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) mental processes – concepts performed in the human mind and mathematical concepts – mathematical calculations.
Regarding claim 1, the claim is directed to mental processes and mathematical concepts.
The limitations ‘augmenting the preprocessed data with supplemental data to form augmented data to enhance predictive analysis; merging the augmented data and applying an ontology process to organize the augmented data based on defined relationships and hierarchies into organized data; generating a knowledge graph from the organized data to visualize and computationally represent the relationships and the entities within the source data; generating node embeddings, edge embeddings, and graph embeddings; identifying a signature based on node embeddings, edge embeddings, and graph embeddings; classifying the signature into a clean category, a fault category, or an outlier category; for any said signature classified in the outlier category, augmenting the source data with additional data and continuing analysis until the signature is reclassified into the clean category or the fault category; predicting a potential system fault for any said signature in the fault category; determining a specific corrective action tailored to the predicted potential system fault for the signature classified in the fault category; monitoring effectiveness of the specific corrective action through a feedback loop mechanism that aggregates performance data from the system following the specific corrective action; refining the knowledge graph and the quantum correlated relationships based on the monitored effectiveness to improve accuracy of future fault predictions for the system’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
The limitations ‘extracting quantum correlated relationships from the knowledge graph using a Hamiltonian transformation followed by a parameterized evolution process, utilizing quantum computing, by transforming the knowledge graph into a quantum Hamiltonian representation utilizing qubits, performing a parameterized evolution process employing quantum sampling to measure correlations between parameters at each stage of the evolution, and compiling the correlations into a quantum attention matrix, distinct quantum circuits being employed for each attention head,,to form quantum processed data; generating an attention matrix from the quantum processed data; applying a multi-head attention mechanism within a Graph Transformer Network (GTN) to the attention matrix for analyzing spatio-temporal patterns and learning from the quantum correlated relationships; employing multi-channel 1x1 convolution within the GTN to process the attention matrix’ are mathematical concepts of mathematical calculations.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘using an edge computing analytics data collection server; refining predictive algorithms used to predict the potential system fault’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The ‘refining predictive algorithms’ is defined in a generic manner.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘collecting source data from a plurality of sources; preprocessing the source data in near-real-time or real-time to extract data logs and structure the data into preprocessed data; generating an alert for corrective action based on the predicted potential system fault’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘automatically initiating the specific corrective action, without human intervention, to prevent the potential system fault, wherein the specific corrective action comprises at least one of deploying a software update, deploying a patch, or adjusting a system configuration tailored to the predicted potential system fault’ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high-level of generality to the judicial exception (MPEP 2106.05(d)) –
KR20050007436A - A solution according to the prior art involves loading patch code into data memory located in a smart card component. Such loading can be done during card customization, or by sending it directly to the applicant of the decoder service, for example. In such a solution, any potential location where a problem may occur may be predicted for "masked code" in code memory, for example in the following cases.
USPN 20060259974A1 - The foregoing problems with the state of the prior art are overcome by the principles of the present invention, which are directed toward a system, method, and computer-readable medium for opportunistically installing a software update on a computer that closes a vulnerability that exists on the computer.
USPN 20160276873A1 – paragraph 0259 - It is noted that the installing a software update may be required to fix a known fault or may be necessary to make use of new software features.
Regarding claim 2, the limitation ‘generating an alert for corrective action based on classification of the signature’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 3, the limitation ‘producing documentation based on said classification of the signature’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 4, the limitation ‘real-time monitoring of the plurality of sources; and providing a real-time feed into the edge computing analytics data collection server for continuous data preprocessing’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering.
