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 .
Response to Amendment
Claims 1-4 and 6-13 are amended and claim 5 is cancelled. Claims 1-4 and 6-13 are pending.
Response to Arguments
Applicant's arguments filed 9/19/2025 have been fully considered and are partially persuasive.
Regarding the objection to the drawings and related objection to the specification, and as noted by Applicant on page 8 of the response, the amendment to the drawings overcomes the objections to the drawings and specification, which are withdrawn.
Regarding the objections to claims 2-3, 5, and 8, and as noted by Applicant on page 8 of the response, the amendments to the claims overcome the objections, which are withdrawn. The Examiner notes that the amendment to claim 13 introduces a minor informality that is objected to in the current Action.
Regarding the rejections of claims 1-13 under 112(b), and as noted by Applicant on page 9 of the response, the amendments to the claims overcome the rejections, which are withdrawn.
Regarding the rejections of independent claims 1 and 13 under 103, and as noted by Applicant on pages 15-16 of the response, claims 1 and 13 are amended to incorporate the limitations of now-cancelled claim 5, which renders claims 1 and 13 and all claims depending therefrom allowable over the prior arts for the reasons set forth below. The rejections of claims 1-4 and 6-13 under 103 are therefore withdrawn.
Regarding the rejections of claims 1-13 (now claims 1-4 and 6-13) under 101, the Examiner respectfully disagrees that the amendments to independent claims 1 and 13 renders claims 1-4 and 6-13 patentable under 101 for the following reasons.
The Examiner acknowledges, as contended by Applicant on page 11 of the response, that “collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility” cannot be practically be implemented via mental processes and therefore constitutes an “additional element.” On pages 11-12 of the response, Applicant contends that this additional element integrates the judicial exception into a practical application, such that the claim is not directed to the judicial exception. Applicant notes that one judicially accepted consideration for such integration is whether the judicial exception is implemented using/with a particular machine or manufacture that is integral to the claim, and asserts that the additional element “an IOT sensor communicatively coupled with the server and installed in the facility” in order to collected operation data of a machine to perform predictive maintenance of the machine integrates the exception into a practical application because the IOT sensor and the server are integral to the claim. Supporting the IOT sensor and server configuration being integral the claim, Applicant notes that the server, which is used to perform the recited method using the IOT sensor, is not a generic computer but is a particular machine to which the judicial exception is implemented with or the judicial exception is used in conjunction with, such that the method recited in claim 13 is tied to a particular machine and that the IOT sensor is not recited at a higher level of generality but instead performs the integral function of collecting operational data to be used in the method performed by the predictive maintenance system.
The Examiner acknowledges that the structural features “an IOT sensor communicatively coupled with the server and installed in the facility” are integral to the claim in the sense that these structures limit the scope of the overall apparatus for implementing the method steps. However, the Examiner respectfully disagrees that these structures constitute a particular machine that is meaningfully integral in terms of playing a particularized function having significance to the underlying processing steps falling within the judicial exception. As noted in the grounds for rejecting claim 13 under 101, a broadest reasonable interpretation of “IOT sensor” may entail any sensor used in an IOT processing environment and that such IOT sensing represents high-level data collection using known data collection components such as disclosed in the prior arts (e.g., Basel (US 2020/0134510 A1) and Kang (US 2019/0050752 A1). Furthermore, communicatively coupling the IOT sensor with a well-known and relatively generic computer collection and distribution processing device such as a server also constitutes high-level data collection using known data collection components in a typical data collection configuration (e.g., Basel and Kang disclose a server coupled to IOT sensor components) that plays no particularized role in the underlying method steps falling within the judicial exception. Therefore, the additional element “collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility” constitutes pre-solution type insignificant extra solution activity that fails to integrate the judicial exception into a practical application.
