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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) elements which under their broadest reasonable interpretation, are directed to mental processes. This judicial exception is not integrated into a practical application as explained below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as explained below.
Regarding Claim 13, the claim recites a regulatory reporting system, comprising:
a memory for storing machine-readable instructions; and
a processor core for accessing the machine-readable instructions and executing the machine-readable instructions as operations, the operations comprising:
assigning a set of labels to an event log of a set of event logs based on applying a pattern matching algorithm to the event log, wherein the event log characterizes a portion of an event associated with a nuclear power plant;
employing a natural language processor (NLP)-based classifier of a set of NLP-based classifiers to determine whether the event is reportable, wherein the NLP-based classifier determines whether the event is reportable based on analyzing the set of labels; and
generating a regulatory report based on the set of event logs, wherein, in response to a determination that the event log is reportable, the regulatory report indicates the event.
Claim Interpretation: Under the broadest reasonable interpretation, the terms of the claim are
presumed to have their plain meaning consistent with the specification as it would be interpreted by
one of ordinary skill in the art. See MPEP 2111.
Claim element c as written covers any method for assigned labels to an event log. The human mind can assign labels to events.
Claim element d as written utilizes NLP to determine if events are reportable based on the labels. The human mind can make a determination if an event is reportable based on a label.
Claim element e as written covers any method to create a report using the determination. The human mind can create a report utilizing pen and paper.
Additional elements recited are memory, processor, and natural language processor(NLP).
Claim 13 is patent illegible as it is directed to an abstract idea without being significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory
category. See MPEP 2106.03. The claim is directed to a system, which falls within one of the statutory
categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a
judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception
when the judicial exception is “set forth” or “described” in the claim.
As discussed above, the broadest reasonable interpretation of steps (c)-(e) is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion . See MPEP 2106.04(a)(2), subsection III.
Claim element c is directed to a mental process as a human can utilize pattern matching to label an event in an event log.
Claim element d is directed to a mental process as a human can make a determination if an event is reportable based on a label.
Claim element e is directed to a mental process as a human can generate a report using pen and paper based on reportable events in an event log.
Hence, these steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Therefore, these limitations are considered together as an abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating
those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Additional elements recited are memory, processor, and natural language processor(NLP). The memory, processor and natural language processor provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amount to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
At Step 2A, the additional elements of memory, processor, and natural language processor(NLP) were found to represent no more than mere instructions to apply the judicial exception on a generic computer using generic computer components. Mere instructions to “apply” the abstract ideas, cannot provide an inventive concept. See MPEP 2106.05(f). The analysis under Step 2A, Prong Two is carried through to Step 2B. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
As such Claim 13 is patent illegible.
Regarding Claim 1, analysis analogous to that of Claim 13 is applicable.
Regarding Claim 2, a human can analyze labels to determine if equipment in the log in usable. The second NLP is an additional element that is no more than mere instructions to apply the judicial exception on a generic computer using generic computer components.
Analysis analogous to Claim 2 is applicable to Claims 3, 16, 17
Regarding Claim 4, a human can analyze sensor data and create a description of the behavior of the equipment. The sensor is an additional element which is directed to data gathering which is well understood routine and conventional.
Analysis analogous to Claim 4 is applicable to Claims 14 and 18.
Regarding Claim 5, a human can perform the calculations necessary for ARIMA analysis.
Regarding Claim 6, the transformer model is an additional element which is directed to generic computer components which apply the judicial exception.
Regarding Claim 8, a human can analyze logs to determine the start and end times of an event and simply compute the duration based on that. The generative AI model is directed to an additional element that is no more than mere instructions to apply the judicial exception on a generic computer using generic computer components.
This analysis is applicable to Claims 7, 15 and, 19.
Regarding Claim 11, a human can select labels from a list and update the list using feedback from others. Additionally, a human can use feedback from prior events to determine labels they select. The weak supervision model is directed to an additional element that is no more than mere instructions to apply the judicial exception on a generic computer using generic computer components.