Regarding claim 5, the limitation ‘wherein the supplemental data is integrated based on outcomes of the real-time monitoring relevant to operational context of the system, to enrich the preprocessed data with contextual analysis’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 6, the limitation ‘wherein the quantum correlated relationships are extracted based on non-linear relationships within the source data and the source logs’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 7, the limitation ‘wherein the ontology process, applied to the merged data from the edge computing analytics data collection server augmented with the supplemental data, defines a structured model’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 8, the limitation ‘wherein identification of the signature is based on the node embeddings, edge embeddings, and graph embeddings that generated from the quantum correlated relationships’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 9, the limitation ‘wherein the corrective action is automatically initiated without human intervention and is prioritized based on a severity and an immediacy of the predicted potential system fault’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 10, the limitation ‘wherein the feedback loop mechanism further analyzes outcomes of the corrective action to identify patterns of success or areas for improvement, and refines predictive algorithms of the Graph Transformer Network based on the analyzed outcomes to improve accuracy and efficacy of future fault predictions and corresponding preventive measures’ are a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 11, the limitation ‘further comprising the step of generating documentation describing the corrective action and system performance post-intervention’ is a mental process – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Regarding claim 12, the claim is directed to mental processes and mathematical concept.
The limitations ‘aggregate operational data from data sources; tasked with preprocessing the operational data in near real-time or real-time by extracting logs and structuring extracted data into preprocessed data; applies a structured set of relationships and hierarchies to the preprocessed data in order to generate organized data; that transforms the organized data into a knowledge graph, visually and computationally representing the relationships in the organized data; processes the embeddings and categorizes a resulting data signature into a clean category, a fault category, or an outlier category; monitoring effectiveness of the specific corrective action through a feedback loop mechanism that aggregates performance data from the system following the specific corrective action; refining the knowledge graph and the quantum correlated relationships based on the monitored effectiveness to improve accuracy of future fault predictions for the system; for any said data signature categorized in the outlier category, augment the operational data with additional data and cause continued analysis until the data signature is reclassified into the clean category or the fault category; determine a specific corrective action tailored to a system fault corresponding to the data signature categorized in the fault category;’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
The limitations ‘equipped with algorithms for Hamiltonian transformation and parameterized evolution, that extracts quantum correlated relationships from the knowledge graph by transforming the knowledge graph into a quantum Hamiltonian representation utilizing qubits, performing a parameterized evolution process employing quantum sampling to measure correlations between parameters at each stage of the evolution, and compiling the correlations into a quantum attention matrix, distinct quantum circuits being employed for each attention head;; a graph transformer network (GTN) that utilizes a multi-head attention mechanism to analyze the quantum correlated relationships; to generate embeddings based on output from the GTN’ are mathematical concepts of mathematical calculations.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘one or more processors and memory storing instructions that, when executed by the one or more processors; a data collection subsystem; an edge computing analytics module; a knowledge graph construction module; a quantum computing analysis module; an embedding generator; a classification engine; an ontology-based data organization module; a notice generator; corrective action module; refine predictive algorithms used to predict the system fault based on the monitored effectiveness to improve accuracy of future predictions’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The ‘refining’ is defined at a high level of generality.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘provide an alert for corrective action for any said data signature categorized in the fault category; automatically initiate the corrective action to prevent a system fault corresponding to the data signature’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘automatically initiate the specific corrective action, without human intervention, to prevent the potential system fault, wherein the specific corrective action comprises at least one of deploying a software update, deploying a patch, or adjusting a system configuration tailored to the predicted potential system fault’ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high-level of generality to the judicial exception (MPEP 2106.05(d)) –
KR20050007436A - A solution according to the prior art involves loading patch code into data memory located in a smart card component. Such loading can be done during card customization, or by sending it directly to the applicant of the decoder service, for example. In such a solution, any potential location where a problem may occur may be predicted for "masked code" in code memory, for example in the following cases.
USPN 20060259974A1 - The foregoing problems with the state of the prior art are overcome by the principles of the present invention, which are directed toward a system, method, and computer-readable medium for opportunistically installing a software update on a computer that closes a vulnerability that exists on the computer.
USPN 20160276873A1 – paragraph 0259 - It is noted that the installing a software update may be required to fix a known fault or may be necessary to make use of new software features.
Regarding claim 13, the limitation ‘further comprising a notice generator to provide an alert for any said data signature categorized in the fault category’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 14, the limitation ‘wherein corrective action is automatically taken when any said data signature is categorized in the fault category’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Regarding claim 15, the limitation ‘wherein the data collection subsystem further includes real-time monitoring that dynamically captures said operational data’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering.
