DETAILED ACTION
This action is in response to claims filed 05 August 2026 for application 18651011 filed 30 April 2024. Currently claims 1-20 are pending.
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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 7-9, 11-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chougule et al. (US 20180157723 A1) in view of Ghiran et al. (A Metamodeling Approach to Teaching Conceptual Modeling "at Large").
Regarding claims 1, 11 and 20, Chougule discloses: A method, comprising:
monitoring, by a learning agent that is part of a monitored system, information used by a metamodel that describes the monitored system (“Further in the second part, once the template model is defined and agreed, the plurality of data sources can be monitored online and the conversion to instance model can be triggered on data presence. During this process an appropriate template is picked up which best matches to the data source. For this in the template data source specific images, texts, or any other identifiable data can be marked as key for a template to match. In absence of any such key markings a best template can also be configured to be chosen based on the content and largest match of the templates available. For example, a bank name, a bank logo along with the name of form can be looked for matching by specifying these as key markings. A template with all three will be best match and will be used for the instance model generation.” [0032]), wherein the metamodel comprises … a knowledge graph (“In the context of present disclosure, the term ‘knowledge’ and ‘information’ can be defined as follow: Knowledge: a domain model capturing structural and the functional properties of all domain elements. The knowledge representation may be termed as (domain) ontology or knowledge graph. And the reasoning and inference engine is the processing unit. Information: It is the subset of knowledge (in exact/specific context) used to solve a certain problem And both information extraction as well as information retrieval is performed here.” [0022], [0038] discloses layers in entity and attribute);
making, by the learning agent, a determination whether performance of the metamodel satisfies one or more criteria (“In case of simple information extraction (structured data), only ER model and simple static patterns are sufficient. Once a template is provided, the template gets applied on input data and the instance model gets generated. Also there could be need of time series data extraction, targeting the time factor over the infrastructure and their properties. The time based blueprint is useful for time based data analytics like predicting future events and setting alarms etc.” [0036], “ The instance model then can be validated using the validation module 116. Based on the validation, the review information or feedback can be given back to the template model generation module for it to adapt. The meaning of adapt here can be explained as follows: Template model generation is performed initially using the plurality of data sources, the existing ER model and the knowledge. An additional data which needs to be extracted from the plurality of data sources is then annotated. This template model is then shown to the user in form of annotations on the data source. The user can change/delete/add annotations as appropriate. The annotated additional data is then mapped with the ER model elements. These annotations are then used along with the plurality of data sources and the ER model to generate a new template model. Also the modifications made by user are used as feedback to enhance the knowledge which is used for further automated generations of the new template model.” [0030], model validation is interpreted as determining if performance meets a criteria);
generating, by the learning agent and based on the determination, an update to the metamodel (“Also to make the processing faster and smarter for given domain, ontology of knowledge elements can also be created over the set of given entity, attribute, actions within the domain. The knowledge elements are open to link (or contain) elements from NLP ontologies. That way it is a pre-processing of knowledge (data sources) to increase the processing efficiency and accuracy. Over the time, the learning (continual improvement) will make this domain ontology more and more complete and correct. Language structures can also be learnt over the period to avoid processing in some cases and to minimize processing in others.” [0035]); and
sending, by the learning agent, the update to the metamodel to a device that maintains the metamodel (“creating the instance model by the instance model generation module; and merging the created instance model to one or more existing instance models.” Claim 1).
Chougule does not explicitly disclose, however, Ghiran teaches: plurality of layers; The plurality of layers including one or more sub-symbolic layers associated with sensor data and one or more symbolic layers associated with concepts and relations between the concepts
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Chougule and Ghiran are in the same field of endeavor of knowledge and metamodels and are analogous. Chougule discloses metamodels for monitored systems. Ghiran teaches a multilevel metamodel with sensors in a sub-symbolic layer and a symbolic layer having concepts and relations. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known metamodel monitoring system of Chougule with the known symbolic and subsymbolic layers as taught by Ghiran as this provides an agile way to model and analyze system (abstract).