This analysis is applicable to Claims 9,10 and 20.
Regarding Claim 12, a human can format a report.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 9, 10, 11, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Brikis(US PGPub 20230376795) in view of Tang(CN 118070642).
Regarding Claim 1, Brikis teaches a non-transitory machine-readable medium having machine executable instructions for a regulatory reporting system that causes a processor core to execute operations(non-transitory computer readable medium encoded with executable instructions (such as a software component on a storage device) that when executed, causes at least one processor to carry out the described method.[0012]), the operations comprising: assigning a set of labels(semantic metadata 134) to an event log(The block represents a sequence of co-occurring log messages…As used herein “co-occurring messages” refers to messages related to a single event or co-related events [0017]) of a set of event logs based on applying a pattern matching algorithm to the event log(At step 130, a semantic label suggestion module 135 is annotates the co-occurring log messages of the block 122 using semantic metadata 134. The semantic metadata 134 act like labels that define one or more message types for the co-occurring log messages. The semantic metadata 134 includes the following labels a start action, an end action, a source, an anomaly, a cause and an inspect action[0046], The semantic metadata may be generated though supervised, semi-supervised or unsupervised learning[0026]), wherein the event log ( block) characterizes a portion of an event ( event ) associated with a power plant(The identified patterns(identified patterns (i.e., blocks)) represent higher-level events that happen in the industrial plant [0013]…The log files(block in log entries of the log files(Interpretation: log-files hold multiple blocks/events)) may refer to power plants [0016]); employing a natural language processor (NLP)-based classifier of a set of NLP-based classifiers to determine whether the event is reportable ( critical) , wherein the NLP-based classifier determines whether the event is reportable based on analyzing the set of labels(In an embodiment, the comparison and labeling module 155 is an inference module configured to determine a comparable template representation 152 from the template representations based on semantic matching between the sematic metadata 134 in the block 122 with sematic metadata associated with the template representations and predict the criticality 154 of the event in the block 122.[0053], a high critical event, a medium or low critical event or even a non-event[0029] Interpretation: an event is reportable if it is critical).
Brikis does not teach event associated with a nuclear power plant nor generating a regulatory report based on the set of event logs, wherein, in response to a determination that the event log is reportable, the regulatory report indicates the event.
However, Tang teaches event associated with a nuclear power plant(based on the nuclear reactor operation data[Embodiment 1]) and generating a regulatory report based on the set of event logs, wherein, in response to a determination that the event log is reportable, the regulatory report indicates the event(the abnormal detection and response module based on risk management plan, adopts abnormal detection algorithm(Interpretation: critical classification of Brikis) and emergency response mechanism to perform state monitoring and abnormal identification, generating abnormal report[Content of the Invention]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Brikis with the report generation of Tang because it would provide comprehensive information support[Tang Abstract].
Claim 13 recites similar limitations and is rejected under the same rationale.
Regarding Claim 9, Brikis as in Claim 1 teaches the pattern matching algorithm is employed by a weak supervision model (The semantic metadata may be generated though supervised, semi-supervised or unsupervised learning[0026]).
Regarding Claim 10, Brikis as in Claim 9, teaches the weak supervision model is trained(semantic labeling task is formulated as a multi-class classification problem on the basis of a phrase/chunk. The classification is performed by the trained machine learning model) based on user feedback to a prior set of labels assigned by the pattern matching algorithm(Through the feedback the block suggestion module 420 and the semantic segment module 430 are trained based on the modification of the semantic segmentation(The semantic segmentation refers to annotation of the block(s) based on the semantic metadata) performed by the domain expert 410.[0066]) to a prior event log of the set of event logs, wherein the prior event log was added to the set of event logs before the event log(Fig.5 512 discloses feedback for prior event logs(mapped to block)).
