DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 08/14/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because FIG.1 is missing labels and/or units to provide explanation for vertical axes on three shown graphs. Examiner notes in specification [0008] mentions content illustrated in FIG. 1 “PUE metric is stable over the observed time period”, however three different graphs are shown, each with differing numerical values on the vertical axis. It is not clear the meaning of the numerical values, and specification does not provide sufficient clarity.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Specification [0007] recites:
“It is straightforward to observe DC-based efficiency metrics (e.g., PUE,)”
The term PUE is not previously mentioned or defined. For examination, the term is reasonably interpreted, based on context of claimed invention, to mean “Power Usage Effectiveness”, a metric for assessing energy efficiency. The acronym should be defined prior to its use for clarity.
Appropriate correction is required.
Claim Objections
Claims 1, 19 objected to because of the following informalities:
Claim 1 recites: “obtaining all new ones of the sensor measurements”. The language shown in bold is not clear, but will be assumed for examination to mean “real-time” or “updated” data acquisition from sensors.
Claim 19 recites “evaluate and observing” in the last limitation statement. Examiner suggests that the phrase be corrected to read (emphasis added) “evaluate and observe”.
Appropriate correction is 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.
Claim(s) 1-5, 10-14, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 10, and 19 recite (emphasis added): “detect[ing] changes at each time step”. Examiner notes term in bold is not previously recited in claims, and not defined. This renders Claim 1 as indefinite. For examination purposes Examiner assumes limitation to mean that sensor measurements as recited in previous sentence is time dependent. This language should be made clear to detail how time steps are involved in process as described by previous sentence.
Claims 2 and 11 recites (emphasis added): “for each group identifier”: Examiner notes term in bold is not previously recited in claims and not defined. This renders Claim 2 as indefinite. Examiner finds mention of the term “group identifier” in [0037] with reference to “managed by the system” and “sensor measurements that below to a group identifier” but without further explanation, the term remains undefined. The “group identifier” is recited as related to “PCA Modeler 210” [0037, FIG. 3; [0068], FIG. 15), “Change detector 220” ([0040], FIG. 4), and “change date handler table” ([0044-45], FIG. 5). Examiner notes the term “group ids” is found in [0053]For examination purposes, the term will be interpreted to mean a spatial location designation, with guidance using FIG. 5, where Col.2 lists the term “Group ID”, with column values listed as “Room 1”, and “Room 2”.
Claims 3-4, and 12-13 recite (emphasis added): “determining whether there is a change date directly continuing or in close proximity from a previous change date”. Examiner note the term in bold is a relative term which renders the claim indefinite. The term “close proximity” is not defined by the claim, and the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner finds that that the use of the term “close proximity” renders the ability to understand the meaning of “detecting the changes at each time step” and “change date” indefinite.
Claim 5 and 14 recite “determining score metrics based a division of related data”. Examiner finds the term “division”, not appearing in Claim 1 nor Claim 10 to which Claim 5 and 14 have dependency, respectively, is undefined. It is not clear if this is meant as a mathematical operation or a qualitative separation. Likewise the term “related data” does not appear in Claim 1, and is undefined. The intention and meaning of the limitation language in the context of a multiple sensor data acquisition for model development is not clear.
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 1-5, 7-14, 16-20 rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. These claims fall into statutory categories as set forth in 35 U.S.C. 101 (See MPEP § 2106.03), as discussed in detail below.
Claim 1, 10 and 19 are considered to be in a statutory category using Step one of eligibility analysis. (MPEP § 2106.03).
Evaluation under Step 2A, Prong One, and applying broadest reasonable interpretation, reveals the limitation recites a judicial exception of abstract idea in the “Mathematical Concept” grouping. (See MPEP §2106.04(a)(2), subsection I.)
Claim 1, and similarly claim 10 and 19, recites: “
executing an incremental Principal Component Analysis (PCA) modeler to build a PCA model for sensor measurements associated with one or more cooling devices of a location to be monitored;
detecting changes at each time step based on a change in a number of principal components or for when a reconstruction error exceeds a threshold; and
evaluating and observing the changes to generate feedback”
Examiner points to text emphasized in bold. These terms describe explicitly mathematical operations or calculations produce numerical (quantitative) values or qualitative results. Claim limitation language with these terms is interpreted as nothing more than a series of mathematical calculations using input data. This interpretation and conclusion is supported by further review of the specification, for example, specification in at least [0071]-[0077], where in details of mathematical operations and calculations as performed computational are presented. Specifically, FIG. 8 depicts explicitly the mathematical operations involved in “Change calculator” process.
In further evaluation of eligibility, Step 2A, Prong two was applied. The limitations of Claim 1 and 10 recite no additional elements that integrate the judicial exception into practical application. Claim 19 recites the additional element of a processor which is a generic computer component which does not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 5, 7, 14, 16 further limit the abstract ideas with including additional limitations to integrate the abstract idea into a practical application.
Claims 2-4 and 11-13-contain additional limitations referring to necessary data gathering or extra solution activity, and storing or updating models or databases . which are not sufficient to integrate the abstract idea into a practical application.
