0073] These changes in the process network may be sensor failures, or other errors).
Asendorf also teaches “wherein the non-human root causes include equipment, process, or ambient root causes” (Asendorf ¶ [0029] 5th-8th sentences and ¶ [0046] 3rd sentence noting the operating conditions correspond to temperature, heat, humidity, visibility, pressure, etc.)
Rationales to have modified/combined Paradies / Pack / Asendorf were presented above.
Claim 6 Paradies / Pack / Asendorf teaches all the limitations in claim 1 above. Furthermore,
Paradies does not explicitly teach: “wherein the distinct regimes of operation are recorded in time series data of event data in the steady state process data” as claimed. However,
Pack in analogous industrial operation teaches or suggests: “wherein the distinct regimes of operation are recorded in time series data of event data in the steady state process data” (Pack ¶ [0252] 1st-3rd sentences: Historical data may comprise time series for measured values and/or observables. Generally, the historical data comprise various states of the production plant or the network of production plants. These states may comprise amongst other things, a steady-state).
Rationales to have modified/combined Paradies / Pack / Asendorf were presented above.
Claim 7 Paradies / Pack / Asendorf teaches all the limitations in claim 1 above. Furthermore,
Paradies does not explicitly teach:
- “identifying and removing downtime data from the steady state process data” as claimed.
Pack however in analogous art of industrial operation teaches or suggests:
- “identifying and removing downtime data from the steady state process data”
(Pack ¶ [0252] 1st-3rd sentences: Historical data may comprise time series for measured values and/or observables. Generally, the historical data comprise various states of the production plant or the network of production plants. These states may comprise amongst other things, a steady-state. ¶ [0258] 3rd sentence: Any anomalies shut down periods or other non-stationary segments in the data set are removed)
Rationales to have modified/combined Paradies / Pack / Asendorf were presented above.
Claim 8 Paradies / Pack / Asendorf teaches all the limitations in claim 1 above. Furthermore,
Paradies does not explicitly teach:
- “identifying and removing data associated with abnormal periods of operation from the steady state process data”.
Pack however in analogous art of industrial operation teaches or suggests:
- “identifying and removing data associated with abnormal periods of operation from the steady state process data”. (Pack ¶ [0252] 1st-3rd sentences historical data comprise time series for measured values and/or observables. historical data comprise various states of production plant or network of production plants. These states comprise amongst other things, a steady-state. ¶ [0258] 3rd sentence: Any anomalies shut down periods or other non-stationary segments in the data set are removed).
Rationales to have modified/combined Paradies / Pack / Asendorf were presented above.
Claim 9 Paradies / Pack / Asendorf teaches all the limitations in claim 8 above.
Paradies teaches: “wherein the abnormal periods of operation correspond to periods of significantly reduced production rates or periods in which at least one product produced by the industrial operation is of off specification quality” (Paradies column 6 line 65 - column 7 line 3, column 7 lines 5-10: At about 4:20 am a cooling water hose ruptured on a temporary water treatment unit at a chemical manufacturing plant. The failure of the hose caused a loss of cooling water to the unit, which should have been immediately secured. The continued operations of the unit without cooling water caused the unit’s resin to overheat and degraded. This allowed high-temperature, low-PH water to enter the plant’s outfall, thus causing the death of about 100 fish in the downstream section of the river at the plant boundary The hose, which had been bent around a comer, ruptured due to dry rot when pressurized to about 100 psig. Fig. 46: About 40 minutes after hose ruptured, plant personnel looking into a report of water on a roadway adjacent to the manufacturing building found the ruptured hose and the sleeping operator and had the operator secure the temporary water unit, thus topping the release of the high temperature, low pH water)
Pack also teaches “wherein the abnormal periods of operation correspond to periods of significantly reduced production rates or periods in which at least one product produced by the industrial operation is of off specification quality” (Pack ¶ [0257] 3rd sentence noting ramp-down states. ¶ [0258] 3rd sentence: noting shut down periods or other non-stationary segments).
Rationales to have modified/combined Paradies / Pack / Asendorf were presented above.
Claim 10. Paradies / Pack / Asendorf teaches all the limitations in claim 1 above.
Paradies does not explicitly teach: “performing outlier detection and applying one or more rules for removing samples from the steady state process data” as claimed. However,
Pack in analogous industrial operation teaches/suggests: “performing outlier detection and applying one or more rules for removing samples from the steady state process data”
(Pack ¶ [0258] 4th sentence: The stationarity test may include time series analysis based on volatility or activity to detect outlier and anomalies based on dynamics in time constants derived from historical data set. ¶ [0258] 3rd sentence: such anomalies or shut down periods are removed. As explained by ¶ [0258] 5th-6th sentences: such stationarity analysis allows to reduce the historical data set to such data that represents normal operating conditions in the process network. This restricts historical data to stationary and/ or cyclic stationary operating conditions).
Rationales to have modified/combined Paradies / Pack / Asendorf were presented above.