On pages 12-13 of the response, Applicant contends that amended claim 13 meets the Step 2B condition of the additional elements resulting in the claim as a whole amounting to significantly more than the judicial exception. In support, Applicant notes that claim 13 now incorporates the limitations of claim 5 that are not generic and well-understood, and further includes “collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility” and “converting the operation data to time-series operation data of the at least one machine” that in addition to the previously recited elements enhance the predictive maintenance and when considered as an ordered combination, transform the claim into a patent-eligible invention by creating a technical solution that is significantly more than the abstract idea of collecting information, analyzing it, and displaying certain results.
The Examiner submits that the “inventive concept” of claim 13 (explained in greater detail below) is entailed entirely within a subset of the additional element “wherein in the graph, any one of an X-axis and a Y-axis indicates a presence or absence of an abnormal state, a remaining one of the X-axis or the Y-axis indicates a degree of the abnormal state, and a result value of FFT analysis of the operation data is displayed as dots of different colors for a three-phase channel, which while rendering claim 13 patentable over the prior arts, represents display output processing having substantially insignificant functional relation to the method steps falling within the judicial exception and therefore constitute insignificant post-solution activity that does not result in the claim as a whole amounting to significantly more than the judicial exception.
The step “converting the operation data to time-series operation data of the at least one machine,” is found in the current grounds of rejection to fall within the mental processes judicial exception as explained in the current grounds of rejection. However, as further explained in the current grounds of rejection, the Examiner submits that even if this step is interpreted more narrowly to fall outside the mental processes judicial exception, converting operation data, such as may be measured by sensors, into time-series data represents standard time-domain based sensing in which measured values are associated with respective times (e.g., time increments). For example, Basel teaches such conversion (FIG. 3 depicting measures values that have been converted to time series data via association with respective time points in a time series; [0017] sensors configured to generate time-series data (i.e., associate measured values with time points)), as does Kang (Abstract operations data represented in time domain; [0008] collect time sequential data and divide at time intervals (data values have been associated with corresponding time points); FIG. 6). In this manner, this step represents high-level data collection constituting insignificant extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim Objections
Claim 13 is objected to because of the following informalities:
In claim 13 line 25, the first instance of the acronym “FFT” should be spelled out in association with the acronym.
Appropriate correction is required.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Independent claim 13, representative also of claim 1, recites:
“[a] server performing an embedding analysis-based facility predictive maintenance method, comprising:
a memory in which a program configured to perform an embedding analysis-based facility predictive maintenance method is stored; and
a processor configured to execute the program, wherein the method comprises:
(a) collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility;
(b) converting the operation data to time-series operation data of the at least one machine;
(c) deriving abnormal state information by determining whether the collected time-series operation data deviates from a time-series threshold, deriving an embedding result pattern through embedding analysis on the collected time-series operation data, and mapping the embedding result pattern and the abnormal state information;
(d) building an abnormal pattern analysis model by performing machine learning on each mapped information and analyzing whether the embedding result pattern indicates abnormality or normality; and
(e) when new time-series operation data is collected, applying the new time-series operation data to the abnormal pattern analysis model to derive current state information and future prediction state information,
wherein the embedding result pattern is expressed as a graph consisting of four quadrants, and
wherein in the graph, any one of an X-axis and a Y-axis indicates a presence or absence of an abnormal state, a remaining one of the X-axis or the Y-axis indicates a degree of the abnormal state, and a result value of FFT analysis of the operation data is displayed as dots of different colors for a three-phase channel.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 13 recites an apparatus and claim 1 recites a method and each therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 13 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion) and the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations). MPEP § 2106.04(a)(2).
The recited functions:
“converting the operation data to time-series operation data of the at least one machine,”
“deriving abnormal state information by determining whether the collected time-series operation data deviates from a time-series threshold, deriving an embedding result pattern through embedding analysis on the collected time-series operation data, and mapping the embedding result pattern and the abnormal state information,”
“analyzing whether the embedding result pattern indicates abnormality or normality,” and
“when new time-series operation data is collected, applying the new time-series operation data to the abnormal pattern analysis” “to derive current state information and future prediction state information,” may be performed as mental processes.