Claims 8-9 and 17-18 recites a graphical user interface configured to intake input and display is considered a generic computer element which does not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 6 and 15 integrate the abstract idea into a practical application and are not rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3- 7, 10, 12-16, 19 are rejected under 35 U.S.C. 103(a) as being unpatentable over MISHIN (“Real time change point detection by incremental PCA in large scale sensor data” 2014 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2014, pp. 1-6. (Year: 2014)) in view of BHUSHAN (Bhushan, et al., “INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-4/W2, 2015 International Workshop on Spatiotemporal Computing, 13–15 July 2015, Fairfax, Virginia, USA), and further in view of HAMILTON (US 20170366414 A1).
With respect to Claim 1, 10, and 19 MISHIN teaches:
a processor,
(Above, parallel limitation, Claims 1 and 10 MISHIN teaches a computational system for carrying out incremental PCA for real-time sensor based measurements, Abstract, Pg1,Col1.)
executing an incremental Principal Component Analysis (PCA) modeler to build a
PCA model for sensor measurements associated with one or more cooling devices of a location to be monitored; (MISHIN is in same technical field and teaches incremental PCA model applied to temperature sensors in a data center, Abstract: “work with the deployment of a 600-piece temperature sensor network, data harvesting framework, and real time analysis system in a Data Center…Sensor data streams were processed by robust incremental PCA”; MISHIN teaches sensor measurements associated with thermal control and cooling equipment, Pg1,Col1, I. Introduction: “controlling the layout for maximizing the amount of hardware that can be used safely without creating a thermal overload for the hardware or the DC AC systems”; Examiner notes reference “DC” refers to Data Center, as in instant application, and interpretation of claim limitation language “cooling devices” to be analogous to reference of “AC systems”, where reference is to “air conditioner”.)
detecting changes at each time step (MISHIN teaches time step analysis method, Pg1,Col2,I. Introduction: “developing hardware and software environment to process our data, and explored the possibilities of automatic detection of changepoints or atypical time series events in very high dimensional sensor data streams”, and .Pg.5,Fig.4: “Points represent time steps”, and PG.4,Col1: “input data stream can be filtered by a specific time range”; MISHIN teaches monitoring for change events, Pg.4, Col2, B. Changepoint Event detection: “identifying a transition event in real time, sample data should be aggregated in a sliding window long enough to cover the changepoint event and some typical, steady state data before and after the event
and evaluating and observing the changes (MISHIN teaches evaluation of observe time-dependent sensor data via PCA analysis, as above, Pg.4, Col2.)
However, MISHIN is silent to the language of:
[detecting changes at each time step] based on a change in a number of principal components or for when a reconstruction error exceeds a threshold;
[and evaluating and observing the changes] to generate feedback regarding the one or more cooling devices.
BHUSHAN teaches:
[detecting changes at each time step] based on a change in a number of principal components or for when a reconstruction error exceeds a threshold; (BHUSHAN in same technical field, with focus on incremental PCA for time dependent sensor data management and analysis: Abstract: “address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations.” and Pg2Col1:” focus on Incremental PCA” and Pg69,Fig.1 with Pg70,Col1: “data and its mean are shown in Figure 1. In this figure, time is on the x-axis and values corresponding to all sensors are on the y-axis. ; BHUSHAN analysis to determine principal components based on received data, Pg1Col2: “characteristics may change with time… (i) mean and covariance, and (ii) correlation structure which results in increase or decrease in number of principal components”; Examiner notes BHUSHAN also discloses reconstruction error analysis and use of thresholding for change detection, Pg.70,Col1: “we compare them based on number of PCs required in each method, reconstructed values, and reconstruction error obtained from each method”, and generally Pg.68-29, §3 and Pg.70,§5.2.2 for discussion of thresholding methods.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify MISHIN to include [detecting changes] based on a change in a number of principal components or for when a reconstruction error exceeds a threshold, such as that of BHUSHAN.
One of ordinary skill would be motivated to modify MISHIN to include [detecting changes] based on a change in a number of principal components or for when a reconstruction error exceeds a threshold, as taught by BHUSHAN because it would be understood as a way to fully utilize Principal Component Analysis to optimize sensor performance as related to dynamic (time dependent) data by refining models recursively in real time. One of ordinary skill would be motivated to use the detailed incremental PCA technique for data analysis and consideration of optimal number of principal components based on each data set received, as taught by BHUSHAN to streamline the data reduction reconstruction and optimization resulted in measurable and significant energy savings.
However, MISHIN as modified by BHUSHAN, as taught above, is silent to the language of:
[and evaluating and observing the changes] to generate feedback regarding the one or more cooling devices.