Claims 15,27,31 Paradies/Pack/Asendorf teaches all limitations in claims 1,26,29. Further
Paradies teaches “selecting the one or more mapped solutions includes
- “analyzing the one or more gaps to determine if relevant characteristics associated with the one or more gaps justify the selected one or more mapped solution for addressing the one or more gaps for the industrial operation” (Paradies column 1 lines 21-25: incidents result not only in lost revenue, but also of life and environmental damage. Fortunately, most incidents can be prevented if business managers understand and eliminate causes of such incidents. For example, at column 6 line 62-column 7 line 10: On Jun.1 1998, at about 4:20 a.m., a cooling water hose ruptured on a temporary water treatment unit at a chemical manufacturing plant. The failure of the hose caused a loss of cooling water to the unit, which should have been immediately secured. However, the contract operator had fallen asleep. Also, the automatic shutdown features had been bypassed by the contractor who owns the equipment. The continued operation of the unit without cooling water caused the unit’s resin to overheat and degrade. This allowed high-temperature, low-pH water to enter the plant’s outfall, thus causing death of 100 fish in downstream section of the river at plant boundary. The contract operator had fallen asleep sometime after filling out his log sheet at 4 a.m. He was on his 5th week of 12-hour shifts. He would typically work 8 straight days of 12 hours on and 12 hours off, followed by 4 days off before returning for another 8 straight days of 12 hours on and 12 hours off. His 12-hour shifts began at 6 pm and ended 6 am. He was on his 2nd day of 12-hour night-shift duty when the incident occurred. He had been awake the whole day before his first 12-hour shift and had slept for only about 4 hours before his 2nd night shift. Thus, he had had only 4 hours sleep in the prior 44 hours. The cooling water hose that ruptured was four-inch diameter fire hose. The hose, which had been bent around a comer, apparently ruptured due to dry rot when pressurized to about 100 psig. column 9 lines 41-43: For the causal factor of this example, contract operator falls asleep, none of Near Root Cause level categories are applicable. Thus, the user would cross out each of category blocks 470-490. In this manner, the user indicated that none of the root causes listed beneath the category blocks 470-490 are possible root causes for this example. [However] when the user decides that a potential root cause is actually a root cause of the incident, the user selects the root cause by clicking once on the text of the root cause. For example, below the SPAC not used category 510 in Fig.19, user has checked root cause: no way to implement 540 as a reason for why SPAC was not used. When user clicks on text of a root cause, processor 20 designates the root cause and generates a check mark next to the text in Fig.19.
Paradies column 10 lines 36-48: Now that the system helped the user pinpoint a root cause of the particular incident of this example, one of the most powerful features provided by the preferred embodiment of the invention is utilized to help the user determine corrective action to prevent future occurrences of similar incidents. The Corrective Action Helper is a database of suggested corrective actions that are linked to root causes on the Root Cause level of the causal information database. Preferably, each root cause contained in the causal information database, such as each root cause listed under the Near Root Cause level category blocks 500-530 in Fig.19, is linked to a corresponding corrective action in the Corrective Action Helper™ database. column 11 line 62-column 12 line 3: At this point in the analysis of a particular incident, the user has narrowed the cause of the incident down to one or more root causes. However, the user may have no idea what to do to eliminate a root cause, and thus preventing the incident from recurring. Prior incident analysis systems offer no further help to the user. However, as described further below, the Corrective Action Helper of the present invention provides additional help in the form of suggested actions that the user may take to resolve the problem.
Paradies column 13 lines 13-25: At this point in the current example, the user viewed the Near Root Cause and Root Cause levels beneath each of the basic cause categories that were determined to be relevant based on the user’s answers to the questions in Figs.9-16. Thus, at this point, the system has completed the root cause analysis steps that are relevant to the “contract officer falls asleep” causal factor. In an actual use situation, the user might proceed at this time to the Corrective Action or Reports options, as described in detail hereinafter. However, for completeness, this description continues with discussions of the Near Root Cause and Root Cause levels beneath the other basic cause categories that have not yet been described.
Paradies column 12 lines 11-30: processor 20 accesses Corrective Action Helper database and retrieves corrective action suggestions linked [or mapped] to this root cause. Preferably, processor 20 then generates the screen in Fig.24 to display the linked corrective actions. in Fig.24, one of suggested corrective actions [1(a) through] 1(d)... This corrective action is applicable to a wide variety of industrial monitoring situations where the operator must remain alert… As Fig.24 indicates, the invention typically provides multiple suggestions for actions that may be implemented separately or in concert. For example at Annotated Fig. 24: 1(a) Provide an alarm [control signal] to alert the worker. 1(b) Provide automate monitoring [control signal] and response system [controllable device] to replace human monitoring and response. Also see MPEP 2111.04 II noting the limited patentable weight of contingent limitations).
