DETAILED ACTION
Status of the Application
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 .
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 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.
This action is a Final Action on the merits in response to the application filed on 05/13/2026.
Claims 1, 4, 9, 11-12 and 15-18 have been amended.
Claim 10 has been cancelled.
Claims 1-9 and 11-21 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 103 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments, however a new 103 rejections was added.
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 1-9 and 21 are directed towards an method, claims 11-16 are directed towards a system, and claims 17-20 are directed towards a computer program product, all of which are among the statutory categories of invention.
Claims 1-9 and 11-21 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without 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 recites at least one step or act, including training normalized data. Thus, the claim is to a process, which is 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.
With respect to claims 1-9 and 11-21, the independent claims (claims 1, 11, and 17) are directed to managing of data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
a computer-implemented method of autonomous environmental sustainability control related to a computing environment performing a functional objective, the computer-implemented method comprising:
identifying, using the learned set of dynamic key performance indicators, an anomaly;
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as business relations; mental processes which includes concepts performed in the human such as observation and evaluation (i.e. determining a size of the image) (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction; observation and evaluation, then it falls within the “method of organizing human activity” and “mental processes” groupings of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (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). The claim recites the additional elements of artificial intelligence model, autonomous environmental sustainability control, computing environment, swarm intelligence (claim 11 computer system, memory, processor, artificial intelligence model, autonomous environmental sustainability control, computing environment, swarm intelligence; Claim 17 computer program product, computer readable storage media, artificial intelligence model, autonomous environmental sustainability control, computing environment, swarm intelligence). The claims recite the steps are performed by the artificial intelligence model, autonomous environmental sustainability control, computing environment, swarm intelligence.
The limitations of
obtaining normalized data from heterogeneous data obtained from a plurality of data sources, the normalized data being obtained using swarm intelligence-based data normalization processing, the heterogeneous data relating, at least in part, to the functional objective and to at least one environmental sustainability metric of the computing environment;
training, using the normalized data, an artificial intelligence model to learn dynamic key performance indicators relating, at least in part, computing environment performance of the functional objective to environmental sustainability;
using the artificial intelligence model to learn a set of dynamic key performance indicators to relate current computing environment performance of the functional objective to environmental sustainability;
generating one or more actions to remediate a risk associated with the anomaly, the one or more actions facilitating the autonomous environmental sustainability control related to the computing environment performing the functional objective.
are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by artificial intelligence model, autonomous environmental sustainability control, computing environment, swarm intelligence. The artificial intelligence model, autonomous environmental sustainability control, computing environment, swarm intelligence are recited at a high level of generality. In limitation (a), artificial intelligence model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The artificial intelligence model are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites artificial intelligence model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
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 amounts 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. As explained with respect to Step 2A, Prong Two, the additional elements are the artificial intelligence model, autonomous environmental sustainability control, computing environment, swarm intelligence. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering. Then, the artificial intelligence model is a machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0149 “The forecast component of the system uses machine learning, in one embodiment, for use in forecasting, to predict supply/demand/inventory of the system of record, and improve performance. Machine learning can analyze historical data to understand the demand, supply, and inventory, and then forecast future demand, supply, and inventory, in one embodiment. The governance components analyze any anomalies to track the progress of sustainability improvements, and generate recommendations of one or more actions as a roadmap to move to a next level for a desired sustainability state. In one or more embodiments, artificial intelligence models can leverage machine learning to forecast risk.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
obtaining normalized data from heterogeneous data obtained from a plurality of data sources, the normalized data being obtained using swarm intelligence-based data normalization processing, the heterogeneous data relating, at least in part, to the functional objective and to at least one environmental sustainability metric of the computing environment;
training, using the normalized data, an artificial intelligence model to learn dynamic key performance indicators relating, at least in part, computing environment performance of the functional objective to environmental sustainability;
using the artificial intelligence model to learn a set of dynamic key performance indicators to relate current computing environment performance of the functional objective to environmental sustainability;
generating one or more actions to remediate a risk associated with the anomaly, the one or more actions facilitating the autonomous environmental sustainability control related to the computing environment performing the functional objective.
are recited at a high level of generality. These elements amount to receiving and generating data, are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of an artificial intelligence model, autonomous sustainability control, swarm intelligence, memory, processor, computer system, computer program product, computer readable storage media to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. 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).
Dependent claims 2-9, 12-16, and 18-21 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims. Dependent claims 4, 11, 12, 18 recites computing environment; claims 6 and 7 recite artificial intelligence model to optimize the set of data; claim 16 recites swarm intelligence to process data. The dependent claims 2-9, 12-16, and 18-21 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-9, 12-16, and 18-21 recites artificial intelligence model, autonomous sustainability control, swarm intelligence, memory, processor, computer system, computer program product, computer readable storage media which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-9, 12-16, and 18-21 recites artificial intelligence model, autonomous sustainability control, memory, processor, computer system, computer program product, computer readable storage media, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-9, 12-16, and 18-21 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 11, and 17. Therefore claims 2-9, 12-16, and 18-21 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 of this title, 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.
Claims 1, 2, 4, 9, 11, 12, 15-18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20220197625, Franchitti to hereinafter Franchitti in view of United States Patent Publication US 20210209483, Bose, et al.