Regarding claim 16, the limitation ‘wherein the ontology-based data organization module employs an adaptive ontology framework that updates its structure based on evolving data patterns, ensuring that the knowledge graph remains accurate and reflective of current operational dynamics’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 17, the limitation ‘wherein the quantum computing analysis module implements quantum algorithms that are dynamically adjusted based on data observation characteristics to optimize extraction of the quantum correlated relationships for each unique dataset’ are mathematical concepts of mathematical calculations.
Regarding claim 18, the limitation ‘wherein the notice generator includes an automated decision-making process that prioritizes alerts based on severity and immediacy of a predicted fault’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 19, the limitation ‘further comprising a maintenance scheduling interface that communicates with maintenance management systems, allowing for the automated scheduling of preventive maintenance actions based on the alerts and their prioritization’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 20, the claims is directed to mental processes and mathematical concepts.
The limitations ‘organizing the operational data into a structured dataset using an ontology process to establish relationships among data points; generating a knowledge graph from the structured dataset; classifying, based on the spatio-temporal patterns, a signature for the operational data into a clean category, a potential fault category, or an outlier category; augmenting the source data and the source logs for any said signature in the outlier category with additional data and additional logs; continuing analysis, for any said signature classified in the outlier category, until the operational data, augmented with the additional data and additional logs, generates a new signature that can be classified in either the clean category or the potential fault category; monitoring effectiveness of the corrective action through a feedback loop mechanism that aggregates performance data from the system following the corrective action; refining the knowledge graph and the quantum correlated relationships based on the monitored effectiveness to improve accuracy of future fault predictions for the system; determining a specific corrective action tailored to a system fault corresponding to said signature classified in the potential fault category’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
The limitations ‘applying quantum computing techniques to the knowledge graph to extract quantum correlated relationships using Hamiltonian transformation and parameterized evolution by transforming the knowledge graph into a quantum Hamiltonian representation utilizing qubits, performing a parameterized evolution process employing quantum sampling to measure correlations between parameters at each stage of the evolution, and compiling the correlations into a quantum attention matrix, distinct quantum circuits being employed for each attention head; analyzing the quantum correlated relationships using a Graph Transformer Network (GTN) equipped with a multi-head attention mechanism to identify spatio-temporal patterns’ are mathematical concepts of mathematical calculations.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional elements ‘using an edge computing device; predictive algorithms used to predict the system fault based on the monitored effectiveness to improve accuracy of future fault predictions for the computing environment’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)). The ‘refining’ is defined at a high level of generality.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘collecting source data and source logs from a plurality of data sources; preprocessing the source data and the source logs into operational data in real-time to enhance data suitability for in-depth analysis; initiating a corrective action for any classification of said signature in said potential fault category by generating an alert for the corrective action and automatically initiating the specific corrective action, without human intervention, to prevent a system fault corresponding to the signature’ are directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), including data gathering.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements ‘automatically initiating the corrective action to prevent the potential system fault, wherein the specific corrective action comprises at least one of deploying a software update, deploying a patch, or adjusting a system configuration tailored to the predicted potential system fault’ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high-level of generality to the judicial exception (MPEP 2106.05(d)) –
KR20050007436A - A solution according to the prior art involves loading patch code into data memory located in a smart card component. Such loading can be done during card customization, or by sending it directly to the applicant of the decoder service, for example. In such a solution, any potential location where a problem may occur may be predicted for "masked code" in code memory, for example in the following cases.
USPN 20060259974A1 - The foregoing problems with the state of the prior art are overcome by the principles of the present invention, which are directed toward a system, method, and computer-readable medium for opportunistically installing a software update on a computer that closes a vulnerability that exists on the computer.