Claims 16,28,32 Paradies/Pack/Asendorf teaches all limitations in claims 15,27,31. Further
Paradies teaches or suggests “further comprising”:
- “wherein the one or more actions are taken contingent upon the determining that the relevant characteristics associated with the one or more gaps justify the selected one or more mapped solutions” (dependent Claims 16,32) / “in response to determining the relevant characteristics associated with the one or more gaps justify the selected one or more mapped solutions for addressing the one or more gaps for the industrial operation, the one or more actions are taken contingent upon the determination whether the relevant characteristics associated with the gap justify the selected one or more mapped solutions” (dependent Claim 28)
(Paradies column 10 lines 36-48: Now that the system helped the user pinpoint a root cause of the particular incident of this example, one of the most powerful features provided by the preferred embodiment of the invention is utilized to help user determine corrective action to prevent future occurrences of similar incidents [i.e. chemical plant killing fishes]. The Corrective Action Helper is a database of suggested corrective actions linked to root causes on the Root Cause level of the causal information database. Preferably, each root cause contained in the causal information database, such as each root cause listed under the Near Root Cause level category blocks 500-530 in Fig.19, is linked to a corresponding corrective action in Corrective Action Helper database. column 12 lines 17-30: For example, to prevent recurrence of “Temp Water Treatment Unit Fish Kill” incident, the operator may be given [communicated] additional task of generating reports of measured values of cooling water pressure and temperature of the treatment unit's resin. This corrective action is applicable to wide variety of industrial monitoring situations where operator must remain alert. This suggested corrective action is not obvious solution, even to people who routinely do this trouble-shooting. As Fig.24 indicates, the invention provides multiple suggestions for actions implemented in concert. For example, at Annotated Fig. 24: 1(a) Provide an alarm [or communication] to alert the worker. 1(b) Provide automate monitoring and response system to replace human monitoring and response. Also see MPEP 2111.04 II noting the limited patentable weight of contingent limitations).
Claim 17 Paradies/Pack/Asendorf teaches all the limitations in claim 16. Furthermore,
Paradies teaches or suggests “wherein the one or more actions taken based on or using the selected one or more mapped solutions further include communicating information relating to the selected one or more mapped solutions” (Paradies column 12 lines 17-30: to prevent recurrence of Temp Water Treatment Unit Fish Kill incident, the operator is given [communicated] additional task of generating reports of measured values of cooling water pressure and temperature of the treatment unit's resin. This corrective action is applicable to wide variety of industrial monitoring situations where operator must remain alert. This suggested corrective action is not obvious solution, even to people who routinely do this trouble-shooting. As Fig.24 indicates, the invention provides multiple suggestions for actions implemented in concert: 1(a) Provide an alarm [or communication] to alert the worker. 1(b) Provide automate monitoring and response system to replace human monitoring and response. Fig. 47, corrective action 10: Implement new monitoring, overtime, and supervisory requirements so that the odds of sleeping on job are greatly reduced. Fig.47 corrective action 11: create guidance for operators, mechanics, and contract personnel for actions they are to take if they feel excessively drowsy or in any way feel unfit to perform the job they have been assigned. Fig. 47, Corrective action 12: Create and provide training for all operators, mechanic and supervisors on actions they are to take if they feel excessively drowsy or in any way feel unfit to perform the job that they have been assigned).
Claim 18 Paradies/Pack/Asendorf teaches all the limitations in claim 17. Furthermore,
Paradies teaches or suggests “wherein the information includes predicted economic benefits by implementing the selected one or more mapped solutions” (Paradies column 1 lines 37-43 implementation of effective corrective action is preferred means to achieve dramatic, lasting reductions in the numbers of repeat incidents. Thus, to prevent occurrence of a costly incident, the underlying cause of the incident should first be identified, and then a specific corrective action is implemented directed to eliminating the specific underlying cause. For example, see Fig. 46 noting an incident cost of $256953 for Incident 980195 E entitled Temp Water Treatment Unit Fish Kill, with the Immediate corrective action: All fish were collected and disposed of No long term environmental damage is expected as a result of the temporary outfall exceedance. Further Fig. 47: Corrective Actions 10-12)
Claim 19 Paradies/Pack/Asendorf teaches all the limitations in claim 17. Furthermore,
Paradies teaches “the information is communicated via a report, text, email or audibly”
(Paradies column 15 lines 54-57: invention provides reports that summarize facts relating to a particular incident, that detail the root cause analysis, and that detail the corrective actions).
Claim 20 Paradies/Pack/Asendorf teaches all the limitations in claim 1. Furthermore,
Paradies teaches or suggests: “wherein the input data from which the steady state process data identified includes at least one of non-steady state process data and downtime data in addition to the steady state process data” (Paradies Fig. 46 Initial Conditions: Plant in operation at steady state before the Event identified as: Cooling water hose ruptured. Then the event: Cooling water hose ruptured at 4:20 am casing loss of cooling water to the unit. The continued operation of the unit without cooling water caused the unit resin to overheat and degrade. Therefore, failure of the cooling water hose plus failure to secure the unit allowed high temperature and low PH water (estimated to be 200 degrees Fahrenheit and pH 3) to enter the plants outfall and cause about 100 fish to be filled in the downstream section of the river at the plant boundary).