Referring to Claim 1, Franchitti teaches a computer-implemented method of autonomous environmental sustainability control related to a computing environment performing a functional objective, the computer-implemented method comprising:
obtaining normalized data from heterogeneous data obtained from a plurality of data sources, the normalized data being obtained using swarm intelligence-based data (See Bose) normalization processing, the heterogeneous data relating, at least in part, to the functional objective and to at least one environmental sustainability metric of the computing environment; (
Franchitti: Sec. 0140, The results in Table 1C include the results of Table 1B as well as the Heterogeneous Data Integration Platform as a service solution, by virtue of its match to the newly-added criterion (“Innovation Area/Process”)
Franchitti: Sec. 0321, Thinking in patterns did help relate some patterns to our DKME technology environment requirement model and revealed that our software solution should leverage characteristics from several architectural styles including Cloud, P2P, and Microservices. We also identified the need to leverage the EAM, BPM and Intelligent Active and/or autonomous Agents architectural patterns as part of our software solution.
Franchitti: Sec. 0415, Beginning with the metrics in the taxonomy hierarchy, metric values are normalized. Criteria values are normalized to avoid calculations with criteria representing different quantitative units or qualitative indices. An assumption regarding normalization is that a particular criteria value can be divided by a norm aligned to a desired criteria value. In this regard, the normalized values are oriented towards the desirable criteria value.
Franchitti: Sec. 0518, Agents can be defined as computer systems that are capable of active and/or autonomous action in some environment in order to meet their design objectives. Environments have various properties such as being accessible vs. inaccessible (i.e., depending on whether or not it is possible to obtain complete, accurate, up-to-date information about the environment), deterministic vs. non-deterministic (i.e., depending on whether or not it is possible to guarantee the effect of any action within the environment), episodic vs. non-episodic (i.e., depending on whether or not the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios), static vs. dynamic ((i.e., depending on whether or not the environment can be assumed to remain unchanged except by the performance of the actions by the agent), discrete vs. continuous (i.e., depending on whether or not there are a fixed number of actions and percepts in the environment). For example, the physical world is inaccessible, non-deterministic, and dynamic; Mail sorting is an episodic environment; Taxi driving is a continuous environment. The most complex, general class of environments are those that are inaccessible, non-deterministic, non-episodic, dynamic, and continuous. An agent will typically sense its environment (by physical sensors in the case of agents situated in part of the real world, or by software sensors in the case of software agents), and will have available a repertoire of actions that can be executed to modify the environment, which may appear to respond non-deterministically to the execution of these actions. Any control system can be viewed as an agent (e.g., thermostat, space probes).
Franchitti: Sec. 0520, Using this formalism, the interaction of an intelligent agent and its environment can be represented as an history or sequence of transitions from states to states as a result of successive actions. Reactive agents base their decision making entirely on the present, with no reference at all to history. Agent architectures typically separate the agent's decision function into perception and action subsystems. Perception is a function p that maps environment states in S to percepts in a set of perceptions P (e.g., p: S->P). Then an action is now a function action: P*->A that maps sequences of percepts to actions. Rather than just rely on history to influence their decision making, agents can have state (i.e., internal data structures) to record information about the environment state and history).);
Franchitti teaches the analyzing of cloud and the environments, which includes evaluation of functional performance objectives and autonomous sustained controls in a computing environment, which is similar to Applicant’s spec at 0003 and 0151
training, using the normalized data, an artificial intelligence model to learn dynamic key performance indicators relating, at least in part, computing environment performance of the functional objective to environmental sustainability (
Franchitti: Sec. 0132, in any order and in any combination (depending upon the implementation or application): parsing the request; normalizing or reformatting the request (e.g., such that a logical “OR” relationship is imparted between terms of the request); querying a repository organized according to and/or storing a taxonomy; restricting a query associated with the request to a single domain or set of domains; locally querying the compute node (i.e., querying the compute node at which the request was received); querying a remote compute node within the same network as the receiving compute node (e.g., where the common network is associated with a relevant domain, as discussed further below); and/or querying a remote compute node within a network that is outside (and separate from) the network of the receiving compute node (e.g., where the outside network has a domain type that differs from a domain type of the network to which the receiving node belongs). Each of the foregoing processes can be performed by the system automatically/autonomously (i.e., without further user input). The foregoing queries could be performed in succession, as needed (e.g., terminating when a match occurs, to minimize computation costs), or in parallel (e.g., to maximize the volume of applicable results).
Franchitti: Sec. 0183, Recent advances in the artificial intelligence (AI) field and in deep learning, have made it possible to build software applications that are trained to perform specific human tasks faster and more reliably.
Franchitti: Sec. 0192, to realize our vision and address the challenge of harnessing knowledge at many levels, we developed an ecosystem and framework that combines the use of taxonomies derived from expert knowledge and machine learning to manage (i.e., create, discover, classify, and operate) intelligent knowledge-driven active and/or autonomous business solutions that are driven via individualized learning as well as support provided by a knowledge backbone.
Franchitti: Sec. 0281, It is used to train and deploy machine and deep learning algorithms on various types of operational performance data provided by the DKME (e.g., end users' requests, taxonomy/solution matches characteristics, business solutions logs, solutions users' feedback on needs understanding and solutions availability and suitability, and DKME solutions usage, and satisfaction feedback logs across the global P2P constellation that may include digital ecosystem feedback from sources such as social media and social review sites).
Franchitti: Sec. 0547, Sentiment analysis is a common type of predictive analytics. The input to the model is plain text and the output of that model is a sentiment score. Predictive models are typically trained on historical patterns and behaviors.