Converting the operation data to time-series operation data of at least one machine may be performed via mental processes (e.g., evaluation of sensor data and (perhaps aided by pen-and-paper plotting or charting the sensor vs. time). Deriving abnormal state information by determining whether the collected time-series operation data deviates from a time-series threshold may be performed via mental processes (e.g., evaluating and judgement of whether time-series data deviates from a threshold and recognizing abnormal state data accordingly). Deriving an embedding result pattern through embedding analysis on the collected time-series operation data, and mapping the embedding result pattern and the abnormal state information may be performed via mental processes (e.g., observation and evaluation of time-series data to recognize a pattern and mental correlation of (mapping) the pattern with the recognized abnormal state information). Analyzing whether the embedding result pattern indicates abnormality or normality may be performed via mental processes (e.g., observation and evaluation (possibly repeated) of mapped information and judgement in determining whether a result pattern indicates abnormality/normality). When new time-series operation data is collected, applying the new time-series operation data to the abnormal pattern analysis to derive current state information and future prediction state information may also be performed via mental processes (e.g., observation and evaluation of new time-series data to derive current state information and future prediction state information).
The type of high-level information analysis and deduction recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind).
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 13 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” including “a server,” “a memory in which a program configured to perform an embedding analysis-based facility predictive maintenance method is stored; and a processor configured to execute the program,” “collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility,” “building an abnormal pattern analysis model by performing machine learning on each mapped information,” and applying the new time-series operation data to the abnormal pattern analysis “model” to derive current state information and future prediction state information, in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted steps or a device for implementing the highlighted steps such as a signal processing device or a generic computer. Regarding in particular, the step of “collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility,” the Examiner notes that a broadest reasonable interpretation of an IoT sensor in view of Applicant’s specification entails any sensor within a facility that may be incorporated into a system for collecting IoT information. The connection of a sensor to a commonplace data collection and processing system such as a “server” bears no particularized relation to the underlying processing steps that are determined to fall within the judicial exception and instead represents a high-level data collection structure constituting insignificant extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception. The additional elements “wherein the embedding result pattern is expressed as a graph consisting of four quadrants, and wherein in the graph, any one of an X-axis and a Y-axis indicates a presence or absence of an abnormal state, the other indicates a degree of the abnormal state, and a result value of FFT analysis of the operation data is displayed as dots of different colors for a three-phase channel,” characterize a particular display output format for processed data that does not relate to the manner in which the data is processed via the elements constituting the judicial exception. Therefore, these additional elements constitute extra solution activity that does not integrate the judicial exception into a practical application.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements relating to the device implementing the method are configured and implemented in a conventional rather than a particularized manner of implementing operations monitoring such as for equipment/machines.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 13 does not include any such transformation or reduction. Instead, claim 13 as a whole entails receiving/generating input information (converting collected operations data to time-series operation data), applying standard processing techniques (i.e., machine learning and application of machine learning) to the information to obtain and deduce current and predicted abnormality information with the additional elements “server,” “memory,” “processor” merely providing a means for implementing the abstract idea, and “collecting time-series operation data of at least one machine” representing high-level data collection and therefore failing to provide a meaningful integration of the abstract idea (deriving abnormal state data based on a threshold, deriving a data pattern from the time-series data, mapping the pattern and abnormal state information, analyzing whether the pattern indicates abnormality/normality, and deriving current and future prediction stat information) in an application that transforms an article to a different state. Instead, the additional elements represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 13 does not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, claim 13 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements in claim 13 constitute insignificant extra-solution activity and therefore, in addition to failing to integrate the judicial exception into a practical application, also fail to result in the claim as a whole amounting to significantly more than the judicial exception. Regarding, in particular “wherein in the graph, any one of an X-axis and a Y-axis indicates a presence or absence of an abnormal state, a remaining one of the X-axis or the Y-axis indicates a degree of the abnormal state, and a result value of FFT analysis of the operation data is displayed as dots of different colors for a three-phase channel, while as indicated below the features “a remaining one of the X-axis or the Y-axis indicates a degree of the abnormal state, and a result value of FFT analysis of the operation data is displayed as dots of different colors for a three-phase channel” render claim 13 patentable over the prior arts, this element overall represents display output processing having substantially insignificant functional relation to the method steps falling within the judicial exception and therefore constitute insignificant post-solution activity that does not result in the claim as a whole amounting to significantly more than the judicial exception.