Pack also teaches or suggests “wherein the input data from which the steady state process data is identified includes at least one of non-steady state process data and downtime data in addition to the steady state process data” (Pack ¶ [0191] retrieving historical data related to observable physico-chemical quantities and metadata related to at least one process network operation parameter from a database comprise retrieving time series data. ¶ [0252] 2nd-3rd sentences: the historical data comprise various states of production plant or network of production plants. These states comprise amongst other things, a steady-state, start-up state, shut-down state and an error state. Also ¶ [0257] 3rd sentence: inspect the time series data and classify data in stationary and non-stationary [non-steady], the non-stationary data relate to ramp-up, ramp down states, on/off states or error states and be labeled accordingly. ¶ [0258] 2nd-3rd sentences: Here all those data clusters which were recorded under stationary operating conditions are filtered. Any anomalies shut down periods or other non-stationary segments in the data set are removed) Rationales to have modified/combined Paradies/Pack/Asendorf were presented above.
Claim 21. Paradies/Pack/Asendorf teaches all the limitations in claim 1. Furthermore,
Paradies teaches or suggests “the input data includes time series or alarm event data collected from at least one industrial process associated with the industrial operation”.
(Paradies column 6 lines 54-column 7 line 28: examples of time series throughout shift
Paradies Fig.51 Where alarms and equipment identified or operated improperly? -> Yes)
Paradies column 7 lines 48-50 the alarm cannot be heard due to a nearby diesel engine)
Pack also teaches or suggests: “wherein the input data includes time series”
(Pack ¶ [0137], ¶ [0191] The step of retrieving historical data related to observable physico-chemical quantities and metadata related to the at least one process network operation parameter from a database may comprise retrieving time series data. ¶ [0252] Historical data may comprise time series for measured values and/or observables. Generally, the historical data comprise various states of the production plant or the network of production plants. These states may comprise amongst other things, a steady-state, a start-up state, a shut-down state and an error state) “or alarm event data collected from at least one industrial process associated with the industrial operation” (Pack ¶ [0196] 5th-6th sentences: This signal may be an alarm signal and may be provided to a plant network control center. The alarm signal may shut down of one plant or trigger shut down of the process network. Similarly, ¶ [0269] notification or alarm may be triggered in the affected plants or may be used for root cause analytics).
Rationales to have modified/combined Paradies/Pack/Asendorf were presented above.
Claim 22. Paradies/Pack/Asendorf teaches all the limitations in claim 1. Furthermore,
Paradies teaches or suggests: “wherein the input data is received in or converted to digital form” (Paradies column 18 lines 12-14: the causal information database in a digital format that may be accessed or received] by processor 20. Column 18 lines 43-46: The information storage device 60 also includes a root cause helper module 1170. This module 1170 stores the previously-described root cause helper database in a digital format accessed [or received] by processor 20. Also, column 18 lines 28-33: noting a different example where another component of the information storage device 60 is corrective action helper module 1160. This module 1160 stores previously-described Corrective Action Helper database in a digital format that may be accessed by the processor 20. Alternatively see column 1 lines 59-62 noting converting the incident information into a System-compatible format) “and includes one or more timestamps, and the input data is indicative of at least one action by the plurality of operators or timing of the at least one action” (Paradies Specifically see Fig.44 shows an example of the first page of a printed Corrective Actions Status Tracking Report generated by the system for the “fish kill” incident. This report is preferably presented in a tabular format with columns for the incident, the corrective action, the status of the corrective action, the due date for completing the corrective action, and the actual completion date. Fig. 46 noting Date and time of Incident 6/1/98 5:00 am, Date and Time Investigation Started 6/1/88 9:00 am, Date Draft Report Sent for Approval 6/22/98, Date Final Report Issued 7/1/98. Also see under section entitled incident Description: About 40 minutes after hose ruptured, plant personnel looking into a report of water on a roadway adjacent to the manufacturing building found the ruptured hose and the sleeping operator and had the operator secure the temporary water unit, thus topping the release of the high temperature, low pH water).
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Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over
Paradies/Pack/Asendorf as applied to claim 1 above, in view of
Panda; Prasanta US 20190050763 A1 hereinafter Panda. As per,
Claim 11 Paradies/Pack/Asendorf teaches all the limitations in claim 1 above.
Paradies/Pack/Asendorf does not teach “wherein clustering the one or more types of data for each of the identified distinct products or distinct regimes of operation using one or more data clustering techniques, includes”:
- “determining one or more best stationary data clustering techniques for clustering the one or more types of data for each of the identified distinct products or distinct regimes of operation”; “and”
- “clustering the one or more types of data for each of the identified distinct products or distinct regimes of operation using the determined one or more best stationary data clustering techniques”.