HAMILTON teaches
[and evaluating and observing the changes] to generate feedback regarding the one or more cooling devices. (HAMILTON is in same technical field, teaching use of PCA modeler, acquiring sensor data, with applications to cooling devices: Abstract: “building management system…includes a communications interface, a principal component analysis (PCA) modeler…receive samples of the monitored variables from the connected equipment…construct PCA models“ and [0040]: “building management system (BMS)…includes sensors,” and [0055] “includes an HVAC system …a plurality of HVAC devices (e.g., heaters, chillers…)”; HAMILTON teaches evaluation and observation of changes and generation of feedback on user interface, [0137]: “When the normal state changes, predictive diagnostics system 502 may switch to the PCA model representing the new normal state”, [0094]: “Integrated control layer 418 can be configured to provide feedback to demand response layer” and [0097]: “configured to output a specific identification of the faulty component or cause of the fault”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN as modified by BHUSHAN, as taught above, to include the step of generating feedback regarding the one or more cooling devices after performing evaluation and observing the changes, such as that of HAMILTON.
One of ordinary skill would be to further modify MISHIN as modified by BHUSHAN, as taught above, to include the step of generating feedback regarding the one or more cooling devices after performing evaluation and observing the changes, as taught by HAMILTON because it would be understood as an efficient method of optimizing a system based on real-time data streaming. One of ordinary skill would understand the advantage of combining the idea of generating feedback as taught by HAMILTON with the incremental PCA method of MISHIN as modified by BHUSHAN to produce a more reliable system of equipment monitoring and management.
With respect to Claim 3 and12, MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations of claims 1 and 10.
MISHIN is silent to the language of:
wherein the detecting the changes at the each time step comprises:
for a flag associated with the PCA model indicative of the change in the number of principal components having an increase:
determining whether there is a change date directly continuing or in close
proximity from a previous change date;
for the determining indicative of the change date directly continuing or in close proximity from the previous change date,
updating a change date database entry associated with the PCA model
with a new change date and increment the time steps passed since
changed for the PCA model;
and for the determining indicative of the change date not directly continuing or in close proximity from the previous change date,
adding a new change date database entry associated with the PCA
model.
BHUSHAN further teaches, as above:
wherein the detecting the changes at the each time step comprises: for a flag associated with the PCA model indicative of the change in the number of principal components having an increase: (BHUSHAN teaches dynamic data impacts number of principal components, as above, Pg1Col2)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN, BHUSHAN, and HAMILTON as taught above to include wherein the detecting the changes at the each time step comprises: for a flag associated with the PCA model indicative of the change in the number of principal components having an increase, such as that further disclosed by BHUSHAN.
One of ordinary skill would be motivated to further modify MISHIN as modified by BHUSHAN and HAMILTON as taught above to include wherein the detecting the changes at the each time step comprises: for a flag associated with the PCA model indicative of the change in the number of principal components having an increase, such as that further as taught by BHUSHAN because it would be understood as a logical step to include in the incremental PCA method taught by MISHIN as modified and taught above, and would serve to improve and make the method more efficient by avoiding unnecessary re-calculation of principal components, and provide focus on global shifts in variance rather than individual or spurious behavior.
MISHIN and BHUSHAN is silent to the language of:
determining whether there is a change date directly continuing or in close
proximity from a previous change date;
for the determining indicative of the change date directly continuing or in close proximity from the previous change date,
updating a change date database entry associated with the PCA model
with a new change date and increment the time steps passed since
changed for the PCA model;
and for the determining indicative of the change date not directly continuing or in close proximity from the previous change date,
adding a new change date database entry associated with the PCA
model.
HAMILTON further teaches:
determining whether there is a change date directly continuing or in close proximity from a previous change date; (Examiner notes interpretation of claim language as above; HAMILTON teaches use of a temporal “proximity metric” with thresholding analysis to determine temporal “proximity” between change events, FIG. 19, and [0037]: “FIG. 19 is a graph of a proximity metric as a function of time which indicates the proximity of the samples of the monitored variables to an identified operating state of the connected equipment”)
and for the determining indicative of the change date directly continuing or in close proximity from the previous change date, (HAMILTON teaches use of proximity metric, as above, to determine importance of observed change, [0265]: “FIG. 19, the proximity metric pj(x) crosses the proximity threshold”.)
updating a change date database entry associated with the PCA model with a new change date and increment the time steps passed since changed for the PCA model; (HAMILTON teaches updating model with changes to monitored variables, [0039]: “FIG. 21 is a flow diagram, of a proximity determination technique which can be used by the predictive diagnostics system to determine the proximity of a sample of the monitored variables to an identified operating state of the connected equipment, according to some embodiments” and FIG.11, with [0196]: “Model updater 1140 can be configured to update the PCA models 1130 with new samples of the monitored variables”.)
and for the determining indicative of the change date not directly continuing or in close proximity from the previous change date, adding a new change date database entry associated with the PCA model. (HAMILTON teaches as above, monitoring a proximity metric to determine time between change events, and generally updated model regarding change events, FIG. 19 with [0265]; Examiner notes interpretation of claim language as above.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN, BHUSHAN, and HAMILTON to include determining whether there is a change date directly continuing or in close proximity from a previous change date; and for the determining indicative of the change date directly continuing or in close proximity from the previous change date, updating a change date database entry associated with the PCA model with a new change date and increment the time steps passed since changed for the PCA model; and for the determining indicative of the change date not directly continuing or in close proximity from the previous change date, adding a new change date database entry associated with the PCA model, such as that further disclosed by HAMILTON.
