DETAILED ACTION
This FINAL office action is in response to Applicant’s amendment filed June 8, 2026. Applicant’s June 8th amendment amended claims 1, 3, 6; canceled claims 2, 5 and added new claims 8-11. Currently claims 1, 3, 4 and 6-11 are pending. Claim 1 is the independent claim.
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
The 35 U.S.C. 101 rejection of claims 1, 3, 4, 6 and 7 in the previous office action is maintained.
The 35 U.S.C. 103 rejections of claims 1-7 in in the previous office action is withdrawn in response to Applicant's amendments to the claims.
Response to Arguments
Applicant’s arguments, see Page 6, filed June 8, 2026, with respect to Zhang et al., Pankova et al., Fishburn et al. have been fully considered and are persuasive. The 35 U.S.C. 103 rejection(s) of claims 1, 3, 4, 6 and 7 has been withdrawn.
Applicant's arguments filed June 8, 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to an abstract idea (e.g. not directed to a mathematical algorithm, improvement in the operation of AMI, mapping physical and functional grid relationships; Remarks: Last Paragraph, Page 4; Paragraph 1, Page 5) and the claims recite significantly more than an abstract idea (e.g. captures interdependencies via GNN, solves poor anomaly detection solving recognized problem in AMI monitoring; specific application - graph based feature selection/node-to-edge operations for AMI; Remarks: Page 5).
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to an abstract idea, the examiner respectfully disagrees.
The claims are directed to a method for classifying events, specifically classifying events associated with a plurality of electrical meters in an ‘advanced metering infrastructure’ wherein event classification (e.g. anomaly detection) is an old, well-known, routine and conventional economic practice. Further collecting and analyzing data associated with electrical meters is also an old, well-known, routine and conventional economic practice.
While the claims may represent an improvement to the fundamental economic process of event classification related to electrical meters (intended use), the claims in no way either claimed or disclosed integrate the abstract idea into a practical application, provide a technical solution to a technical problem, improve any of the underlying technology (electrical meter, advanced metering infrastructure). Event classification/categorization, even as part of an electrical grid/advanced metering infrastructure (AMI) – non-functional descriptive material/label, is a well-known economic practice and does not represent a technical field or technology.
Additionally, the claims are directed to a mental process practically capable of being performed in the human mind via observation, evaluation, judgement and opinion. Independent claim 1: The step of collecting AMI meter data from a plurality of electrical meters may be performed in the human mind/via pen and paper using observation of data. The step of applying the collected AMI data to a GNN having a plurality of nodes/connections may be performed in the human mind/via pen and paper using judgement and evaluation. The step of capturing AMI meter data represented as a graph, relationships and dependencies may be performed in the human mind/via pen and paper using judgement and evaluation. The step of determining classifying events from the relationships/dependencies based on holistic interactions may be performed in the human mind/via pen and paper using evaluation. The step of providing to utility energy provided the classifying events may be performed in the human mind/via pen and paper via observation and opinion.
The claimed method fails to recite who or what performs any of the method steps, accordingly nothing in the claimed steps precludes the step from practically being performed in the mind. The claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea. The recited electrical meters do not play an active role in the claims, as data is merely collected from a plurality of electrical meters. Similarly, that the plurality of meters are part of a ‘advanced metering infrastructure’ is merely non-functional descriptive material and/or an intended use. That the meters are part of an AMI does not preclude the method steps from being performed by a human/via pen and paper. See MPEP 2106.05(f). Further the mere nominal recitation of an electrical meter and/or AMI each used for their well-understood, conventional and routine purpose does not take the claim limitation out of the mental processes grouping. The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of event classification. In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 612 (Fed. Cir. 2016). The recited technology (processor, memories, etc.), are used as a “conduit for the abstract idea,” not to provide a technological solution to a specific technological problem. Id.; see also id. at 611–13 (holding claims reciting the use of a cellular telephone and a network server to classify an image and store the image based on its classification to be abstract because the patent did “not describe a new telephone, a new server, or a new physical combination of the two” and did not address “how to combine a camera with a cellular telephone, how to transmit images via a cellular network, or even how to append classification information to that data”).