Regarding claims 2, and 12, Chougule discloses: The method as in claim 1, wherein the monitored system comprises a distributed computing environment (“Entity can have more than one instance, for example user, file, file system can have multiple instance. Entity Instances can have association with other entity instances.” [0038]).
Regarding claims 3, and 13, Chougule discloses: The method as in claim 1, wherein the learning agent is executed by a network router, switch, or gateway (“Existing meta-modeling environments typically may be used to create domain-specific modeling tools. Meta-models include syntax, semantics and entities. Entities such as routers, switches, operating systems, VMs, Servers continuously generate vast amount of logs data per second. This data contain useful information which can be used to take automatic action if machine can understand it. Automation tools can perform troubleshooting, security check if this unstructured information can be converted into structured format. The input information such as log files or any other data sources can be straightforward or obscure, depending on the attitude of the developer who wrote them. Either way, most of the time they are written with human readers in mind. It is necessary to extract relevant information from the data.” [0004]).
Regarding claims 4, and 14, Chougule discloses: The method as in claim 3, wherein the device is a different network router, switch, or gateways as that of the learning agent (“According to an embodiment of the disclosure the user interface 102 is configured to provide inputs to the system 100. The user interface 102 is configured to provide a plurality of data sources as a first input. The user interface 102 can also be configured to provide an existing entity relationship (ER) model as a second input. The user interface 102 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite.” [0024]).
Regarding claims 7, and 17, Chougule discloses: The method as in claim 1, further comprising: providing an indication of the update to the metamodel to a user interface (“Meta-modeling environments allow modelers to simulate complex scenarios with high-level modeling tools. Meta-modeling environments provide the user with the basic tools with which the user can create a meta-model. The user-defined meta-model, which may also be referred to as an ontology, can then be processed by the meta-modeling environment to generate an interface that can be used to create one or more instance models. Often, meta-modeling environments provide a visual language, allowing modelers to create detailed models, without requiring low-level knowledge of the underlying classes that make up the model.” [0003]).
Regarding claims 8, and 18, Chougule discloses: The method as in claim 1, wherein the update to the metamodel is sent, by the device, to other learning agents that are part of the monitored system (“According to an embodiment of the disclosure the user interface 102 is configured to provide inputs to the system 100. The user interface 102 is configured to provide a plurality of data sources as a first input. The user interface 102 can also be configured to provide an existing entity relationship (ER) model as a second input. The user interface 102 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite.” [0024]).
Regarding claims 9, and 19, Chougule discloses: The method as in claim 1, wherein the update to the metamodel comprises information obtained by the learning agent that is compressible by an autoencoder (“Also to make the processing faster and smarter for given domain, ontology of knowledge elements can also be created over the set of given entity, attribute, actions within the domain. The knowledge elements are open to link (or contain) elements from NLP ontologies. That way it is a pre-processing of knowledge (data sources) to increase the processing efficiency and accuracy. Over the time, the learning (continual improvement) will make this domain ontology more and more complete and correct. Language structures can also be learnt over the period to avoid processing in some cases and to minimize processing in others.” [0035], note: the ontology data and updates include information compressible by an autoencoder as autoencoders can be used for many types of data including text and relationships. The claim does not require using an autoencoder to perform anything).
Claim(s) 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chougule in view of Ghiran and further in view of Rubin (US 9471885 B1).
Regarding claims 5, and 15, Chougule does not explicitly disclose: The method as in claim 1, wherein the relations are symmetric relations between the concepts.
Rubin teaches: wherein the relations are symmetric relations between the concepts (C22L60-C23-18 disclose symbols the represent sub-symbolic concepts, “In the final analysis, this methodology captures symmetric knowledge to the extent that it blurs with random knowledge. It evidences that the machine learning of rules can be increasingly creative as a function of scale (and attendant processor capabilities). Unlike neural network paradigms, against which it may be compared, this methodology is capable of modus ponens and analogical reasoning. These serve as the basis for human-level symbolic reasoning, which is something that neural networks can never do. Most significantly, storing knowledge as a composition of transforms can exponentially increase the applicability of that knowledge in comparison to literal storage (e.g., simple production systems). This is possible through the use of carefully controlled (i.e., domain-specific) generalization.” C26 L42-56).
