DETAILED ACTION
This action is in response to the application filed on 6/13/2024.
Claims 1-13 are pending.
Acknowledgment is made of a claim for foreign priority. All of the certified copies of the priority documents have been received.
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 references listed on the Information Disclosure Statement submitted on has/have been considered by the examiner (see attached PTO-1449).
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 12 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims 12 and 13 are not clearly limited to the non-transitory medium. The specification also does not clearly define the claimed " " as being limited to a non-transitory medium.
Means plus Function - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim limitation “data processing device” has/have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “adapted” coupled with functional language “carry out the method” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) “data processing device” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the at least one corresponding structure described in the specification for each of the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
“data processing device“ : Pre-processing unit 3
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Mapping Notation
In this office action, following notations are being used to refer to the paragraph numbers or column number and lines of portions of the cited reference.
In this office action, following notations are being used to refer to the paragraph numbers or column number and lines of portions of the cited reference.
[0005] (Paragraph number [0005])
C5 (Column 5)
Pa5 (Page 5)
S5 (Section 5)
Furthermore, unless necessary to distinguish from other references in this action, “et al.” will be omitted when referring to the reference.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 9-13 are rejected under 35 U.S.C. 102(a1) and (a2) as being anticipated by Cader et al. (US 20220404235 A1)
1. A computer-implemented method for recalibrating a trained data-based calibration model for the use in a sensor system for measuring one or more physical variables, wherein the data-based calibration model is formed as a neural graph network which is trained to output an output vector comprising one or more output variables and a plurality of auxiliary variables in dependence on a sensor state graph representing a sensor state, the method comprising:
detecting one or more detection variables relating to the one or more physical variables and one or more state variables which indicate one or more environmental influences on the sensor system at a time of detection;
“[0013]… The sensor data can include the sensor quality range for automatic and at-scale detection of sensor drift, drift out of calibration, drift out of an anticipated or historical range, anomalous data that identifies a sensor malfunctioning, etc. The AI/ML model executed by the computing device can automatically initiate a correction process for the sensor (e.g., recalibration on the fly, etc.) to improve future statistical and machine learning models that monitor the health of the data center environment.”
ascertaining a sensor state graph depending on the one or more detection variables and the one or more state variables;
“[0042] In some examples, a drift detection alert may trigger the recalibration models to start to automatically recompute calibration parameters. Results may be immediately available for single metrics. For some sets of correlated metrics, operator intervention may be required to determine which sensor has drifted from its expected behavior. For some sets of correlated metrics, an ML model may determine which sensor(s) is(are) faulty using root-cause analysis (e.g., correlating events with the root cause of the faulty sensor, establishing a causal graph between the root cause and the resulting faulty sensor, etc.).”
augmenting the sensor state graph; evaluating the augmented sensor state graph with the data-based calibration model to obtain the output vector;
“[0042]…In some examples, the recalibration parameters may be generated on a regular basis, or during shutdown or routine maintenance checks. A series of scenarios may put the sensor into predefined or known states with known operational characteristics. Sensor metadata and quality module 108 may verify that the sensors are calibrated and produce expected measurements.”
determining a loss depending on the output vector; and training in an unsupervised manner the calibration model depending on the determined loss.
“[0042]…In some examples, the recalibration parameters may be generated on a regular basis, or during shutdown or routine maintenance checks. A series of scenarios may put the sensor into predefined or known states with known operational characteristics. Sensor metadata and quality module 108 may verify that the sensors are calibrated and produce expected measurements.”
2. The method according to claim 1, wherein the sensor state graph is ascertained by assigning to nodes of the sensor state graph node variables which correspond to the one or more detection variables and the one or more state variables at the time of detection, and assigning to edges of the sensor state graph, which in each case connect two nodes of the sensor state graph to one another, in each case an edge variable which indicates a correlation between the detection variables or state variables within a predetermined time window, state variables represented by the respective edge, wherein the correlation is determined by evaluating time courses of the detection variables or state variables within a predetermined time window.
“[0065] In some examples, the system may use only uncalibrated sensors and/or a cluster of sensors can be adjusted. The adjustments can be implemented according to the sensor graph (e.g., location, etc.) and deterministic system behavior in the baseline state. A simple example would be a state where all nodes are powered off. All temperature sensors in one cooling loop should measure the same temperature after a defined period (e.g., overnight, etc.). With historical data collected during all baseline periods it may be possible to calibrate the system sensors over time. For example, a node's power consumption is directly converted into heat. In some examples, all sensors measuring the heat transfer from a node may be able to sum up to the node power consumption.”
9. The method according to claim 1, wherein the one or more output variables comprise one or more correction variables for applying to the one or more detection variables to obtain one or more sensor output variables depending on the one or more correction variables, or wherein the one or more output variables correspond to the one or more sensor output variables.