Additionally, the claims do not recite any specific claim limitations that would provide a meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. Nor do the claims present any other issues as set forth in the MPEP 2106.04(a) regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application.
Regarding the recited Graph Neural Network (GNN) to which the collected AMI data is applied and by which relationships and dependencies between electrical meters are mapped, the GNN is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic GNN on a generic computer, also recited at a high level of generality. The GNN is used to generally apply the abstract idea without limiting how the GNN functions. The GNN is described at a high level such that it amounts to using a generic computer with a generic GNN to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. The recitation of a semi-supervised framework and Graph Neural Network in the claims does not negate the mental nature of these limitations because the semi-supervised framework and Graph Neural Network are merely used as tools to perform an otherwise mental process.
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps recited in the claims through anything other than well understood technology used in a routine and conventional manner. Therefore, the claims lack an inventive concept. See also, e.g., Elec. Power Grp., 830 F.3d at 1355 (holding claims lacked inventive concept where “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”); Content Extraction, 776 F.3d at 1348 (holding claims lacked an inventive concept where the claims recited the use of “existing scanning and processing technology”).
Reevaluating the steps of receiving collecting AMI meter data and providing the classifying events which are considered insignificant extra solution activity, these limitations are mere data gathering and output recited at a high level of generality and amount to nothing more than receiving data which are both well-understood, routine and conventional activities. The limitations remain insignificant extra solution activity even upon reconsideration. Even when considered in combination the additional elements represent mere instructions to apply an exception and insignificant extra solution activity which cannot provide an inventive concept.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 recite significantly more than an abstract idea, the examiner respectfully disagrees.
As discussed above, and as noted in the previous office action, the claims fail to positively recite any technological elements, components or the like in the claims. Independent claim 1 fails to positively recite who or what performs the various method steps. Applicant is strongly encouraged, if supported by Applicant’s disclosure, to amend the claims to positively recite who or what performs the various method steps.
Utilizing generic, well-known, conventional and routine mathematical approaches and/or machine learning approaches/techniques to apply the abstract idea wherein the GNN and semi-supervised ‘framework’ are recited at a high level of generality and merely recite results without details as to how the results are obtained or the GNN/semi-supervised framework function at best represent elements represent mere instructions to apply an exception which cannot provide an inventive concept.
Even if Applicant were to amend the claims to recite a computer/processor performs the various method steps (if supported) the generic computer/processor and the above additional elements (electrical meter) in the claims amount to no more than a mere instruction to apply the abstract idea using generic computing components, wherein mere instructions to apply an judicial exception using generic computer components cannot integrate a judicial exception into a practical application or provide an inventive concept. For the collecting and providing steps that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. Applicant’s specification does not provide any indication that the computer/processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept.
The claims are ineligible under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
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, 3, 4 and 6-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding independent Claim 1 the claims are directed to the abstract idea of event classification. This is a process (i.e. a series of steps) which (Statutory Category – Yes –process).
The claims recite a judicial exception, a method for organizing human activity, event classification (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to providing to utility and energy providers (businesses, humans), determined classifying events (data) associated with advanced meter infrastructure (AMI) (intended use) comprising a plurality of electrical meter (Specification: Paragraphs 2, 3). See 2106.04(a). Further all of the steps of “collecting”, “applying”, “capturing”, “determining”, and “providing” recite functions of the event classification for event classification/management. The step of applying the collected meter to a graph neural network is also directed to a mathematical operation/concept. The intended purpose of independent claim 1 appears to be to provide classifying events (data) to human users/businesses, wherein the classified events are related to a plurality of electrical meters (e.g. AMI).