Franchitti: Sec. 0559, The DKME prescriptive analytics function uses a predictive model and leverages DKME actionable data (e.g., availability of ontology and related taxonomy) as well as the DKME feedback system that tracks feedback produced as a result of action taken (e.g., ability to understand user requests when using available taxonomies) …Prescriptive analytics are used across the board within the DKMF to gain insight into how the architecture, ontologies and related taxonomies, and business solutions may behave in the near term and take actions to adjust them accordingly. Intelligent active and/or autonomous business solutions architectures (including the DKMF framework) rely on an EAF meta-framework and are designed to align with certain key performance indicators (KPIs) in mind, which drive the choice and use of certain recommended patterns.);
Franchitti teaches the analyzing of cloud and the environments, which includes the training and normalizing of data by classifying data within machine learning that learns KPI for evaluating of functional performance objectives and autonomous sustained controls in a computing environment, which is similar to Applicant’s spec at 0003 and 0151.
using the artificial intelligence model to learn a set of dynamic key performance indicators to relate current computing environment performance of the functional objective to environmental sustainability (
Franchitti: Sec. 0559, Prescriptive analytics techniques in this case make it possible to optimize processes so they operate within their KPI.
Franchitti: Sec. 0560, Another core DKMF solutions management functionality that leverages cognitive analytics techniques is the underlying BPM and service integration and composition approach. BPMN processes must adhere to KPIs that are injected by the ArchBPM™ tool and used as a way to provide feedback as to the health of processes at execution time…cognitive analytics techniques make it possible to discover feedback anomalies that help optimize processes so they operate within their KPI boundaries as well as optimize the choice of services to maximize QoS.
Franchitti: Sec. 0518, Agents can be defined as computer systems that are capable of active and/or autonomous action in some environment in order to meet their design objectives. Environments have various properties such as being accessible vs. inaccessible (i.e., depending on whether or not it is possible to obtain complete, accurate, up-to-date information about the environment), deterministic vs. non-deterministic (i.e., depending on whether or not it is possible to guarantee the effect of any action within the environment), episodic vs. non-episodic (i.e., depending on whether or not the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios), static vs. dynamic ((i.e., depending on whether or not the environment can be assumed to remain unchanged except by the performance of the actions by the agent), discrete vs. continuous (i.e., depending on whether or not there are a fixed number of actions and percepts in the environment). For example, the physical world is inaccessible, non-deterministic, and dynamic; Mail sorting is an episodic environment; Taxi driving is a continuous environment. The most complex, general class of environments are those that are inaccessible, non-deterministic, non-episodic, dynamic, and continuous. An agent will typically sense its environment (by physical sensors in the case of agents situated in part of the real world, or by software sensors in the case of software agents), and will have available a repertoire of actions that can be executed to modify the environment, which may appear to respond non-deterministically to the execution of these actions. Any control system can be viewed as an agent (e.g., thermostat, space probes).
Franchitti: Sec. 0520, Using this formalism, the interaction of an intelligent agent and its environment can be represented as an history or sequence of transitions from states to states as a result of successive actions. Reactive agents base their decision making entirely on the present, with no reference at all to history. Agent architectures typically separate the agent's decision function into perception and action subsystems. Perception is a function p that maps environment states in S to percepts in a set of perceptions P (e.g., p: S->P). Then an action is now a function action: P*->A that maps sequences of percepts to actions. Rather than just rely on history to influence their decision making, agents can have state (i.e., internal data structures) to record information about the environment state and history).
Franchitti: Sec. 0698, In addition to the above capabilities, Archemy™ can use various types of algorithms (e., fuzzy matching, distance-vector based, and ML) to search its knowledge repository. Archemy™ can also use machine learning and/or artificial intelligence methods to perform intelligent autonomous component adaptations (a.k.a., transmutations) in real-time.);
Franchitti teaches the analyzing of cloud and the environments, which includes artificial intelligence and the use of machine learning that learns KPI for evaluating of functional performance objectives and autonomous sustained controls in a computing environment, which is similar to Applicant’s spec at 0003 and 0151.
identifying, using the learned set of dynamic key performance indicators, an anomaly (
Franchitti: Sec. 0560, cognitive analytics techniques make it possible to discover feedback anomalies that help optimize processes so they operate within their KPI boundaries as well as optimize the choice of services to maximize QoS);
Franchitti teaches the analyzing of cloud and the environments, which includes artificial intelligence and the use of machine learning that learns KPI for evaluating of functional performance in a computing environment, and consider anomalies.
generating one or more actions to remediate a risk associated with the anomaly, the one or more actions facilitating the autonomous environmental sustainability control related to the computing environment performing the functional objective (
Franchitti: Sec. 0465, It is typically hard for enterprises to predict their product line requirements well into the future and have the time and resources for a long development cycle. In this case, using the DKME minimizes the upfront costs and risks since it is possible to leverage an existing software reuse framework. A reactive investment approach typically takes an incremental approach to assets building. Reusable assets are developed as the reuse opportunity arises while developing products. Unless there is a clear problem boundary and projected requirements variations, it may be difficult to apply this approach.
Franchitti: Sec. 0559, Intelligent active and/or autonomous business solutions architectures (including the DKMF framework) rely on an EAF meta-framework and are designed to align with certain key performance indicators (KPIs) in mind, which drive the choice and use of certain recommended patterns. Feedback on business solutions architecture performance adequacy obtained via detection of log anomalies in solution components' generated logs (e.g., event handler logs, KMP history logs) can help improve the choice of structural or process patterns used to assemble intelligent active and/or autonomous business solutions.