USPN 20160276873A1 – paragraph 0259 - It is noted that the installing a software update may be required to fix a known fault or may be necessary to make use of new software features.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: ‘data collection subsystem’ in claim 12; ‘edge computing analytics module’ in claim 12; ‘ontology-based data organization module’ in claim 12; ‘knowledge graph construction module’ in claims 12,16; ‘quantum computing analysis module’ in claims 12,17; ‘embedding generator‘ in claim 12; ‘classification engine’ in claim 12; ‘notice generator’ in claims 12,13,18; ‘corrective action module’ in claim 12; ‘augmentation module’ in claim 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
There is no prior art rejection for claims 1-20 because of the inclusion of the following limitations: ‘extracting quantum correlated relationships from the knowledge graph using a Hamiltonian transformation followed by a parameterized evolution process to form quantum processed data; generating an attention matrix from the quantum processed data; applying a multi-head attention mechanism within a Graph Transformer Network (GTN) to the attention matrix for analyzing spatio-temporal patterns and learning from the quantum correlated relationships; e employing multi-channel 1x1 convolution within the GTN to process the attention matrix; predicting a potential system fault for any said signature in the fault category’.
Response to Arguments
Applicant's arguments and amendments filed 07/08/2026 have been fully considered. Concerning Applicant’s arguments of the 101 rejection of IV. The Amendments Are Responsive to the Examiner’s Interview Comments A. Responsive to the Examiner’s comment that Example 47 recited a particular action – The Examiner would like to point out to that despite the specific corrective action, the types of specific corrective actions are well-understood, routine, conventional. Concerning Applicant’s arguments of the 101 rejection B. Responsive to the Examiner’s comment that ‘the quantum attention matrix in and of itself is math’ and the claim now recited the extraction is performed by transforming the knowledge graph into a quantum Hamiltonian representation utilizing qubits and quantum sampling, the Examiner would like to point out that the now recited language is still performing math. The quantum circuits are computational models. They are what is on the hardware. Not the hardware itself. Concerning Applicant’s arguments of the 101 rejection C. Responsive to the Examiner’s comments that the 101 well-understood/routine/convention inquiry and 102/103 prior art inquiry being two separate analysis, the Examiner would like to point out that well-understood, routine, conventional can exist in 101 despite no 102/103 rejection. The corrective actions disclosed in the claims are known types of corrective actions that systems use to correct faults. As disclosed in section 2106.05(d) in the MPEP – “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”. Concerning Applicant’s arguments of V. Step 2A, Prong One – No mathematical concept and No mental process, the claim limitations do in fact recite mathematical concepts. The transformations of the knowledge graph into quantum Hamiltonian representation utilizing qubits involves math and compiling the correlations into a quantum attention matrix involves math. The mental processes are disclosed in the above rejection. Concerning Applicant’s argument of the 101 rejection of VI. Step 2A Prong two – integration into a practical application, the Examiner would like to point out that the mathematical is in the form of mathematical relationships and a quantum attention matrix is inherently math. The qubits and quantum circuits are not defined in terms of the hardware that uses these concepts. The correction actions are well-understood, routine, conventional. The output of the analysis is merely insignificant extra-solution activity. The edge computing is disclosed at a high-level of generality. There are no ‘other meaningful limitations’ because the corrective actions are well-understood, routine, conventional. They are well known corrective actions. The use of mathematical concepts and mental processes does not involve the use of a specific technological improvement.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. USPN 20210303381 - abstract - A system and method of automating fault prediction and remediation for a multi-tenant system is disclosed. The system and method offer an intelligent augmentation of a multi-tenant system by automating the harvesting and processing of raw data logs generated by the various aspects of the platform as well as the implementation of an appropriate response. In some embodiments, the proposed system includes a hybrid model that can be configured to offer both assisted and unassisted errors. The incorporation of a dynamic learning algorithm minimizes operation errors for any set of computing units. Potential system faults can be detected with little to no human intervention and allow for an unattended platform that collects performance data across the system from a wide range of sources to provide centralized and automated fault prediction, as well as expedited automated resolutions to such faults that depend on little to no human intervention.; [0056] Knowledge graphs present connections (relationships) in the data, and allows for the ready transfer of new data items as they are added into the data pool. In addition, the meaning of the data is encoded alongside the data in the graph, in the form of the ontology. Because the underlying basis of a knowledge graph is the ontology, specifying the semantics of the data, the knowledge graph further allows implicit information to be derived from explicitly asserted data that would otherwise be hard to discover. For example, classifications and other data obtained during end-user or administrator interactions can be used to create nodes and assign word sequences to a node in a conversation graph as well as form corresponding transitional paths. Generally, a conversation graph can be understood to represent a plurality of conversations that have occurred and were directed to a particular task, intent, goal, and/or objective. The information provided by a knowledge graph can be used by the artificial intelligence engine to both detect potential faults, as well as determine the preferred course of remediation.;