One of ordinary skill would be motivated to further modify MISHIN, BHUSHAN, and HAMILTON to include the steps taught by HAMILTON because it would be understood as an improved way to organize and store information made available by the incremental PCA analysis technique using real-time data taught by MISHIN, as modified above by BHUSHAN and HAMILTON. One of ordinary skill would view the detailed categorization of timing as further taught by HAMILTON to be advantageous that would result in a reasonable expectation of success to result in more efficient data handling, and to reduce unnecessary computation.
With respect to Claim 4, 13, MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations of claims 3 and 12.
MISHIN is silent to the language of:
wherein the detecting the changes at the each time step comprises: for the flag associated with the PCA model not indicative of the change in the number of principal components as having the increase:
calculating a reconstructed value with the PCA model for the each time step;
calculating the reconstruction error from the reconstructed value;
for the reconstruction error exceeding the threshold:
determining whether there is the change date directly continuing or in close proximity from the previous change date;
for the determining indicative of the change date directly continuing or in close proximity from the previous change date, updating the change date database entry associated with the PCA model with the new change date and increment the number of time steps passed since changed for the PCA model;
and for the determining indicative of the change date not directly continuing from the previous change date, adding the new change date database entry associated with the PCA model.
BUSHAN further teaches:
wherein the detecting the changes at the each time step comprises:
for the flag associated with the PCA model not indicative of the change in the number of principal components as having the increase: (As above, BHUSHAN teaches consideration and determination of increase in number of principal components, Pg1,Col.2)
calculating a reconstructed value with the PCA model for the each time step;
calculating the reconstruction error from the reconstructed value; (BHUSHAN discloses a comparison between two analysis methods using incremental PCA, with calculations for each method, Pg70,Col1,§5: “we compare them based on number of PCs required in each method, reconstructed values, and reconstruction error obtained from each method.”; One of ordinary skill would understand relationship between reconstruction error and reconstruction value as known in the art in general PCA analysis method.)
for the reconstruction error exceeding the threshold: (BHUSHAN teaches threshold-base evaluation of reconstruction error, Pg69, Col1: “threshold on SRE [squared reconstruction error] can also be computed using mean of previous SRE values”
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN, BHUSHAN, and HAMILTON to include wherein the detecting the changes at the each time step comprises: for the flag associated with the PCA model not indicative of the change in the number of principal components as having the increase; and calculating a reconstructed value with the PCA model for the each time step; calculating the reconstruction error from the reconstructed value; and to use the analysis step to consider whether a reconstruction error exceeds the threshold, such as that further disclosed by BHUSHAN.
One of ordinary skill would be motivated to further modify MISHIN in view of BHUSHAN, and further in view of HAMILTON to include the analysis steps as taught in detail described above, such as that further as taught by BHUSHAN because it would be understood as an advantageous and improved method for sorting, storing and considering time-dependent data. One of ordinary skill would be familiar with the generally known incremental PCA method of calculation reconstructed data and would understand doing such calculations at each time interval would be a logical way to perform the incremental / recursive updates to avoid full re-calculation of a full model. One of ordinary skill would understand the value of using a thresholding technique taught by BHUSHAN in the incremental PCA method as taught by MISHIN, as modified, to be an efficient way to may timely predictions and decisions regarding equipment performance.
MISHIN and BHUSHAN is silent to the language of:
determining whether there is the change date directly continuing or in close proximity from the previous change date;
for the determining indicative of the change date directly continuing or in close proximity from the previous change date, updating the change date database entry associated with the PCA model with the new change date and increment the number of time steps passed since changed for the PCA model;
and for the determining indicative of the change date not directly continuing from the previous change date, adding the new change date database entry associated with the PCA model.
HAMILTON further teaches:
determining whether there is a change date directly continuing or in close proximity from a previous change date; (Above, parallel limitation, Claim 3, HAMILTON teaches use of a temporal “proximity metric” with thresholding analysis to determine temporal “proximity” between change events, FIG. 19, and [0037])
for the determining indicative of the change date directly continuing or in close
proximity from the previous change date, updating a change date database entry associated with the PCA model with a new change date and increment the time steps passed since changed for the PCA model; (Above, parallel limitation, Claim 3, HAMILTON teaches this limitation FIG. 19 and [0265] and HAMILTON teaches updating model with changes to monitored variables, [0039])
and for the determining indicative of the change date not directly continuing or in close proximity from the previous change date, adding a new change date database entry associated with the PCA model.(Above, parallel limitation, Claim 3, HAMILTON teaches as above, monitoring a proximity metric to determine time between change events, and generally updated model regarding change events; Examiner notes interpretation of claim language as above.)
It would have been obvious to one of ordinary skill in the art before effective filing
date of the claimed invention to further modify MISHIN in view of BHUSHAN, and further in view of HAMILTON to include determining whether there is a change date directly continuing or in close proximity from a previous change date; for the determining indicative of the change date directly continuing or in close proximity from the previous change date, updating a change date database entry associated with the PCA model with a new change date and increment the time steps passed since changed for the PCA model; and for the determining indicative of the change date not directly continuing or in close proximity from the previous change date, adding a new change date database entry associated with the PCA model, such as that further disclosed by HAMILTON.