Franchitti: Sec. 0518, Agents can be defined as computer systems that are capable of active and/or autonomous action in some environment in order to meet their design objectives. Environments have various properties such as being accessible vs. inaccessible (i.e., depending on whether or not it is possible to obtain complete, accurate, up-to-date information about the environment), deterministic vs. non-deterministic (i.e., depending on whether or not it is possible to guarantee the effect of any action within the environment), episodic vs. non-episodic (i.e., depending on whether or not the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios), static vs. dynamic ((i.e., depending on whether or not the environment can be assumed to remain unchanged except by the performance of the actions by the agent), discrete vs. continuous (i.e., depending on whether or not there are a fixed number of actions and percepts in the environment). For example, the physical world is inaccessible, non-deterministic, and dynamic; Mail sorting is an episodic environment; Taxi driving is a continuous environment. The most complex, general class of environments are those that are inaccessible, non-deterministic, non-episodic, dynamic, and continuous. An agent will typically sense its environment (by physical sensors in the case of agents situated in part of the real world, or by software sensors in the case of software agents), and will have available a repertoire of actions that can be executed to modify the environment, which may appear to respond non-deterministically to the execution of these actions. Any control system can be viewed as an agent (e.g., thermostat, space probes).
Franchitti: Sec. 0520, Using this formalism, the interaction of an intelligent agent and its environment can be represented as an history or sequence of transitions from states to states as a result of successive actions. Reactive agents base their decision making entirely on the present, with no reference at all to history. Agent architectures typically separate the agent's decision function into perception and action subsystems. Perception is a function p that maps environment states in S to percepts in a set of perceptions P (e.g., p: S->P). Then an action is now a function action: P*->A that maps sequences of percepts to actions. Rather than just rely on history to influence their decision making, agents can have state (i.e., internal data structures) to record information about the environment state and history).
Franchitti: Sec. 0647, The HealthMeter™ solution uses machine learning and deep learning algorithms as needed to categorize the patients that exhibits symptoms in three (or more) risk-zones associated with the probability of developing the disease being monitored (e.g., green or safe if no risk, yellow or warning if low risk, red or danger for high risk).
Franchitti: Sec. 0703, The Archemy™ platform binds the need to the information by powering the iTracker™ active business solution that provides a risk analysis and management platform for the insurance industry.).
Franchitti teaches determining various risk and how to correct and leverage risk while considering anomalies and using autonomous sustained controls in a computing environment.
Franchitti does not explicitly teach data being obtained using swarm intelligence-based data.
However, Bose teaches data being obtained using swarm intelligence-based data (
Bose: Sec. 0094, A technology using blockchain in a swarm environment has not been disclosed and because collective properties of a swarm change over time, the natures of swarm and blockchain are opposite, and thus there is a need to develop an intelligence system capable of using the swarm and the blockchain including dynamic attributes of the swarm. Also, considering various future smart devices, there is also a need to develop an intelligence system capable of accommodating properties of both homogeneous and heterogeneous swarms. The present disclosure may correspond to an intelligence system including blockchain rules that are generated, changed, updated, and managed by a current state and property of the swarm.
Bose: Sec. 0160, FIG. 5 is a diagram for describing a swarm environment according to an embodiment. Referring to FIG. 5, the swarm environment may include a swarm (including homogeneous or heterogeneous nodes) interacting with another swarm and interacting with an environment to solve a problem or achieve a desired behavior. In other words, the swarm environment may denote a target to be interacted by a swarm-based system interacting with an environment or with each other or by a swarm control device including a plurality of devices.
Bose: Sec. 0162, The swarm intelligence unit 501 may cause generation, formation, and evolution of a swarm, based on a given series of simple rules. For example, the swarm intelligence unit 501 defines the simple rules for local interaction of devices in the swarm, and generate the simple rules to adapt to a state, property, attribute, and parameter of the swarm. The swarm intelligence unit 501 may transmit, to the blockchain unit 602, data about the current state of the swarm, rules, parameters, properties, and attributes of the swarm).
Bose teaches using swarm intelligence-based data within artificial intelligence and blockchain, Additionally, Bose teaches the classifying data, which is similar to Applicant’s spec at 0006 for disclosing normalizing data.
Franchitti and Bose are all directed to the analysis of machine learning (See Franchitti at 0004, 0157, 0182; Bose at 0002, 0267, 0269). Franchitti discloses that additional elements, such as the autonomous application software units can be considered (See Franchitti at 0265). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Franchitti, which teaches detecting and repairing information technology problems in view of Bose, to efficiently apply analysis of machine learning to by improving the processing of data by including the use of extra tools. (See Bose at 0003, 0007, 0093).
Referring to Claim 2, Franchitti teaches the computer-implemented method of claim 1, wherein the obtaining comprises classifying, at least in part, the heterogeneous data into classified data (
Franchitti: Sec. 0394, machine learning classification-based comparison method (i.e., comparison method that uses decision trees), as well as neural network-based comparison methods.
Franchitti: Sec. 0415, These include classification models of types of reuse, reuse library metrics, cost benefit models, maturity assessment models, amount of reuse metrics, failure modes models, and reusability assessment models.