Furthermore, most of the additional elements appear to be generic and well understood as evidenced by the disclosures of Basel (US 2020/0134510 A1) and Kang (US 2019/0050752 A1), each of which teach virtually the same data collection/processing operations using the same structural features.
As explained in the grounds for rejecting claim 13 under 103 in the Non-Final Office Action dated 6/5/2025, Basel teaches “a server,” “a memory in which a program configured to perform an embedding analysis-based facility predictive maintenance method is stored; and a processor configured to execute the program,” “building an abnormal pattern analysis model by performing machine learning on each mapped information,” and applying the new time-series operation data to the abnormal pattern analysis “model” to derive current state information and future prediction state information. Basel further teaches collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility (FIG. 1A depicting computing device 110 that per [0014] may be configured as a server in an Internet of Things environment receiving sensor data from sensors 102 that performed proximity-dependent sensing (e.g., pressure, vibration) and therefore are within whichever facility houses or supports the target equipment). Similarly, Kang teaches computer processing and networking functions including a server (Abstract, FIG. 4), a memory ([0054]), a processor ([0054]), collection of IoT sensor data in a facility in which sensor coupled to server (FIG. 1 sensor assembly 100 disposed in proximity to machine 10 and providing data to server 200; [0037]-[0041]), and use of machine learning modelling, which inherently includes building a model, ([0046]; FIG. 13 block S130) for implementing machine anomaly detection.
Regarding “converting the operation data to time-series operation data of the at least one machine,” the Examiner submits that even if this step is interpreted more narrowly to fall outside the mental processes judicial exception, converting operation data, such as may be measured by sensors, into time-series data represents standard time-domain based sensing in which measured values are associated with respective times (e.g., time increments). For example, Basel teaches such conversion (FIG. 3 depicting measures values that have been converted to time series data via association with respective time points in a time series; [0017] sensors configured to generate time-series data (i.e., associate measured values with time points)), as does Kang (Abstract operations data represented in time domain; [0008] collect time sequential data and divide at time intervals (data values have been associated with corresponding time points); FIG. 6). In this manner, this step represents high-level data collection constituting insignificant extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Therefore, the additional elements are insufficient to amount to significantly more than the judicial exception.
Independent claim 13 is therefore not patent eligible.
Method claim 1 includes substantially the same elements constituting an abstract idea type judicial exception as claim 13, and further includes no additional elements that either integrate the abstract idea into a practical application or result in the claim as a whole amounting to significantly more than the judicial exception.
Claim 1 is therefore also patent ineligible under 101.
Claims 2-4 and 6-12, depending from claim 1, provide additional features/steps that are part of an expanded algorithm that includes the abstract idea of claim 1 (Step 2A, Prong One). None of dependent claims 2-4 and 6-12 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for similar reasons as discussed with regard to claim 13 and the additional reasons set forth below.
Regarding claim 2, the recited function:
“converting the operation data into frequency data by performing the (FFT) analysis” is determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)) because performing FFT analysis to convert data is fundamentally characterized by mathematical calculations (e.g., Fourier transform) and therefore constitutes mathematical relationships, such that this step falls within the judicial exception.
The step “dividing the time-series operation data by using time required for the machine to manufacture one product as one cycle,” may be performed via mental processes (e.g., evaluation of operation data in accordance with an average product manufacture cycle time) and therefore falls within the judicial exception.