One of ordinary skill would be motivated to further modify MISHIN in view of BHUSHAN, and further in view of HAMILTON to include the steps described above, such as that further as taught by HAMILTON because it would be understood that the combination of the explicit detail of evaluating change events based on temporal proximity would be a way to improve the time-dependent data streaming analysis method of MISHIN, as modified previously by BHUSHAN and HAMILTON an lead to an a more accurate and reliable way to discern actual fault-like events and generally know changes to a system, as is taught by HAMILTON.
With respect to Claim 5, 14 MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations of claims 1 and 10.
MISHIN further teaches:
wherein the evaluating and observing the changes regarding the one or more cooling devices comprises: (MISHIN teaches evaluation of observe time-dependent sensor data related to cooling devices, using PCA analysis, as above, Pg.4, Col2.)
for each of the changes: determining time steps passed since the PCA model changed from a change date database entry; (Above, parallel limitation, Claim 1, MISHIN teaches time step analysis method, Pg1,Col2,I. Introduction, Pg.5,Fig.4; and monitoring for change events, Pg.4, Col2, B.)
[evaluation and observation of] data from before the change date and after the change date (Above, parallel limitation, Claim 1, MISHIN teaches the importance of collecting data before and after a change event, Pg.4, Col2, B.: “sample data should be aggregated in a sliding window long enough to cover the changepoint event and some typical, steady state data before and after the event”
MISHIN is silent to the language of:
[wherein the evaluating and observing] the changes to generate the feedback regarding the one or more cooling devices comprises:
for the number of time steps passed meeting a step threshold: determining score metrics based a division of related data from change date
and generating the feedback for the score metrics exceeding a score threshold.
HAMILTON further teaches:
[wherein the evaluating and observing] the changes to generate the feedback regarding the one or more cooling devices comprises: (Above, parallel limitation for Claim 1, HAMILTON teaches evaluation and observation of changes and generation of feedback on user interface, [0137], [0094], and [0097])
for the number of time steps passed meeting a step threshold: determining score metrics based a division of related data from change date (Examiner notes interpretation of claim limitation language “division” as discussed above.; HAMILTON teaches a score metric method, as above, for example, [0005] “fault predictor is configured to identify at least one of the operating states as a faulty operating state, generate a proximity metric indicating the proximity of the new sample to the faulty operating state, and predict the fault occurrence using a value of the proximity metric.”;
and generating the feedback for the score metrics exceeding a score threshold. (Above, HAMILTON teaches scoring metric, and generation of feedback of analysis results, [0137], [0094], and [0097])
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN in view of BHUSHAN, and further in view of HAMILTON to include the steps of [wherein the evaluating and observing] the changes to generate the feedback regarding the one or more cooling devices comprises: for the number of time steps passed meeting a step threshold: determining score metrics based a division of related data from change date and generating the feedback for the score metrics exceeding a score threshold, such as that further disclosed by HAMILTON.
One of ordinary skill would be motivated to further modify MISHIN in view of BHUSHAN, and further in view of HAMILTON to include the steps above, such as that further as taught by HAMILTON because it would be understood as a way to maximize impact and efficiency of applying a threshold technique to analyze time-dependent data within the incremental PCA method taught by MISHIN, as modified above. One of ordinary skill would understand and be motivated by the expectation to improve reliability and allow for consistent evaluation across multiple data sets that the steps further taught by HAMILTON would provide when incorporated into an incremental PCA analysis method for real-time data from sensors.
With respect to Claim 6 and 15, MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations of claims 1 and 10.
MISHIN is silent to the language of:
wherein the feedback comprises active recommendations for adjusting the one or
more cooling devices.
HAMILTON teaches:
wherein the feedback comprises active recommendations for adjusting the one or
more cooling devices. (As above, HAMILTON teaches generation of feedback output from PCA-based data analysis, [0137], [0094], and [0097]; HAMILTON teaches feedback related to system adjustments, with examples found in [0087]: “integration layer 420 can be configured to manage communications between BMS controller 366 and building subsystems 428… provide output data and control signals to building subsystems”; [0094] “control layer 418 can be configured to provide feedback to demand response layer”; and [0109]: “control algorithms…to control a variable state or condition (e.g., temperature, humidity, airflow, lighting, etc.) in or around building”; and [0201]: “Building controller 1144 can automatically adjust the efficiency or expected performance of the connected equipment in an automated control algorithm”)
It would have been obvious to one of ordinary skill in the art before effective filing
date of the claimed invention to further modify MISHIN, BHUSHAN, and HAMILTON to include wherein the feedback comprises active recommendations for adjusting the one or more cooling devices, such as that further disclosed by HAMILTON.