Franchitti: Sec. 0527, There are five classes of agents that have been classified in the literature based on their degree of perceived intelligence and capability including: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents).
Referring to Claim 4, Franchitti teaches the computer-implemented method of claim 1, wherein the identifying further comprises:
using the learned set of dynamic key performance indicators to detect an anomaly in the normalized data (
Franchitti: Sec. 0559, Intelligent active and/or autonomous business solutions architectures (including the DKMF framework) rely on an EAF meta-framework and are designed to align with certain key performance indicators (KPIs) in mind, which drive the choice and use of certain recommended patterns. Feedback on business solutions architecture performance adequacy obtained via detection of log anomalies in solution components' generated logs (e.g., event handler logs, KMP history logs) can help improve the choice of structural or process patterns used to assemble intelligent active and/or autonomous business solutions.);
based on detecting the anomaly, identifying an incident and predicting an associated risk related to the environmental sustainability and the computing environment performance of the functional objective (
Franchitti: Sec. 0447, Sustainability of reuse (i.e., means of sustaining reuse programs on a long-term basis such as links between reuse and domain engineering and corporate strategy, identifying what should be made reusable based on envisioned ROI or extent of technology transfers needed).
Franchitti: Sec. 0518, Agents can be defined as computer systems that are capable of active and/or autonomous action in some environment in order to meet their design objectives. Environments have various properties such as being accessible vs. inaccessible (i.e., depending on whether or not it is possible to obtain complete, accurate, up-to-date information about the environment), deterministic vs. non-deterministic (i.e., depending on whether or not it is possible to guarantee the effect of any action within the environment), episodic vs. non-episodic (i.e., depending on whether or not the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios), static vs. dynamic ((i.e., depending on whether or not the environment can be assumed to remain unchanged except by the performance of the actions by the agent), discrete vs. continuous (i.e., depending on whether or not there are a fixed number of actions and percepts in the environment). For example, the physical world is inaccessible, non-deterministic, and dynamic; Mail sorting is an episodic environment; Taxi driving is a continuous environment. The most complex, general class of environments are those that are inaccessible, non-deterministic, non-episodic, dynamic, and continuous. An agent will typically sense its environment (by physical sensors in the case of agents situated in part of the real world, or by software sensors in the case of software agents), and will have available a repertoire of actions that can be executed to modify the environment, which may appear to respond non-deterministically to the execution of these actions. Any control system can be viewed as an agent (e.g., thermostat, space probes).
Franchitti: Sec. 0520, Using this formalism, the interaction of an intelligent agent and its environment can be represented as an history or sequence of transitions from states to states as a result of successive actions. Reactive agents base their decision making entirely on the present, with no reference at all to history. Agent architectures typically separate the agent's decision function into perception and action subsystems. Perception is a function p that maps environment states in S to percepts in a set of perceptions P (e.g., p: S->P). Then an action is now a function action: P*->A that maps sequences of percepts to actions. Rather than just rely on history to influence their decision making, agents can have state (i.e., internal data structures) to record information about the environment state and history).
Franchitti: Sec. 0647, The HealthMeter™ solution uses machine learning and deep learning algorithms as needed to categorize the patients that exhibits symptoms in three (or more) risk-zones associated with the probability of developing the disease being monitored (e.g., green or safe if no risk, yellow or warning if low risk, red or danger for high risk). ).
Referring to Claim 9, Franchitti teaches the computer-implemented method of claim 1, further comprising executing autonomously the one or more actions to make one or more real-time changes to remediate the risk and facilitate reaching a desired environmental sustainability state (
Franchitti: Sec. 0151, Alternatively or in addition, in some embodiments, one or more compute nodes of the network can actively, dynamically and/or autonomously implement changes to the taxonomy and/or to the Enterprise Catalog, in real-time, in response to one or more of: machine-observed system performance, alerts detected, user-observed system performance, and/or qualitative user feedback (i.e., indicators of user “sentiment”). As used herein, “active” (or “actively”) refers to the implementation of adaptations in an intelligent (e.g., using AI/ML), autonomous (e.g., using AI-driven autonomous systems capabilities and/or robotics), and/or collaborative (e.g., using an intelligent agent protocol to enable nodes to communicate with other intelligent networked nodes and optionally humans to create augmented/assisted intelligence and more precise autonomous reactions) fashion.
Franchitti: Sec. 0647, The HealthMeter™ solution uses machine learning and deep learning algorithms as needed to categorize the patients that exhibits symptoms in three (or more) risk-zones associated with the probability of developing the disease being monitored (e.g., green or safe if no risk, yellow or warning if low risk, red or danger for high risk). The HealthMeter™ DKMF-based business solution is a real-time,
Franchitti: Sec. 0653, Finally, various target risk-zones were defined for the dataset using contextual research results provided by the DKME actors. The HealthMeter™ solution is able to provide information about patients' lymphedema condition based on real-time processing of symptomatic statuses via machine-learning algorithms that provide insights as to the potential future condition of new patients.).
Claims 11, 12, 15 recite limitations that stand rejected via the art citations and rationale applied to claims 1, 4, 9. Regarding the computer system comprising:
a memory; at least one processor in communication with the memory (
Franchitti: Sec. 0011, Each compute node from the second set of compute nodes includes a memory storing code representing instructions to cause a processor to receive, at a processor of that compute node, a signal representing system data associated with at least one system capability request associated with a system capability. ),
Referring to Claim 16, Franchitti teaches the computer system of claim 11, Franchitti does not explicitly teach wherein the heterogeneous data is obtained from a plurality of different systems.