Panda however in analogous art of technological analysis teaches or suggests: “wherein clustering the one or more types of data for each of the identified distinct products or distinct regimes of operation using one or more data clustering techniques, includes”:
- “determining one or more best stationary data clustering techniques for clustering the one or more types of data for each of the identified distinct products or distinct regimes of operation”; (Panda ¶ [0027] 3rd, 9th-10th sentences: At step 406, processors 102 in conjunction with the model fitting module 108 are configured to determine a best fit model, from a plurality of time series models, for the first time series that provides an error below a Error Tolerance (ET) threshold. At step 408, processors 102 in conjunction with model fitting module 108 determine the best fit model, from the plurality of time series models in the repository, for a second time series. The second time series corresponds to time series placed at cluster height equal to cluster height of first time series. ¶ [0029] At step 410, processors 102 in conjunction with model fitting module 108 are configured to determine best fit models for the time series placed at successive higher cluster heights of the branch. This is performed by iterating steps of determining of the best fit model for the first and the second time series at every cluster height of the branch) “and”
- “clustering the one or more types of data for each of the identified distinct products or distinct regimes of operation using the determined one or more best stationary data clustering techniques”. (Panda ¶ [0029] At step 410, processors 102 in conjunction with model fitting module 108 determine the best fit models for the plurality of time series placed at successive higher cluster heights of the branch. This is performed by iterating steps of determining of the best fit model for the first time series and second time series at every cluster height of the branch).
It would have been obvious to one skilled in the art, to have further modified Paradies/Pack/Asendorf “method” to have included Panda’s teachings in order to have sought and achieved a good balance between identifying best fit model for each time series and identifying common best fit models for plurality of series so as to achieve good time efficiency during model fitting along with good forecast accuracy (Panda ¶ [0004] last sentence, MEP 2143 G). The predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as further articulated by Panda ¶ [0051], ¶ [0054].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar variation analysis field of endeavor. In such combination each element merely would have performed the same analytical and operative function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Paradies/Pack/Asendorf’s in further view of Panda, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 12 Paradies/Pack/Asendorf/Panda teaches all the limitations in claim 11 above.
Paradies/Pack/Asendorf does not explicitly teach “wherein gross clusters are created for the one or more types of data using the determined one or more best stationary data clustering techniques” as claimed.
Panda however in analogous art of technological analysis teaches or suggests: “wherein gross clusters are created for the one or more types of data using the determined one or more best stationary data clustering techniques” (Panda ¶ [0027] 9th-10th sentences: At step 408, processors 102 in conjunction with model fitting module 108 determine the best fit model, from the time series models in the repository, for a second time series. The second time series corresponds to time series placed at cluster height equal to cluster height of first time series. ¶ [0029] At step 410, processors 102 in conjunction with the model fitting module 108 are configured to determine the best fit models for the plurality of time series placed at successive higher cluster heights of the branch. This is performed by iterating steps of determining of the best fit model for the first time series and the second time series at every cluster height of the branch). Rationales to have modified/combined Paradies/Pack/Asendorf/Panda are above and reincorporated.
Claim 13 Paradies/Pack/Asendorf/Panda teaches all the limitations in claim 11 above.
Paradies/Pack/Asendorf does not explicitly recite:
- “building an autoregressive integrated moving average (ARIMA) model for each steady state cluster associated with the clustered one or more types of data”; “and”
- “identifying points with high prediction error in the ARIMA model” as claimed. However,
Panda in analogous art of technological analysis teaches or suggests
- “building an autoregressive integrated moving average (ARIMA) model for each steady state cluster associated with the clustered one or more types of data”;
(Panda ¶ [0006] a processor implemented method for model building, interchangeably referred as model fitting, for time series clusters. ¶ [0027] 6th sentence: the models for the time series that are evaluated include… AutoRegressive Integrated Moving Average (ARIMA)) “and”
- “identifying points with high prediction error in the ARIMA model”
(Panda ¶ [0027] 3rd, 6th-7th sentences: At step 406, processors 102 in conjunction with the model fitting module 108 are configured to determine a best fit model, from a plurality of time series models, for the first time series that provides an error below an Error Tolerance (ET) threshold. the models for the time series that are evaluated include… AutoRegressive Integrated Moving Average (ARIMA). The ET threshold and ED threshold are user defined values, for example, can be set to 10% to 15% that may be derived from user experience gained by analyzing model fitting process for time series. See one example at ¶ [0048]). Rationales to have modified/combined Paradies/Pack/Asendorf/Panda are above and reincorporated.
Claim 14. Paradies/Pack/Asendorf / Panda teaches all the limitations in claim 13 above.
Paradies/Pack/Asendorf does not explicitly recite:
- “wherein the identified points are used to confirm bounds of each steady state cluster”.
Panda however in analogous art of technological analysis teaches or suggests:
- “wherein the identified points are used to confirm bounds of each steady state cluster”.