Chougule, Ghiran and Rubin are in the same field of endeavor of knowledge and metamodels and are analogous. Chougule discloses metamodels for monitored systems. Ghiran teaches a multilevel metamodel with sensors in a sub-symbolic layer and a symbolic layer having concepts and relations. Rubin discloses symbolic and knowledge graph models. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known metamodel monitoring system of Chougule and Ghiran with the known symbolic reasoning as taught by Rubin to yield predictable results of increased speed (C3 L41-47).
Regarding claims 6, and 16, Chougule does not explicitly disclose: The method as in claim 5, wherein a semantic reasoning engine is used to make an inference about the monitored system.
Rubin teaches: wherein a semantic reasoning engine is used on the symbolic space to make an inference about the monitored system (“FIG. 2 shows a block diagram of an embodiment of a distributed processor system 100 in accordance with the methods disclosed herein. The speed of a case-based reasoning system can be increased through the use of associative memory and/or parallel (distributed) processors, such as shown in FIG. 2. Furthermore, an increase in speed can be obtained if information stores are subdivided for the case knowledge by domain for threaded parallel processing. This is known as segmenting the domain. Such segmentation can be automatically managed by inferred symbolic heuristics, but this will necessarily introduce much redundancy into the system—albeit brain-like. The idea here is to match the candidate case to be acquired against the dynamic case residing at the head of each segment. This case is acquired by those segments, whose head most-closely (not perfectly) matches it based on their possibilities.” C3L36-51).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chougule in view of Ghiran in view of Dehghani et al. (US 20190354567 A1).
Regarding claim 10, Chougule discloses meta-models, however, does not explicitly disclose: The method as in claim 1, wherein making the determination comprises: loading a subset of the metamodel into a short-term memory; and evaluating the subset of the metamodel as a focus of attention.
Deghani teaches: wherein making the determination regarding the performance of the [meta]model at the specified time period comprises: loading a subset of the [meta]model into a short-term memory; and evaluating the subset of the metamodel as a focus of attention (“For subject-verb agreement tasks, the goal is to predict number-agreement between subjects and verbs in English. This task acts as a proxy for measuring the ability of a model to capture hierarchical dependency structure in natural language sentences. The system can use a language modeling training setup, i.e., a next word prediction objective, followed by calculating the ranking accuracy of the target verb at test time. The Universal Transformer was evaluated on subsets of the test data with different task difficulty, measured in terms of agreement attractors the number of intervening nouns with the opposite number from the subject (meant to confuse the model). For example, given the sentence, “The keys to the cabinet,” the objective during training is to predict the verb are (plural). At test time, the ranking accuracy of the agreement attractors is evaluated: i.e. the goal is to rank “are” higher than is in this case. The best LSTM with attention from the literature achieves 99.18% on this task, which outperforms a regular Transformer. The Universal Transformer significantly outperforms standard Transformers and achieves an average result comparable to the current state of the art (99.2%). However, the Universal Transformers (and particularly with dynamic halting) perform progressively better than all other models as the number of attractors increases. TABLE 2 summarizes the results.” [0074]).
Chougule and Deghani are in the same field of endeavor of knowledge and models and are analogous. Chougule discloses metamodels for monitored systems. Ghiran teaches a multilevel metamodel with sensors in a sub-symbolic layer and a symbolic layer having concepts and relations. Deghani discloses short term and attention models. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known metamodel monitoring system of Chougule and Ghiran with the known universal model subset analysis using LSTM and attention as taught by Deghani to yield predictable results of increased performance [0074].
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the updated rejection with new citation from Chougule and new reference Ghiran.
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.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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