The additional elements “collecting the operation data when the machine operates and performs a process of manufacturing a specified product;” and “collecting the time-series operation data by mapping the divided time-series operation data on a time domain having a length corresponding to the one cycle” constitute extra solution activity (high-level data collection) that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding claim 3, “wherein when there is a plurality of measurement sensors installed in the at least one machine, the operation data is data related to an operation of the at least one machine collected by each of a plurality of channels for a preset period from the IoT sensor” relates to the nature of the operation data collected and processed and therefore does not constitute an additional element, instead falling within the recited judicial exception.
Regarding claim 4, “deriving an upper and a lower limit of the time-series threshold by learning time-series operation data from which actual state information is generated and time-series operation data indicating a normal state,” “detecting abnormal state information of real-time time-series operation data according to whether the collected time-series operation data deviates from the lower limit of the time-series threshold,” “deriving the embedding result pattern by embedding analysis of the time-series operation data,” and “matching” “the abnormal state information, the embedding result pattern, and the actual state information of the time-series operation data, wherein the actual state information is information indicating whether there is a machine error and whether a product manufactured by the at least one machine is defective, which is information indicating the machine error and product defect, information indicating the machine error and product normality, or information indicating the machine normality and the product defect,” may individually and collective be performed via mental processes and therefore fall within the judicial exception. The additional element “storing the abnormal state information” represents conventional computer processing activity and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding claim 6, “detects the presence or absence of the abnormal state and the degree of the abnormal state based on which quadrant the second group dots are located” may be performed via mental processes and therefore falls within the judicial exception. The additional element “wherein the dots comprises first group dots distributed sporadically over a preset interval and second group dots distributed close to each other within the preset interval or less” similar to the additional elements in claim 5 characterize a particular display output format for processed data that does not relate to the manner in which the data is processed via the elements constituting the judicial exception. Therefore, this additional element constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding claim 7, “matching” “the abnormal state information, the embedding result pattern, and the actual state information of a specific machine from the normal state to the abnormal state or until returning to the normal state after a failure state occurs, and performing the machine learning” may be performed via mental processes and therefore falls within the judicial exception. The additional element “storing the abnormal state information, the embedding result pattern, and the actual state information of a specific machine” is conventional computer processing activity and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding claim 8, “predicting a future embedding result pattern from the embedding result pattern by the time-series operation data collected in real-time based on a result of the machine learning performed in the (c-3)” may be performed via mental processes and therefore falls within the judicial exception.
Regarding claim 9, “wherein the future embedding result pattern is related to a movement direction, movement distance, and movement time of each dot group forming a current embedding result pattern” characterizes the nature of the data on which the elements constituting the judicial exception operate and therefore does not constitute an additional element.
Regarding claim 10, “wherein the future embedding result pattern is related to a presence or an absence of a failure or the abnormal state, and time taken until normal operation after the failure or the abnormal state occurs” characterizes the nature of the data on which the elements constituting the judicial exception operate and therefore does not constitute an additional element.
Regarding claim 11, “updating the time-series threshold as the operation data is collected and accumulated in real-time” may be performed via mental processes and therefore falls within the judicial exception.
Regarding claim 12, the additional element “providing a manager terminal with prediction information on whether an operating state of the at least one machine will change from a normal state to the abnormal state or a prediction information on whether the operating state of the at least one machine will change from the abnormal state to the normal state and the future prediction state information and actual state information including information on a type of failure and time required for each state transition” constitutes generalized outputting the information generated in accordance with the elements constituting the judicial exception and therefore neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
For the foregoing reasons, dependent claims 2-4 and 6-12 are also patent ineligible under 101.
Subject Matter Allowable Over the Prior Art
Claims 1-4 and 6-13 would be allowable if independent claims 1 and 13 are amended to overcome the rejections under 101 set forth above and for claim 13 the claim objection is overcome.