“[0042] In some examples, a drift detection alert may trigger the recalibration models to start to automatically recompute calibration parameters. Results may be immediately available for single metrics. For some sets of correlated metrics, operator intervention may be required to determine which sensor has drifted from its expected behavior. For some sets of correlated metrics, an ML model may determine which sensor(s) is(are) faulty using root-cause analysis (e.g., correlating events with the root cause of the faulty sensor, establishing a causal graph between the root cause and the resulting faulty sensor, etc.).”
10. The method according to claim 1, wherein the calibration model is used by determining the sensor state graph depending on the one or more detection variables and the one or more state variables, wherein the one or more output variables are determined depending on the sensor state graph.
“[0042] In some examples, a drift detection alert may trigger the recalibration models to start to automatically recompute calibration parameters. Results may be immediately available for single metrics. For some sets of correlated metrics, operator intervention may be required to determine which sensor has drifted from its expected behavior. For some sets of correlated metrics, an ML model may determine which sensor(s) is(are) faulty using root-cause analysis (e.g., correlating events with the root cause of the faulty sensor, establishing a causal graph between the root cause and the resulting faulty sensor, etc.).”
11. An apparatus comprising a data processing device adapted to carry out the method according to claim 1.
“[0011]…Embodiments described herein can include a computing device to receive this data, analyze the data, and help to improve data monitoring and quality in the data center using artificial intelligence (AI) and machine learning (ML). For example, the AI/ML model executed by the computing device can clean the data stream and determine sensor correlation.”
12. A computer program product comprising instructions which, when executed by at least one data processing device, cause the data processing device to perform the steps of the method according to claim 1.
“[0094] FIG. 5 illustrates an example iterative process performed by a computing component 500 for improving data monitoring and quality using AI and ML. Computing component 500 may be, for example, a server computer, a controller, or any other similar computing component capable of processing data. In the example implementation of FIG. 5, the computing component 500 includes a hardware processor 502, and machine-readable storage medium 504. In some embodiments, computing component 500 may be an embodiment of a system corresponding with computing device 100 of FIG. 1.”
13. A machine-readable storage medium comprising instructions which, when executed by at least one data processing device, cause the data processing device to perform the steps of the method according to claim 1.
“[0094] FIG. 5 illustrates an example iterative process performed by a computing component 500 for improving data monitoring and quality using AI and ML. Computing component 500 may be, for example, a server computer, a controller, or any other similar computing component capable of processing data. In the example implementation of FIG. 5, the computing component 500 includes a hardware processor 502, and machine-readable storage medium 504. In some embodiments, computing component 500 may be an embodiment of a system corresponding with computing device 100 of FIG. 1.”
Allowable Subject Matter
Claims 3-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding the claim 3, applicants uniquely claimed distinct features, which are not found in the prior art, either singularly or in an obvious combination of all the limitation of the claim, the distinct features being… the augmenting of the sensor state graph is performed by randomly removing one or more of the nodes, by randomly removing one or more of the edges, by randomly swapping node sizes and/or by randomly swapping edge sizes.
Regarding the claim 4-6, applicants uniquely claimed distinct features, which are not found in the prior art, either singularly or in an obvious combination of all the limitation of the claim, the distinct features being… the loss is determined by the calibration model is used to provide an evaluation model which corresponds to the calibration model or which, in addition to the calibration model, comprises one or more further downstream neuron layers or data-based models, a twin model is provided which is trained in the same way as the evaluation model and has a different configuration with respect to the evaluation model, an augmented sensor state graph is evaluated by the evaluation model to obtain an evaluation vector, a further augmented sensor state graph is evaluated by the twin model to obtain a further evaluation vector, and the loss is ascertained as a measure of the difference between the valuation vectors.
Regarding the claim 7, applicants uniquely claimed distinct features, which are not found in the prior art, either singularly or in an obvious combination of all the limitation of the claim, the distinct features being… the calibration model is provided such that the auxiliary variables indicate an output graph of the calibration model; wherein the loss is determined by the calibration model being evaluated using the augmented sensor state graph to obtain a reconstructed sensor state graph; the loss is ascertained as a measure of a difference between the original sensor state graph and the reconstructed sensor state graph.
Regarding the claim 8, applicants uniquely claimed distinct features, which are not found in the prior art, either singularly or in an obvious combination of all the limitation of the claim, the distinct features being… the data-based calibration model is initially trained before commissioning the sensor system by ascertaining the loss for a plurality of sensor states and a further loss is ascertained from training data sets for a supervised training, wherein a total loss is determined from the loss and the further loss, whereby the calibration model is initially trained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. KIM et al. (KR 20220093877 A) and Aliamiri (US 20200201434 A1) disclose relevant art related to the subject matter of the present invention.
A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action. An extension of time may be obtained under 37 CFR 1.136(a). However, in no event, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAE N NOH whose telephone number is (571)270-0686. The examiner can normally be reached on Mon-Fri 8:30AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Vaughn can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAE N NOH/
Primary Examiner
Art Unit 2481
Prosecution Insights