One of ordinary skill would be motivated to further modify MISHIN in view of BHUSHAN, and further in view of HAMILTON to include wherein the feedback comprises active recommendations for adjusting the one or more cooling devices, such as that further as taught by HAMILTON because it would be an obvious way to use the robust analysis of the incremental PCA method for sensor data analysis. One of ordinary skill would understand the implicit suggestion of MISHIN, as modified above, for output, when combined with the explicit feedback taught by HAMILTON would better serve for an efficient and reliable monitoring and control method.
With respect to Claim 7 and 16, MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations of claims 1 and 10.
MISHIN further teaches
wherein the location to be monitored is a data center server room. (MISHIN teaches application of PCA to manage data center environment, Abstract: output of the signal processing system allows us to better understand the temperature patterns of the DataCenter’s inner space… changepoint events…optimizing the temperature control efficiency”)
Claims 2 and 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over MISHIN, BHUSHAN, and HAMILTON further in view of LI 2000 (LI, et al., “Recursive PCA for adaptive process monitoring”, Journal of Process Control, Volume 10, Issue 5, October 2000, Pages 471-486.)
With respect to Claim 2 and 11, MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations of claims 1 and 10.
MISHIN, is silent to the language of :
wherein the executing the incremental PCA modeler comprises, for each group identifier:
obtaining all new ones of the sensor measurements associated with the each group identifier for the each time step;
for the PCA model not being available for the each group identifier:
creating the PCA model for the group identifier from the new ones
of the sensor measurements;
and storing the created PCA model with a flag indicative of there not being the
change in the number of principal components;
for the PCA model being available for the each group identifier:
updating the PCA model based on the new ones of the sensor measurements;
determining whether there is an increase in the principal components for the
updated PCA model from the PCA model;
and storing the updated PC model with the flag indicating whether there is the
increase in the principal components from the determination.
BHUSHAN further teaches:
wherein the executing the incremental PCA modeler comprises, or each group identifier: obtaining all new ones of the sensor measurements associated with the each group identifier for the each time step; (BHUSHAN teaches application of incremental PCA for real time measurements and data acquired at set intervals: Pg.69,Col1: “appropriate IPCA method is recommended for detecting outliers in spatiotemporal datasets”; BHUSHAN teaches sensor sets at known locations, Pg.67Col2-Pg.68Col.1: “apply these methods to two environmental datasets each consisting of a set of geographically distributed sensors”, and Pg.69,Col1,§4: “objective is to monitor the series and detect point outliers in real-time, i.e., upon arrival of data” and “Pg70,Col1,SS5.1: “Each sensor measures air pollutants at regular intervals and sends the measurement to the central data repository”. Examiner notes interpretation of terms as described above, i.e., “all new ones” to mean data, as acquired in real time or streaming data from sensor measurements; term “time step” to be analogous to reference of “intervals”; and “group identifier” as discussed above to generally mean a set of data associated with a known spatial location)
for the PCA model being available for the each group identifier: updating the PCA model based on the new ones of the sensor measurements; (BHUSHAN teaches real-time updates, Pg70,col.2SS5.3.1: “COVF updates the vectors at each time instance…update the training model in every iteration”.)
determining whether there is an increase in the principal components for the updated PCA model from the PCA model; (BUSHAN teaches variation and evaluation of changing number of principal components with iterative updates, Pg.67,Col.2: “data characteristics may change with time (Li et al., 2000) (i) mean and covariance, and (ii) correlation structure which results in increase or decrease in number of principal components”; Examiner notes reference to LI 2000, explicitly teaches updating model, Pg.473,Col.1SS2.2: “If a block of process data has been used to build an initial PCA model, we need to update the PCA model when a new block of data becomes available”; Examiner notes LI 2000 also discloses this limitation.)
and storing the updated PCA model with the flag indicating whether there is the increase in the principal components from the determination. (BHUSHAN teaches specific method for PCA model updates using incremental method, Pg.67,Col.2: “variants update the models incrementally”; as above BHUSHAN teaches increase (or decrease) in number of principal components based on received data set(s), Pg.67,Col.2; Examiner notes interpretation of limitation language “flag” using broadest reasonable interpretation to mean simple a stored piece of information, analogous to reference term “update” to include new information.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN, BHUSHAN, and HAMILTON as taught above to include wherein the executing the incremental PCA modeler comprises, or each group identifier: obtaining all new ones of the sensor measurements associated with the each group identifier for the each time step; for the PCA model being available for the each group identifier: updating the PCA model based on the new ones of the sensor measurements; determining whether there is an increase in the principal components for the updated PCA model from the PCA model; and storing the updated PCA model with the flag indicating whether there is the increase in the principal components from the determination, such as that further disclosed by BHUSHAN.
One of ordinary skill would be motivated to further modify the method of MISHIN as modified by BHUSHAN and HAMILTON as taught above to include the steps as detailed above, as further taught by BHUSHAN because it would be understood that these detailed analysis steps would improve the incremental PCA analysis method as taught by MISHIN, as modified above, to result with a reasonable expectation of success in a more robust, accurate and repeatable method for real-time data streaming applications. One of ordinary skill would understand that using consistently updated sensor data to refine and update existing PCA system models would be an improvement for more accurately and reliably identifying system faults and/or making reliable predictions about future system events.