(Panda ¶ [0028] In a scenario, best fit model determined for first time series is selected as the model for second time series if error when first best fit model is applied to second time series is below ET threshold. in another scenario, if the error, when first best fit model is applied to second time series, is above ET but ED between the error for first time series and the error for second time series is below Error Difference (ED) threshold then best fit model determined for first time series is applied to second time series. However, if ED is above ED threshold, then a next best fit model is identified. The next best fit model is selected such that it provides the error below the ET threshold. The next best fit model is selected from remaining models excluding the determined best fit model for the selected time series (first time series). In an embodiment, the next best fit model is next in sequence model in repository that satisfies ET threshold. Thus, remaining models in repository are checked, with the method steps for model selection iterating till a model, satisfying ET threshold is not encountered. In an embodiment, the best fit model from remaining models is one selected by applying all models and identifying model with least error).
Rationales to have modified/combined Paradies/Pack/Asendorf/Panda are above and reincorporated.
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Claims 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over
Paradies/Pack/Asendorf as applied to respective parent claim 1 in view of
Rumi et al, US 20060224434 A1 hereinafter Rumi. As per,
Claim 23. Paradies/Pack/Asendorf teaches all the limitations in claim 1. Furthermore,
Paradies teaches or suggests:
- “wherein the input data is (Paradies column 7 lines 5-7: The continued operation of the unit without cooling water caused the unit's resin to overheat and degrade. column 7 lines 22-28: The hose, ruptured due to dry rot when pressurized to about 100 psig. column 7 lines 7-10: This allowed high-temperature, low-pH water to enter the plant's outfall, thus causing the death of about 100 fish in the downstream section of the river at the plant boundary. column 12 lines 17-21: measured values of the cooling water pressure and the temperature of the treatment unit’s resin) “and”
- “wherein identifying the operator variability includes determining correlations between one or more metrics associated with the industrial operation” (Paradies column 5 lines 60-65: the corrective actions reside in a distinct database related to the causal information database by way of links that associate [correlate] the two databases. Similarly, column 6 lines 7-11: the invention then provides the user with a convenient means of recording the planned corrective action, associating [correlating] the planned corrective action with determined root cause, and for tracking the progress of implementation of the corrective action. column 7 lines 44-51: Causal Factor List shows possible causal factors associated [or correlated] with a specific incident. In Fig.6, four causal factors have previously been identified for the fish kill incident: (1) contract operator (CO) falls asleep, (2) fire hose ruptures, (3) sleeping CO can't hear alarm due to nearby diesel engine, and (4) automatic shut-off jumpered. These are four factors that an investigator has previously determined to be possible causes of the fish kill based on an investigation of the incident. column 7 lines 11-21 contract operator had fallen asleep sometime after filling out his log sheet at 4 a.m. He was on his 5th week of 12-hour shifts. He would typically work 8 straight days of 12 hours on and 12 hours off, followed by 4 days off before returning for another 8 straight days of 12 hours on and 12 hours off. His 12-hour shifts began at 6 pm and ended at 6 am He was on 2nd day of 12-hour night-shift duty when the incident occurred. He had been awake the whole day before his first 12-hour shift, and had slept for only about 4 hours before his second night shift. Thus, he had had only four hours sleep in the prior 44 hours. column 8 lines 35-39: “Was there excessive fatigue, impairment, personal problems, or inattentiveness?” An affirmative answer to this question indicates that a root cause may lie under a Human Engineering or Immediate Supervision basic cause category. column 11 lines 52-56: Since the causal factor of the current example, “contract operator falls asleep,” involves a human's loss of control over a machine, the user highlights the “Human-Machine Interface” block 590 at Near Root Cause level. Beneath the block 590, the user checks “monitoring alertness needs improvement (NI)” at the Root Cause level. Column 12 lines 43-57: the screen of FIG. 25 illustrates the preferred Near Root Cause level categories under the “Immediate Supervision” category. These are categories that indicate possible problems with procedures followed by or decisions made by the immediate supervisor of the operator who had fallen asleep. These categories are represented by a “Preparation” block 630, a “Selection of Worker” block 640, and a “Supervision During Work” block 650. For the causal factor of the current example, an operator may fall asleep because of lack of preparation for the shift or because of being on the job for too long at a single stretch. The operator may have been unprepared for the night shift due to a scheduling problem. For example, a scheduling problem may exist if the operator has not had enough sleep due to a work schedule that is not conducive to sleeping during off hours. column 13 lines 17-22: Thus, at this point, the system has completed the root cause analysis steps that are relevant to the “contract officer falls asleep” causal factor. In an actual use situation, the user might proceed at this time to the Corrective Action or Reports options, as described in detail hereinafter. Column 15 lines 47-51: he processor 20 preferably stores the contents of all of the data entry fields in the memory device 30, and associates these fields with the current corrective action as designated by its corrective action number. As a result at, Fig. 44 the overtime is reduced so that no operator monitor the temporary water treatment unit for more than 8 hours)
* While *
Paradies discloses above the hose rupture at 100 psig, and the ensuing high-temperature, low-pH water might, and
Pack similarly teaches at ¶ [0133] 7th sentence a significant stationarity test and, at
¶ [0269] 2nd sentence traches significant differences,
Asendorf mid-¶ [0036], ¶ [0126] discloses safety event statistics.