However, Bose teaches wherein the heterogeneous data is obtained from a plurality of different systems (
Bose: Sec. 0094, A technology using blockchain in a swarm environment has not been disclosed and because collective properties of a swarm change over time, the natures of swarm and blockchain are opposite, and thus there is a need to develop an intelligence system capable of using the swarm and the blockchain including dynamic attributes of the swarm. Also, considering various future smart devices, there is also a need to develop an intelligence system capable of accommodating properties of both homogeneous and heterogeneous swarms. The present disclosure may correspond to an intelligence system including blockchain rules that are generated, changed, updated, and managed by a current state and property of the swarm.
Bose: Sec. 0160, FIG. 5 is a diagram for describing a swarm environment according to an embodiment. Referring to FIG. 5, the swarm environment may include a swarm (including homogeneous or heterogeneous nodes) interacting with another swarm and interacting with an environment to solve a problem or achieve a desired behavior. In other words, the swarm environment may denote a target to be interacted by a swarm-based system interacting with an environment or with each other or by a swarm control device including a plurality of devices.
Bose: Sec. 0162, The swarm intelligence unit 501 may cause generation, formation, and evolution of a swarm, based on a given series of simple rules. For example, the swarm intelligence unit 501 defines the simple rules for local interaction of devices in the swarm, and generate the simple rules to adapt to a state, property, attribute, and parameter of the swarm. The swarm intelligence unit 501 may transmit, to the blockchain unit 602, data about the current state of the swarm, rules, parameters, properties, and attributes of the swarm).
Franchitti and Bose are all directed to the analysis of machine learning (See Franchitti at 0004, 0157, 0182; Bose at 0002, 0267, 0269). Franchitti discloses that additional elements, such as the autonomous application software units can be considered (See Franchitti at 0265). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Franchitti, which teaches detecting and repairing information technology problems in view of Bose, to efficiently apply analysis of machine learning to by improving the processing of data by including the use of extra tools. (See Bose at 0003, 0007, 0093).
Claims 17 and 18 recite limitations that stand rejected via the art citations and rationale applied to claims 1 and 4. Regarding the computer program product comprising:
one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media readable by at least one processing circuit to perform (
Franchitti: Sec. 0013, the non-transitory processor-readable medium can also store code representing instructions to cause a processor to send, prior to sending the first signal, an access token to a remote server for authentication, and to receive, in response to sending the access token, a confirmation message from the remote server. Alternatively or in addition, the non-transitory processor-readable medium can also store code representing instructions to cause a processor to at least one of actively and autonomously via the agent, detect an update to a taxonomy associated with at least one candidate application software unit from a plurality of candidate application software units, and to generate, via the agent and in response to detecting the software update, an alert referencing the update to the taxonomy. Alternatively or in addition, the non-transitory processor-readable medium can also store code representing instructions to cause a processor to: (1) receive a notification that a new software component is available; (2) send, in response to the notification, a request for the new software component; (3) receive, in response to the request, the new software component; and (4) integrate, via the agent, the new software component into the software package.
Franchitti: Sec. 0717, a computer-readable medium may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers)
Referring to Claim 21, Franchitti teaches the computer-implemented method of claim 1, wherein the computing environment comprises a data center environment, and the one or more actions comprise one or more autonomous changes in one or more components of the data center environment to achieve a desired sustainability state for the data center environment (
Franchitti: Sec. 0247, The DKME and its associated DKMF platform allow support for a “Fog” and “Edge” computing models as well as the inclusion of capabilities for analyzing and acting on IoT data. Fog and Edge models improve the agility of Cloud service deployments in addition to bringing computing resources closer to devices and end-users as part of micro data centers located at the edge of the network. A business solution may be programmed to act as a solution user:
Franchitti: Sec. 0247, These bits of information, which we often take for granted, are unstructured. Furthermore, unlike big data analytics, most cognitive analytics tasks do not render themselves to the map-reduce style of distributed computing. Due to completely unpredictable, unstructured and string-based data the computational complexity of the reduce step tends to be the same order of magnitude as the complexity of the original problem. As a result, when used to process big data, cognitive analytics systems must be designed to execute as much as possible in a single node and use problem-specific distributed architectures where needed. That is partly why IBM Watson is a hardware-based solution, a derivative of the Blue Gene supercomputer. This is also one of the reasons why Google, Facebook, Apple, and other companies dealing with large quantities of text, design their data centers using super-fast, flash-based Direct Attached Storage rather than prevalent network storage (SAN or NAS)
Franchitti: Sec. 0550, It is also acceptable to allow active and/or autonomous operations in some environments, including deep space probes operating far from Earth, or supply chain monitoring and ordering processes, or common help desk support functions, or massive data center operations, and many more applications involving smart devices, smart metering systems, and smart data (i.e., self-aware data, whose metadata tags were self-generated through automated cognitive learning algorithms at the point when the data were ingested into the big data system). Applied in the enterprise, cognitive analytics can be used to bridge the important gap between large volumes of information and the need to make decisions in real time. A deep understanding of information helps companies draw from the wide variety of information sources in its knowledge base to improve the quality of enterprise knowledge, and provide competitive positioning as well as a deep and personalized approach to customer service. Cognitive analytics provides a data forward approach that starts and ends with what's contained in information. This unique way of approaching the entirety of information (all types and at any scale) reveals connections, patterns and collocations that enable unprecedented, even unexpected insight.).