Accordingly, the claims recite an abstract idea – fundamental economic practice, specifically in the abstract idea subcategories of sales activities and/or commercial interactions. The exceptions are the utility and energy providers (who are businesses or persons) and additional limitations of generic electrical meters (other machinery – see MPEP § 2106.05(f)). See 2106.04(a).
Accordingly, the claims recite an abstract idea under Step 2A, Prong One, we proceed to Step 2A, Prong Two. Considering whether the additional elements set forth in the claim integrate the abstract idea into a practical application (See 2106.04(a)), the previously identified non-abstract elements directed to generic electrical meters. These generic components are merely used to provide data as described extensively in Applicant’s specification (Specification: Figure 1). Neither Applicant’s disclosure nor the pending claims disclose or recite a computer or other computing elements/components. When viewed as a whole with such additional elements considered as an ordered combination, the claim would be nothing more than a purely conventional computerized (implied by the use of a Graph Neural Network) implementation of applicant's event classification in the general field of event classification/management and would not provide significantly more than the judicial exception itself. Note McRo, Inc. v. Bandai Namco Games America Inc. (837 F.3d 1299 (Fed. Cir. 2016)), guides: "[t]he abstract idea exception prevents patenting a result where 'it matters not by what process or machinery the result is accomplished."' 837 F.3d at 1312 (quoting O'Reilly v. Morse, 56 U.S. 62, 113 (1854)) (emphasis added). The claims are not directed to a particular machine nor do they recite a particular transformation (MPEP § 2106.05(b)).
Additionally, the claims do not recite any specific claim limitations that would provide a meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. Nor do the claims present any other issues as set forth in the MPEP 2106.04(a) regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather, the claims merely use instructions to implement an abstract idea on a computer (implied, not positively claimed), or merely use a computer as a tool to perform an abstract idea. Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), claims 1, 3, 4 and 6-11 do not integrate the judicial exception into a practical application.
Regarding the recited semi-supervised framework (software per se, at best implementing unsupervised learning by using both labeled and unlabeled data to train artificial intelligence (AI) models for classification) and the application of a Graph Neural Network (GNN) to meter data to capture relationships and dependencies between the plurality of meters and classify events, the semi-supervised framework and GNN are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic the semi-supervised framework and GNN on a generic computer (not positively recited in the claims), also recited at a high level of generality. The semi-supervised framework and GNN are used to generally apply the abstract idea without limiting how the semi-supervised framework or GNN function. The semi-supervised framework and GNN described are at a high level such that it amounts to, at best, using a generic computer with a generic the semi-supervised framework and GNN to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Additionally, the claims recite a judicial exception, a mental processes, which can be performed in the human mind or via pen and paper (Judicial Exception – Yes – mental process).
Neither Applicant’s disclosure nor the pending claims disclose or recite any computing elements of any kind. For the purposes of examination, the examiner interpreted the claims to include/imply the use of a generic computer/processor (as tool, conduit) to perform the method steps involving the semi-supervised framework and Graph Neural Network. Applicant is encouraged to positively recite, if supported by the specification, who or what entity performs each of the method steps.
The claimed steps of applying the collected meter data to a graph neural network (also a mathematical operation/concept), capturing relationships and dependencies between the different electrical meters and determining classifying events from the relationships and dependencies all describe the abstract idea. These limitations as drafted are directed to a process that under its reasonable interpretation covers performance of the steps in the mind but for the recitation of the generic computer components. Other than the recitation of a plurality of electrical meters nothing in the claimed steps precludes the step from practically being performed in the mind. The claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea because the step of providing the classifying events is directed to insignificant post-solution activity (i.e. data output). Thus, the claim recites a mental process. (Judicial Exception recited – Yes – mental process).
The claims do not integrate the abstract idea into a practical application. The generic electrical meters are each recited at a high level of generality merely performs computer functions of providing data. At best, the claims may imply the use of a generic processor/computer which merely applies the abstract idea using generic computer components. The elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component.