* Nevertheless*
Paradies/Pack/Asendorf as a combination does not explicitly recite:
- “wherein the input data is statistically significant” as explicitly claimed.
* However *
Rumi in analogous data acquisition and analysis for industrial processes teaches/suggests
- “wherein the input data is statistically significant” (Rumi ¶ [0085] 1st sentence: The minimum between conditions or faults time value can be automatically or manually found or determined by using statistical significance and statistical values to find the lowest possible time values between conditions or faults calculated from the data such as the, the lowest mode time value of that condition or fault occurring for that worker or a factor of the Weibull gamma time value (curve x-axis shift parameter, with the x-axis being time) of that condition or fault occurring by that worker).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Paradies/Pack/Asendorf “method” to have further included Rumi’s teachings in order to have better determined proficiency or training problem by comparison to experienced trained operators to reveal best practices for improvement for a general population of workers while, at same time helped verify training programs (Rumi ¶ [0086] in view of MPEP 2143 G and/or F). Predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Paradies column 18 lines 64-67 in view of Rumi at ¶ [0149]-¶ [0150].
Further, the claimed invention could have also been viewed as mere combination of old elements in similar field of endeavor dealing with data acquisition and analysis for industrial processes. In such combination each element would have merely performed same analytical, managerial or operational function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Paradies/Pack/Asendorf in view of Rumi, the to be combined elements would have fitted together, likes pieces of a puzzle in a logical, complementary, technologically feasible and/or econocmailly desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 24 Paradies/Pack/Asendorf/Rumi teaches all the limitations in claim 23 above.
Paradies does not recite: “wherein at least one of the sensor devices or sensing systems is coupled to at least one piece of industrial equipment associated with the industrial operation and configured to measure output(s) of the at least one piece of industrial equipment”.
Pack however in analogous art of industrial operation teaches or suggests: “wherein at least one of the sensor devices or sensing systems is coupled to at least one piece of industrial equipment associated with the industrial operation and configured to measure output(s) of the at least one piece of industrial equipment”. (Pack Fig.2 ¶ [0300] last sentence: temperature sensor 180, pressure sensor 190, volume flow sensor 195 are provided on residual pipe 170, as example of industrial equipment)
Rationales to have modified/combined Paradies/Pack were presented above and reincorporated.
Rationales to have modified/combined Paradies/Pack/Asendorf/Rumi were presented above.
Claim 25 Paradies/Pack/Asendorf/Rumi teaches all the limitations in claim 23 above.
Paradies does not explicitly teach: “wherein at least one of the sensor devices or sensing systems is configured to visually or audibly monitor the operators”.
Pack however in analogous art of industrial operation teaches or suggests: “wherein at least one of the sensor devices or sensing systems is configured to visually or audibly monitor the operators”. (Pack ¶ [0009] 2nd sentence: the processes are mapped & prepared in such a way that the model or optimization can be easily customized to specific needs of a process user. ¶ [0130] An output interface may be a physical interface (e.g. screen, a monitor) or a non-physical interface (e.g. function call, API) it may also be a combination of physical and non-physical interfaces. ¶ [0042] A measurable tag may be provided to each physico-chemical quantity on edges, when the physico-chemical quantity may be measured in the process network by a sensor, the tag may be measured, when the physico-chemical quantity may not measured, no tag may be associated or a tag un-known may be associated. Measured physico-chemical quantities may be provided directly from a sensor signal or may be derived from a sensor signal by expert knowledge or an expression. The physico-chemical quantity may be provided offline or online. In other words, measured means that the physico-chemical quantity, will be available in the process network either from inline measurements as data from sensor, offline data, e.g. lab data, expert knowledge or an expression. ¶ [0269] Additionally, the hybrid model may be used to detect anomalies based on real-time sensor data. In such a case the output of hybrid model may be compared to real-time sensor data. In case of significant differences, a notification or alarm may be triggered in the affected plants or may be used for root cause analytics. mid-¶ [0142] A signal may be generated, if the stationary test reveals that the current state of the process network is not stationary. This signal may be an alarm signal and may be provided to a plant network control center. The alarm signal may shut down of one plant or trigger shut down of the process network.
Rationales to have modified/combined Paradies/Pack were presented above and reincorporated.
Rationales to have modified/combined Paradies/Pack/Asendorf Rumi were presented above.