The following is an examiner’s statement of reasons for allowability of claims 1-4 and 6-13 over the prior arts:
Regarding claims 1 and 13, the most pertinent prior arts are represented by Basel (US 2020/0134510 A1), Kang (US 2019/0050752 A1), and Graabaek (US 2024/0351209 A1).
Regarding claim 1, and as explained in the grounds for rejecting claim 1 in the Non-Final Office Action dated 6/5/2025, the combination of Basel and Kang teaches or otherwise renders obvious the elements of claim 1 including:
An embedding analysis-based facility predictive maintenance method performed by a server, the method comprising:”
“(c) deriving abnormal state information by determining whether the collected time-series operation data deviates from a time-series threshold, deriving an embedding result pattern through embedding analysis on the collected time-series operation data, and mapping the embedding result pattern and the abnormal state information;
(d) building an abnormal pattern analysis model by performing machine learning on each mapped information and analyzing whether the embedding result pattern indicates abnormality or normality; and
(e) when new time-series operation data is collected, applying the new time-series operation data to the abnormal pattern analysis model to derive current state information and future prediction state information.”
Basel further teaches “wherein the embedding result pattern is expressed as a graph (FIG. 2 depicting feature space clustered data in two-dimensional graph format, [0051]).
Regarding the elements added/modified by amendment, Basel further teaches “(a) collecting operation data of at least one machine in a facility using an internet of things (IoT) sensor communicatively coupled with the server and installed in the facility (FIG. 1A depicting computing device 110 that per [0014] may be configured as a server in an Internet of Things environment receiving sensor data from sensors 102 that performed proximity-dependent sensing (e.g., pressure, vibration) and therefore are within whichever facility houses or supports the target equipment) and “(b) converting the operation data to time-series operation data of the at least one machine (FIG. 3 depicting measures values that have been converted to time series data via association with respective time points in a time series; [0017] sensors configured to generate time-series data (i.e., associate measured values with time points)).”
Neither of Basel nor Kang appears to teach the graph “consisting of four quadrants, and wherein in the graph, any one of an X-axis and a Y-axis indicates a presence or absence of an abnormal state, a remaining one of the X-axis or the Y-axis indicates a degree of the abnormal state, and a result value of fast fourier transform (FFT) analysis of the operation data is displayed as dots of different colors for a three-phase channel.”
Graabaek discloses a method for detecting operation anomalies (Abstract) that including generating a four-quadrant embedded result graph (FIG. 8c depicting feature map into which operation data is mapped and in which the 2D axes (indicated as origin points 0.00 on each mutually orthogonal axis) divide the feature space into quadrants, [0186]) wherein in the graph, any one of an X-axis and a Y-axis indicates a presence or absence of an abnormal state (FIG. 8c either or both axes reveal relative 2D location of anomaly data 861 and therefore indicate a presence of abnormal data; [0191]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Graabaek’s teaching of generating/using a four-quadrant embedded result graph wherein in the graph, any one of an X-axis and a Y-axis indicates a presence or absence of an abnormal state to the method taught by Basel as modified by Kang.
The motivation would have been to present the embedded result data in visually useful and familiar format in which the abnormality indicating axis permits abnormality trends in the data to be efficiently determined by human or non-human observer as suggested by Graabaek.
None of the prior arts, individually or in combination appear to teach or suggest “a remaining one of the X-axis or the Y-axis indicates a degree of the abnormal state, and a result value of fast fourier transform (FFT) analysis of the operation data is displayed as dots of different colors for a three-phase channel” taken in combination with the other limitations of claim 1.
Claims 2-4 and 6-12 depend from claim 1 and are likewise allowable over the prior arts for the same reasons.
Independent claim 13 includes substantially the same elements that distinguish claim 1 from the prior arts and is likewise allowable over the prior arts for the same reasons.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW W BACA whose telephone number is (571)272-2507. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T 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.
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/MATTHEW W. BACA/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863