MISHIN, BHUSHAN, and HAMILTON is silent to the language of:
for the PCA model not being available for the each group identifier:
creating the PCA model for the group identifier from the new ones of the sensor measurements;
and storing the created PCA model with a flag indicative of there not being the change in the number of principal components;
LI 2000 teaches:
for the PCA model not being available for the each group identifier: creating the PCA model for the group identifier from the new ones of the sensor measurements; (Examiner notes interpretation of this limitation as discussed above. LI 2000 is related technical field with focus on PCA, with application to time varying data analysis, and using data from sensors, Pg.471,Col2: “Industrial processes commonly demonstrate slow time-varying behaviors…sensors” and Pg.472,Col1: “two recursive PCA (RPCA) algorithms to adapt for normal process changes”; Examiner notes reference term “recursive” is closely aligned with “incremental”, which would be understood by one of ordinary skill in the art.; LI 2000 teaches initialization of a PCA model if data is received for which no previous model exists, Pg.483Col1-2, “case of limited samples to initialize the monitoring procedure… initial PCA model is built using the first 50 wafers”)
and storing the created PCA model with a flag indicative of there not being the change in the number of principal components; LI 2000 teaches implicitly that initial model is stored, as it is subsequently updated, Pg.473,Col1: “If a block of process data has been used to build an initial PCA model, we need to update the PCA model when a new block of data becomes available.”; Examiner asserts storing a created model would be understood and generally known to one of ordinary skill in the art as a necessary step in executing an iterative modeling process as that of incremental PCA.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the method of MISHIN, BHUSHAN ,and HAMILTON as taught above to include the detailed steps above in the case where data is received, and no pre-determined PCA model exists, such as that of LI 2000.
One of ordinary skill would be motivated to further modify MISHIN, BHUSHAN, and HAMILTON as taught above to include the steps described above, as taught by LI 2000 because it would be understood, and as suggested explicitly by LI 2000, that if there is no existing model, it would be required to develop and initialize a model based on a particular batch of data as a starting point. One of ordinary skill would see this as an advantageous combination with the incremental PCA method of MISHIN, as modified above, to broaden the application to allow for receiving data sets from locations that may not have been either known, or simply not included in the original set of trained PCA models for the building/room or set of equipment sensors.
Claims 8-9, and 17-18 are rejected under 35 U.S.C. 103(a) as being unpatentable over MISHIN, BHUSHAN, and HAMILTON, further in view of ENVER (US 20170102694 A1).
With respect to Claim 8 and 17, MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations of claims 1 and 10.
MISHIN and BHUSHAN is silent to the language of:
providing a graphical user interface.
HAMILTON further teaches:
providing a graphical user interface (HAMILTON teaches graphical user interface , [0077]: “Client device 368 can include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces”)
configured to intake input regarding selection of sensors for providing the sensor measurements, (HAMILTON teaches input from devices and user, [0202] “receive inputs from sensory devices (e.g., temperature sensors, pressure sensors, flow rate sensors, humidity sensors, electric current sensors, cameras, radio frequency sensors, microphones, etc.), user input devices (e.g., computer terminals, client devices, user devices, etc.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further MISHIN, BHUSHAN, and HAMILTON as taught above, to include providing a graphical user interface configured to intake input regarding selection of sensors for providing the sensor measurements, such as that further disclosed by HAMILTON.
One of ordinary skill would be motivated to further modify MISHIN as modified by BHUSHAN and HAMILTON as taught above, to include providing a graphical user interface configured to intake input regarding selection of sensors for providing the sensor measurements, as further taught by HAMILTON because it would be understood as a way to allow for improved efficiency and timely feedback when dealing with real-time data for a multi-sensor application. One of ordinary skill would see the advantage of incorporating graphical interface to facilitate user interaction, and to allow for timely user intervention when needed.
MISHIN, BHUSHAN and HAMILTON as taught above, is silent to the language of:
the graphical user interface configured to display the feedback and the changes occurring.
ENVER teaches:
the graphical user interface configured to display the feedback and the changes occurring. (ENVER is in same technical field, [0002]: “relates generally to process plants and to process control systems, and [0006]: “data analytics tools utilize principal component analysis (PCA)”; ENVER teaches feedback using graphical user interface, [0016]: “includes a user interface displaying continuous output generated in real-time resulting from the data analytics module operating in real-time on the continuous data stream”
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN, BHUSHAN, and HAMILTON as taught above, to include the graphical user interface configured to display the feedback and the changes occurring, such as of ENVER
One of ordinary skill would be motivated to further modify MISHIN in view of BHUSHAN, and further in view of HAMILTON to include the graphical user interface configured to display the feedback and the changes occurring, as taught by ENVER because it would be seen as an obvious extension of the real-time interaction of user with analysis results. One of ordinary skill would understand that while MISHIN, in combination with BHUSHAN and HAMILTON, as taught above did not explicitly teach feedback regarding changes on a graphical interface, providing such feedback to a user would be an advantage to result in a more efficient system, and ultimately for improved management of environment and/or equipment settings.