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
WO 2021156484 A2 teaching Process Network with several Plants
Ji et al, Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue, IEEE Transactions on Vehicular Technology, V53, N4, July 2004
US 20210209189 A1 ¶ [0059] 11th sentence: In an initialization step, the computer system 100 can retrieve 1050 the high alarm thresholds H1 to Hn and low alarm thresholds L1 to Ln associated with respective signals S1 to Sn of the technical system 200 from the alarm management system 300 and use such data for the following data processing steps to determine an indicator reflecting the technical status of entire technical system 200 based on the received signal data and alarm thresholds. ¶ [0073] Fig. 3B shows a view 360 with the aggregate abnormality indicator AAI for the technical system which is provided to the operator of the technical system. The view 360 further includes a visualization of the abnormality threshold AAI against which the AAI is compared. The AAI is computed on the base of the univariate distances of Fig.3A in accordance with formulas F5 of F6. The abnormality threshold AAT is predetermined so that an aggregate abnormality indicator value, when being below the abnormality threshold AAT, reflects normal operation of the technical system with a given probability p (e.g., p=0.95).
- US 20170200108 A1 ¶ [0070] 4th sentence: In addition, a travel-performance metric may be computed by comparing a worker's travel times for movements only to other workers that performed the same movements in order to make a fair comparison
- US 20080228549 A1 teaching Performance evaluation systems and methods
- US 20140032280 A1 [0010] 3rd - 4th sentences: Beyond time-of-day analysis, similar analysis can be performed to determine productivity patterns of the employee by days of the week, days of the month, and months and seasons of the year. In addition, changes in the employee's productivity over time can be measured
- US 20030135406 A1 ¶ [0042] 2nd-3rd sentences: making relative comparisons between different employees and when comparing the same shift or time period between days. Thus, if there is a large variation in the number of customers serviced by a given employee during a particular time shift when compared to other employees during the same time shift on other days, this may be an indication that disguised theft is occurring. ¶ [0047] In addition, in one embodiment of the invention an employee rating system is used based upon one or more of the above identified categories of information. In this embodiment, data from one employee's shift is compared against the same shift from the last ten days and variances in the employee's performance are calculated as against the ten shift average. The program allows tracking the percentage of variance from the norm for each category and then assigns points for each variance based upon a pre-defined points table. While any point system may be developed that is useful to identify significant variances, table 1, set forth below is one proposed point system.
- US 20020129139 A1 ¶ [0023] last sentence: in order to determine the performance of any given remote worker(s), at least one parameter representative of the performance of the worker(s) can be compared to the same or similar parameters for the same any other remote worker(s) at any other time. ¶ [0099] Next, the processor 14 in the server 3 generates time series data of joint positions for each joint based on the joint position data for the joint at each time (time series data generation operation) (ST104). Then, the processor 14 calculates a co-occurrence score of the working activity to be analyzed based on the time series data of joint positions for each joint (working activity evaluation operation) (ST105). Then, the processor 14 ranks the recorded working activities for the same task based on the co-occurrence score of each working activity (working activity ranking operation) (ST106).
- US 20170032473 A1 ¶ [0028] It should be appreciated that the rows of data displayed in the table 310 correspond to the same or nearly the same time during operation, allowing the operator to compare weather and other conditions to the corresponding operator performance criteria.
- US 7155400 B1 column 8 lines 52-61: The system 100 provides for configuring various QA parameters. The sampling rates, tolerance levels, quality assessment method, worker evaluation method are settable, e.g., as entries in the database 217. Some possible types of QA methods include: 1) sequential checking of results in which a task is dispatched to a worker and that worker's results are subsequently reviewed by another worker as a subsequent task, and 2) a comparison of two independent results in which the same task is dispatched to two different workers and the results compared.
- US 20170200109 A1 ¶ [0039] Providing the plurality of reports allows an operator to see how the operator's performance compares to other operators within the same mine or different mines.
- US 20200184404 A1 ¶ [0028] 5th sentence: The fuel performance trigger cluster 304 may further include an engine idle trigger which is compared with a historic trip data for the same or different operator to determine the norm occurrences of engine idle for similar trip types operating the vehicle 102. Similar to the engine idle trigger, the trigger for usage of AC may be compared with a historic trip data for the same or different operator to determine the norm of AC usage for similar outside temperature
- US 20170357923 A1 ¶ [0062] 2nd sentence: comparing the information regarding the upcoming activity with information regarding past activities and looking for similar events, i.e., events with the same employees, employees with similar levels of experience, workers' consumer data, same or similar activity (such as laying a cement foundation), similar weather conditions (amount and type of precipitation, temperature, lighting conditions and/or wind speed), similar working materials and/or using the same or similar equipment.
- US 20160078391 A1 mid-¶ [0060] operator performance report 110 illustratively includes a composite score 190 generated by composite score generator 182 and the recommendations 192 generated by recommendation engine 194. It can include comparisons of the current operator against leading operators in the same context, or other information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/OCTAVIAN ROTARU/
Primary Examiner, Art Unit 3624 A
April 25th, 2026
1 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
2 Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018).
3 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
4 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
5 Assuming the “ARIMA” - “autoregressive integrated moving average” - of claim 13 is computer implemented.
6 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
7 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
8 Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)
9 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
10 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
11 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
12 MPEP 2111.04 I
13 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93
14 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015)
15 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts");
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
16 Flook, 437 U.S. at 594, 198 USPQ2d at 199;
Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
17 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
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