Regarding Claim 11, Brikis as in Claim 10 teaches the set of labels are selected from a pattern reference database(A basic list of the semantic metadata 134 defined for log message analysis in industrial log files is used when a custom semantic metadata is not generated[0047]), and the pattern reference database is updated based on the user feedback(A custom list of predefined semantic metadata is created for each industry/industrial application. This may be done with the help of domain expertise[0049]).
Claim(s) 2, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Briskis(US PGPub 20230376795) in view of Tang(CN 118070642) as applied to claim 1 above, and further in view of Sebih(US 20190158362).
Regarding Claim 2, Brikis as in Claim 1 teaches equipment associated with an event(The log files may refer to power plants…The power plants may have multiple turbines and other pieces of equipment[0016], The classification of the message types may be performed by identifying whether the log message relates to device... Such log messages may be annotated as source[0025]) and labels that indicate operability(Furthermore, cause is annotated when words like failed, is missing, deleted, error, unexpected failure, set computer in FAULT, is not reachable [0025]).
Neither Brikis nor Tang teach employing the second NLP-based classifier of the set of NLP-based classifiers to determine whether an equipment associated with the event is operable, wherein the second NLP-based classifier determines whether the equipment associated with the event is operable based on analyzing the set of labels.
However, Sebih teaches employing a classifier to determine operable status, wherein the classifier determines the operable status based on analyzing the set of labels(a machine learning unit which performs machine learning to determine the operating status… using acquired metrics and labels.[Abstract]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Brikis in view of Tang with the operable check of Sebih because it would enable making an inquiry about the operating status of an instance efficiently without consuming time(Sebih 0050).
Claim 17 recites similar limitations to that of Claim 2 and is rejected under the same rationale.
Claim(s) 3, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Briskis(US PGPub 20230376795) in view of Tang(CN 118070642) as applied to claim 1 above, and further in view of Zaman(Extracting failure time data from industrial maintenance records using text mining).
Regarding Claim 3, neither Brikis nor Tang teach employing the second NLP-based classifier of the set of NLP-based classifiers to determine whether the event was planned, wherein the second NLP-based classifier determines whether the event was planned based at least in part on analyzing the set of labels.
However, Zaman teaches employing an NLP-based classifier to determine whether the event was planned(SVM decision function in eq. (11) is used to classify the (similarly tokenized) DD as planned (𝑐𝑝) and unplanned (𝑐𝑓) work[2.2.2]), wherein the NLP-based classifier determines whether the event was planned based at least in part on analyzing the set of labels(Typically, WO(WO, in some cases, maintenance logs) data sets contain tags(Interpretation: these tags map to the labels/semantic metadata in Claim 1) that indicate the urgency and source of the maintenance request. Thus, these tags can be used to directly label each WO as planned/unplanned[2.1]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Brikis in view of Tang with the planned event check of Zaman because it would improve the estimation of failure times for real-world assets.
Claim 16 recites similar limitations to that of Claim 3 and is rejected under the same rationale.
Claim(s) 4,5,6,14,18,20 are rejected under 35 U.S.C. 103 as being unpatentable over Briskis(US PGPub 20230376795) in view of Tang(CN 118070642) as applied to claim 1 above, and further in view of Jin(What Can Large Language Models Tell Us about Time Series Analysis).
Regarding Claim 4, Brikis in view of Tang teach data from a sensor that monitors an equipment(The power plants may have multiple turbines and other pieces of equipment[Brikis 0016]) associated with the event(an industrial plant may include a combination of industrial assets such as control devices, sensors[Brikis 0015], the real-time monitoring technology comprises sensor data integration[Tang]). Brikis also taught wherein the NLP-based classifier determines whether the event is reportable based on analyzing the descriptive text(In an embodiment, the comparison and labeling module 155 is an inference module configured to determine a comparable template representation 152 from the template representations based on semantic matching between the sematic metadata(descriptive text) 134 in the block 122 with sematic metadata associated with the template representations and predict the criticality 154 of the event in the block 122.[0053], a high critical event, a medium or low critical event or even a non-event[0029] Interpretation: an event is reportable if it is critical).