Regarding the recited semi-supervised framework (software per se, at best implementing unsupervised learning by using both labeled and unlabeled data to train artificial intelligence (AI) models for classification) and the application of a Graph Neural Network (GNN) to meter data to capture relationships and dependencies between the plurality of meters and classify events, the semi-supervised framework and GNN are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic the semi-supervised framework and GNN on a generic computer (not positively recited in the claims), also recited at a high level of generality. The semi-supervised framework and GNN are used to generally apply the abstract idea without limiting how the semi-supervised framework or GNN function. The semi-supervised framework and GNN described are at a high level such that it amounts to, at best, using a generic computer with a generic the semi-supervised framework and GNN to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. The recitation of a semi-supervised framework and Graph Neural Network in the claims does not negate the mental nature of these limitations because the semi-supervised framework and Graph Neural Network are merely used as tools to perform an otherwise mental process.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Integrated into a Practical Application – No).
As discussed above the additional elements in the claims amount to no more than a mere instruction to apply the abstract idea using generic computing components, wherein mere instructions to apply an judicial exception using generic computer components cannot integrate a judicial exception into a practical application or provide an inventive concept. For the retrieving and displaying steps that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. Applicant’s specification does not provide any indication that the computer/processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is ineligible (Provide Inventive Concept – No).
The claims are ineligible under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding dependent claims 3, 4 and 6-11, the claims are directed to the abstract idea of event classification and merely further limit the abstract idea claimed in independent claim 1.
Claim 3 further limits the abstract idea by limiting the AMI data to both labeled and unlabeled data (a more detailed abstract idea remains an abstract idea). Claim 4 further limits the abstract idea by performing event classification using a feature selection operation using the most informative features (a more detailed abstract idea remains an abstract idea). Claim 6 further limits the abstract idea by training the GNN using labeled and unlabeled data (a more detailed abstract idea remains an abstract idea). Claim 7 further limits the abstract idea by utilizing a convolutional neural network to extract additional features combining with an interaction graph (a more detailed abstract idea remains an abstract idea). Claim 8 further limits the abstract idea by identifying anomalies in power distribution grid (a more detailed abstract idea remains an abstract idea). Claim 9 further limits by aggregating AMI meter data prior to applying GNN (a more detailed abstract idea remains an abstract idea). Claim 10 further limits the abstract idea using GNN as an encoder/decoder (a more detailed abstract idea remains an abstract idea). Claim 11 further limits the abstract idea by extracting additional features using a CNN (a more detailed abstract idea remains an abstract idea).
None of the limitations considered as an ordered combination provide eligibility because taken as a whole the claims simply instruct the practitioner to apply the abstract idea to a generic computer.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Allowable Subject Matter
Other that the rejection of pending claims 1, 3, 4 and 6-11 under 35 U.S.C. 101 the claims are allowable over the prior art.
The closest prior art Zhang et al., Pankova et al., Fishburn et al. fail to teach or suggest either singularly or in combination n event classification method for advanced metering infrastructure (AMI) comprising: collecting, in a semi-supervised framework, AMI meter data from a plurality of electrical meters; applying the collected AMI meter data to a Graph Neural Network (GNN), wherein the AMI meter data is represented as a graph having a plurality of nodes and a plurality of connections; capturing, by the GNN using the AMI meter data represented as a graph, relationships and dependencies between different electrical meters of the plurality of electrical meters by mapping each individual electrical meter of the plurality of electrical meters as a node in the graph and mapping physical and functional grid relationships between the different electrical meters as the connections between the nodes, and determining classifying events from the relationships and dependencies based on holistic interactions between the nodes rather than treating each electrical meter independently; and providing, to utility and energy providers, the classifying events so determined as recited in independent Claim 1.
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 SCOTT L JARRETT whose telephone number is (571)272-7033. The examiner can normally be reached M-TH 6am-4:30PM.
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SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625
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