Claims 3, 5-8, 13, 14, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20220197625, Franchitti to hereinafter in view of United States Patent Publication US 20210209483, Bose, et al. to hereinafter Bose in view of United States Patent Publication US 20180082192, Cormier, et al.
Referring to Claim 3, Franchitti teaches the computer-implemented method of claim 2, Franchitti in view of Bose does not explicitly teach wherein the obtaining further comprises normalizing the classified data by:
simplifying the classified data and generating a virtual data model representative of a simplified version of the heterogeneous data; normalizing the virtual data model using, at least in part, functional objective relation data.
However, Cormier teaches these limitations
wherein the obtaining further comprises normalizing the classified data by:
simplifying the classified data and generating a virtual data model representative of a simplified version of the heterogeneous data (
Cormier: Sec. 0052, heterogeneous Graph where Nodes can be Actors or Assets and the Edges define the frame as well as the strength of their connection as a function of risk.
Cormier: Sec. 0072, A graph is a stationary or time-oriented, connectionist architecture that discovers and connects the Actors to Actions, assets, and any other virtual or physical object or concept. Graphs can be Markov or homogeneous or heterogeneous or hyper-graphs. The Order of Operations Markov graphs, as an example, learns the behavior of an Actor for any pre-defined periodicity, e.g. a particular day of the week. );
normalizing the virtual data model using, at least in part, functional objective relation data (
Cormier: Sec. 0427, creating at least one cognitive model using the data dictionary entries for a time period, computing trust of the cognitive model as a fuzzy number, activating the cognitive model if trust of the cognitive model is above a cognitive model trust threshold, when the cognitive model is activated, scheduling a collection of tasks to run that perform regular extraction of actions from an original data source and performing at least one anomaly analysis associated with the cognitive model, for selected data dictionary entries, normalizing associated actor actions by converting at least one event to data dictionary format, inserting at least one normalized terrain entry into the cognitive model, and updating the cognitive model.).
Franchitti, Bose, and Cormier are all directed to the analysis of machine learning (See Franchitti at 0004, 0157, 0182; Bose at 0002, 0267, 0269; Cormier at 0063-0067). Franchitti discloses that additional elements, such as the autonomous application software units can be considered (See Franchitti at 0265). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Franchitti in view of Bose, which teaches detecting and repairing information technology problems in view of Cormier, to efficiently apply analysis of machine learning to enhancing the capability to diagnose data performance problems. (See Cormier at 0003, 0020, 0059).
Referring to Claim 5, Franchitti teaches the computer-implemented method of claim 4, further comprising automatically establishing a relationship between the incident, the predicted risk (
Franchitti: Sec. 0647, The HealthMeter™ solution uses machine learning and deep learning algorithms as needed to categorize the patients that exhibits symptoms in three (or more) risk-zones associated with the probability of developing the disease being monitored (e.g., green or safe if no risk, yellow or warning if low risk, red or danger for high risk).
Franchitti in view of Bose does not explicitly teach an underlying cause to generate a root cause analysis for one or more different risks and incidents.
However, Cormier teaches an underlying cause to generate a root cause analysis for one or more different risks and incidents (
Cormier: Sec. 0296, Cognitive Signals maintain and expose, in terms of results, the qualitative semantic (fuzzy logic and linguistic variable) nature of our machine intelligence functions (the actual algorithmic processing is not exposed). Signals support the necessary time sequence information for deep root cause analysis (this is how cognitive modeling does root cause discovery and the signals can be used for custom root cause analysis). Signals provide critical information for model performance analysis. Signals support (and for hazards and threats, contribute to) the analysis of information density (trustworthiness).).
Franchitti, Bose, and Cormier are all directed to the analysis of machine learning (See Franchitti at 0004, 0157, 0182; Bose at 0002, 0267, 0269; Cormier at 0063-0067). Franchitti discloses that additional elements, such as the autonomous application software units can be considered (See Franchitti at 0265). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Franchitti in view of Bose, which teaches detecting and repairing information technology problems in view of Cormier, to efficiently apply analysis of machine learning to enhancing the capability to diagnose data performance problems. (See Cormier at 0003, 0020, 0059).
Referring to Claim 6, Franchitti teaches the computer-implemented method of claim 5, further comprising optimizing the artificial intelligence model by, at least in part, feeding identification of the incident back to the training of the artificial intelligence model to optimize learning of the set of dynamic key performance indicators (
Franchitti: Sec. 0221, equivalence (i.e., determine if classes in the model denote the same set of instances such as a change process and an incident process being both instances of the class “Process”),
Franchitti: Sec. 0702, There is a need to constantly analyze this information using AI algorithms to detect or predict incidents.
Franchitti: Sec. 0703, The Archemy™ platform binds the need to the information by powering the iTracker™ active business solution that provides a risk analysis and management platform for the insurance industry.).
Referring to Claim 7, Franchitti teaches the computer-implemented method of claim 5, further comprising optimizing the artificial intelligence model by, at least in part, feeding an output of the root cause analysis (See Cormier) back to the training of the artificial intelligence model to optimize learning of the set of dynamic key performance indicators (
Franchitti: Sec. 0221, equivalence (i.e., determine if classes in the model denote the same set of instances such as a change process and an incident process being both instances of the class “Process”),
Franchitti: Sec. 0702, There is a need to constantly analyze this information using AI algorithms to detect or predict incidents.