Neither Brikis nor Tang teach analyzing time series data from a sensor that monitors an equipment associated with the event to extract discrete data that characterizes the behavior of the equipment; and generating descriptive text that characterizes the time series data by applying a natural language generator to the discrete data.
However, Jin teaches analyzing time series data to extract discrete data that characterizes the behavior(Additionally, AmicroN (Chatterjee et al., 2023) and SST (Ghosh et al., 2023) use LLMs for detailed sensor and spatial time series analysis[3.1 Data Based Enhancer]); and generating descriptive text that characterizes the time series data by applying a natural language generator to the discrete data(LLM-assisted enhancers not only enhance data interpretability but also provide supplementary improvements, facilitating a more thorough understanding and effective use of time series data. For interpretability, LLMs offer textual descriptions and summaries, helping to understand patterns and anomalies in time series data[3.1 Data Based Enhancer]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Brikis in view of Tang with the time series analysis of Jin because it would facilitate a more thorough understanding and effective use of time series data(3.1 Data Based Enhancer).
Claim 14 recites similar limitations to Claim 4 and is rejected under the same rationale.
Regarding Claim 5, Jin teaches the time series data is analyzed based on an autoregressive integrated moving average (ARIMA) model(Traditional analytics relied on statistical models like ARIMA[2.3 Research Roadmap]).
Regarding Claim 6, Jin teaches the natural language generator has a transformer architecture( LLMs(LLM has a transformer architecture) offer textual descriptions and summaries, helping to understand patterns and anomalies in time series data[3.1 Data Based Enhancer]).
Claim 18 recites similar limitations to Claims 1 and 4 and is rejected under the same rationales.
Claim 20 recites similar limitations to Claims 1,4 and 11 and is rejected under the same rationales.
Claim(s) 7,8,15 are rejected under 35 U.S.C. 103 as being unpatentable over Briskis(US PGPub 20230376795) in view of Tang(CN 118070642) as applied to claim 1 above, and further in view of Zhong(LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models).
Regarding Claim 8, Brikis as in Claim 1 teaches analyzing the event log and the additional event log to determine a start of the event and, an end of the event(wherein the semantic metadata is indicative of at least one of a start action, an end action…[0009]).
Neither Brikis nor Tang teach, determining a duration of the event comprises employing a generative artificial intelligence to perform semantic analysis on the event log and the additional event log.
However, Zhong teaches analyzing the event log and the additional event log a duration of the event comprises employing a generative artificial intelligence to perform semantic analysis(LLM-based template extractor. The latter capitalizes on the robustness of LLMs to semantically extract log templates from individual log messages…[1. Introduction]) on the event log and the additional event log(As a log parser, your task is to analyze logs and identify dynamic variables, Time/Duration of an Action (TDA): Timespan or duration of actions[Figure 4: Variable Aware Prompt for Log Parsing]).
Claims 7,15 recite similar limitations and are rejected under the same rationale.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Briskis(US PGPub 20230376795) in view of Tang(CN 118070642) as applied to claim 1 above, and further in view of Zhang(CN 116933787).
Regarding Claim 12, neither Brikis nor Tang teach the regulatory report is formatted for upload to a regulatory authority.
However, Zhang teaches the regulatory report is formatted for upload to a regulatory authority(standardizing the main information of the nuclear power plant event report, which is good for reporting the domestic event to the foreign nuclear power organization[0062]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Brikis in view of Tang with the formatting of Zhang because it would realize the transmission of the important information[Zhang Abstract].
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Briskis(US PGPub 20230376795) in view of Tang(CN 118070642) in view of Jin(What Can Large Language Models Tell Us about Time Series Analysis) as applied to claim 18 above, and further in view of Zhong(LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models).
Claim 19 recites similar limitations to Claim 8 and is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARJUN R SWAMY whose telephone number is (571)272-9763. The examiner can normally be reached Mon, Tue, Thur, Fri 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hai Phan can be reached at (571) 272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARJUN SWAMY/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
Prosecution Insights