With respect to Claim 9 and 18, MISHIN in view of BHUSHAN, and further in view of HAMILTON teaches the limitations claims 8 and 17.
MISHIN further teaches:
wherein the graphical user interface is configured to intake the input regarding grouping granularity to produce one or more group identifiers. (MISHIN teaches granularity for data acquisition, Pg.5,Col2,V.Conclusion: “developed a robust sensor network environment to provide high granularity real life temperature sensor data streams to study DC thermal behavior patterns”)
MISHIN, BHUSHAN and HAMILTON as taught above, is silent to the language of:
user interface is configured to intake the input regarding sampling frequency for the sensor measurements
ENVER teaches:
user interface is configured to intake the input regarding sampling frequency for the sensor measurements (ENVER teaches user input for control of data sampling, Abstract: “system includes a user interface having a set of user controls” and FIG 6D, with [0375]: “query may be received from a user via direct input”, where query is instruction of data acquisition which would include sampling frequency, as found in [0372]: “sampling rate”, where one of ordinary skill would understand inverse relationship between “frequency” and “rate”.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify MISHIN as modified by BHUSHAN, and further modified by HAMILTON as taught above, to include a user interface configured to intake the input regarding sampling frequency for the sensor measurements , such as that of ENVER.
One of ordinary skill would be motivated to further modify MISHIN as modified by BHUSHAN, and further modified by HAMILTON as taught above, to include a user interface configured to intake the input regarding sampling frequency for the sensor measurements, as taught by ENVER because it would be understood as an more robust and direct way to improve efficient control and management, with timely input and adjustments to sampling frequency to avoid over or under sampling, which would have significant impact on computational load. One of ordinary skill would understand the positive impact of combining this input technique taught by ENVER with the method of MISHIN as modified and taught above to provide more timely control of an environment or system, and allow for expertise of a user, or refined results of predictive analysis to be used to adjust sampling parameters.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BOETTCHER (US 20180011459 A1) – teaches a generalized predictive modeling method for monitoring and control of building equipment.
CARTY (US-20160187911-A1) – teaches a predictive data monitoring method specific to HVAC system control and efficiency.
WENZEL (US 20200379423 A1 ) – teaches use of PCA algorithms and other methods for monitoring and control of HVAC systems.
BHAGWAT (Bhagwat, et al., "Fast and Accurate Evaluation of Cooling in Data Centers." ASME. J. Electron. Packag. March 2015; 137(1) – teaches method for improving speed of predictive modeling for cooling in data centers.
COTRUFO (Cotrufo, et al., “A PRINCIPAL COMPONENT ANALYSIS-BASED APPROACH FOR THE ONGOING COMMISSIONING OF CENTRIFUGAL CHILLERS”, CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland, pp 407-412) – teaches PCA method for monitoring and control of cooling chillers, with real tine data streaming including data reduction and reduction error analysis methods with visualization of principal components for evaluation.
ES-SAKALIA (Es-Sakalia, et al., “Review of predictive maintenance algorithms applied to HVAC”, ScienceDirect Energy Reports 8 (2022) 1003–1012, May 2022) – teaches a full review of current practice (as of 2022) for using algorithmic monitoring and control as applied to HVAC systems.
JOLIFFE (Jolliffe, et al., “Principal component analysis: a review and recent developments”, Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374, 2065) – teaches an overview of PCA with application examples, with details on generalized method of PCA.
LI (Li, et al., "An enhanced PCA-based chiller sensor fault detection method using ensemble empirical mode decomposition based denoising", Energy Build 2019;183:311-324) – teaches method of fault detection, with relevance to change event monitoring for predictive analysis, monitoring and control of chiller sensor data with extensions and focus on denoising of data streams.
MIGENDA (Migenda, et. al., “Adaptive dimensionality reduction for neural network-based online principal component analysis”, PLoS One. 2021 Mar 30;16(3):e0248896) – teaches a method for application of PCA using a neural network technique for training models, with focus on large data streaming.
MOROSHITA (“Time-dependent principal component analysis: A unified approach to high-dimensional data reduction using adiabatic dynamics”, J. Chem. Phys. 7 October 2021) – teaches generalized method for high-dimensional time-dependent data reduction using PCA.
SEVERSON (Severson, et al., "Principal Component Analysis of Process Datasets with Missing Values", Processes 2017, 5(3), 38.) – teaches method for performing PCA with data streaming and large data sets for industrial application with focus on dealing with incomplete data for model building.
WANG (Wang, et al., “AHU sensor fault diagnosis using principal component analysis method”, Energy Build 2004;36(2):147–60) – teaches application of PCA to air handling (AHU) sensor systems, and generally HVAC systems to determine and predict system fault events.
ZHAO (Zhao, et al., “Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)”, Appl. Energy 112 (2013) 1041–1048) – teaches detailed method for using PCA to monitor data streams from chillers.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TONI D SAUNCY whose telephone number is (703)756-4589. The examiner can normally be reached Monday - Friday 8:30 a.m. - 5:30 p.m. ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TONI D SAUNCY/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863