USPN 20250150326 - abstract - A computing device may implement the techniques described in this disclosure. The computing device may include processing circuitry configured to execute an analysis framework system, and memory configured to store time series data. The analysis framework system may create, based on the time series data, a knowledge graph comprising a plurality of first nodes in the network system referenced in the time series data interconnected by edges. The analysis framework system may cause a graph analytics service of the analysis framework system to receive a graph analysis request comprising a request to determine a fault propagation path, a request to determine changes in the knowledge graph, a request to determine an impact of an emulated fault, or a request to determine an application-to-network path. The analysis framework system may also cause the graph analytics service to determine a response to the graph analysis request, and output the response.; 0007 - determine, based on the time series data, one or more anomalies in the performance of the network system; create, based on the time series data, a knowledge graph comprising first nodes in the network system referenced in the time series, the first nodes representing elements residing at one or more of a plurality of network service layers associated with the network system; determine, in response to detecting the one or more anomalies, and based on the knowledge graph and a machine learning (ML) model trained with previous time series data, a causality graph, wherein the causality graph includes second nodes associated with the performance indicators, wherein edges between the first nodes and the second nodes indicate relationships between the first nodes and the second nodes, and wherein the knowledge graph and the causality graph each includes edges between one or more of the first nodes and one or more of the second nodes that are associated with elements residing at different network service layers of the plurality of network service layers; determine a weighting for each of the edges in the causality graph; determine, based on the edges in the causality graph, one or more candidate root causes associated with the one or more anomalies; determine a ranking of the one or more candidate root causes based on the weighting of the edges in the causality graph; and output at least a portion of the ranking.;
EP 4343621 – abstract - There is presented a method for generating a Graph Neural Network, GNN. The GNN comprising a first layer and a second layer subsequent to the first layer. The GNN is associated with a graph comprising a plurality of graph nodes and edges. For the first layer, a first graph node is represented by a first set of one or more first node features whilst a second graph node is represented by a first set of one or more second node features. The first and second graph node being connected by an edge. The method comprises generating, for the second layer, a second set of one or more first node features. Electromagnetic, EM, radiation is input to at least a first qubit and a second qubit of a quantum computer. The first graph node represented by the first qubit. The second graph node represented by the second qubit. The EM radiation interacts the first qubit with the second qubit. The qubit quantum states are measured and an aggregation factor is determining from the measurements that is used with the first set of one or more second node features to generate the second set of one or more features. The method then generates the GNN at least based upon the second set of one or more first node features.; The present application provides for using a quantum computer to implement at least part of the aggregation processes in training a GNN. For example, determining the normalised adjacency matrix à of a GCN or a matrix of α.sub.ij such as [A].sub.ij.sup.z for a GAT. The use of a quantum computer in this manner allows such quantum enhanced GNNs to distinguish between graphs that would nominally fail the WL test. Furthermore, such quantum enhanced GNNs may generally present more accurate solutions than classical GNNs. Such quantum enhanced GNNs also present an alternative way to generate a GNN.; The qubit states represent the final state of the QC system after a time evolution given by the Hamiltonian. This Hamiltonian may be based off an Ising Hamiltonian or another Hamiltonian such as an XY Hamiltonian, however in this example the Hamiltonian is an XY Hamiltonian representing the energy state of a quantum system wherein the energy states of rubidium neutral atoms are two dipole-coupled Rydberg states. Such a Hamiltonian is described elsewhere herein. The method can derive what α.sub.ij is for nodes 'i' and 'j' by looking at the final states of the atoms.
THIS ACTION IS MADE FINAL. 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 Yolanda L Wilson whose telephone number is (571)272-3653. The examiner can normally be reached M-F (7:30 am - 4 pm).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 571-272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Yolanda L Wilson/Primary Examiner, Art Unit 2113
Prosecution Insights