Franchitti: Sec. 0703, The Archemy™ platform binds the need to the information by powering the iTracker™ active business solution that provides a risk analysis and management platform for the insurance industry.).
Franchitti in view of Bose does not explicitly teach feeding an output of the root cause analysis.
However, Cormier teaches feeding an output of the root cause analysis (
Cormier: Sec. 0296, Cognitive Signals maintain and expose, in terms of results, the qualitative semantic (fuzzy logic and linguistic variable) nature of our machine intelligence functions (the actual algorithmic processing is not exposed). Signals support the necessary time sequence information for deep root cause analysis (this is how cognitive modeling does root cause discovery and the signals can be used for custom root cause analysis). )
Franchitti, Bose, and Cormier are all directed to the analysis of machine learning (See Franchitti at 0004, 0157, 0182; Bose at 0002, 0267, 0269; Cormier at 0063-0067). Franchitti discloses that additional elements, such as the autonomous application software units can be considered (See Franchitti at 0265). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Franchitti in view of Bose, which teaches detecting and repairing information technology problems in view of Cormier, to efficiently apply analysis of machine learning to enhancing the capability to diagnose data performance problems. (See Cormier at 0003, 0020, 0059).
Referring to Claim 8, Franchitti teaches the computer-implemented method of claim 7, the learned set of dynamic key performance indicators to autonomously generate the one or more actions(
Franchitti: Sec. 0132, Each of the foregoing processes can be performed by the system automatically/autonomously (i.e., without further user input).
Franchitti: Sec. 0151, Alternatively or in addition, in some embodiments, one or more compute nodes of the network can actively, dynamically and/or autonomously implement changes to the taxonomy and/or to the Enterprise Catalog, in real-time, in response to one or more of: machine-observed system performance, alerts detected, user-observed system performance, and/or qualitative user feedback (i.e., indicators of user “sentiment”). As used herein, “active” (or “actively”) refers to the implementation of adaptations in an intelligent (e.g., using AI/ML), autonomous (e.g., using AI-driven autonomous systems capabilities and/or robotics)
Franchitti: Sec. 0559, Prescriptive analytics are used across the board within the DKMF to gain insight into how the architecture, ontologies and related taxonomies, and business solutions may behave in the near term and take actions to adjust them accordingly. Intelligent active and/or autonomous business solutions architectures (including the DKMF framework) rely on an EAF meta-framework and are designed to align with certain key performance indicators (KPIs) in mind, which drive the choice and use of certain recommended patterns.
Franchitti: Sec. 0560, Cognitive analytics are used in a similar way across the board within the DKMF to identify ontology and related taxonomy gaps between user needs and business solutions architectures, and take actions to adjust them accordingly. As noted earlier, intelligent active and/or autonomous business solutions architectures (including the DKMF framework) rely on an EAF meta-framework and are designed to align with certain key performance indicators (KPIs), which drive the choice and use of certain recommended patterns. ).
Franchitti in view of Bose does not explicitly teach wherein generating the one or more actions comprises using the output of the root cause analysis.
However, Cormier teaches wherein generating the one or more actions comprises using the output of the root cause analysis (
Cormier: Sec. 0296, Cognitive Signals maintain and expose, in terms of results, the qualitative semantic (fuzzy logic and linguistic variable) nature of our machine intelligence functions (the actual algorithmic processing is not exposed). Signals support the necessary time sequence information for deep root cause analysis (this is how cognitive modeling does root cause discovery and the signals can be used for custom root cause analysis).
Each of the foregoing processes can be performed by the system automatically/autonomously (i.e., without further user input). )
Franchitti, Bose, and Cormier are all directed to the analysis of machine learning (See Franchitti at 0004, 0157, 0182; Bose at 0002, 0267, 0269; Cormier at 0063-0067). Franchitti discloses that additional elements, such as the autonomous application software units can be considered (See Franchitti at 0265). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Franchitti in view of Bose, which teaches detecting and repairing information technology problems in view of Cormier, to efficiently apply analysis of machine learning to enhancing the capability to diagnose data performance problems. (See Cormier at 0003, 0020, 0059).
Claims 13, 14, 19, 20 recite limitations that stand rejected via the art citations and rationale applied to claims 5 and 8.
Response to Arguments
Applicant’s arguments filed 05/13/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 05/13/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 8-13 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
a computer-implemented method of autonomous environmental sustainability control related to a computing environment performing a functional objective, the computer-implemented method comprising:
identifying, using the learned set of dynamic key performance indicators, an anomaly;
obtaining normalized data from heterogeneous data obtained from a plurality of data sources, the normalized data being obtained using swarm intelligence-based data normalization processing, the heterogeneous data relating, at least in part, to the functional objective and to at least one environmental sustainability metric of the computing environment;
training, using the normalized data, an artificial intelligence model to learn dynamic key performance indicators relating, at least in part, computing environment performance of the functional objective to environmental sustainability;
using the artificial intelligence model to learn a set of dynamic key performance indicators to relate current computing environment performance of the functional objective to environmental sustainability;
generating one or more actions to remediate a risk associated with the anomaly, the one or more actions facilitating the autonomous environmental sustainability control related to the computing environment performing the functional objective.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing of data, which constitutes methods related to commercial interactions such as business relations which are still considered an abstract idea under the 2019 PEG. The swarm intelligence is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 10 and 11 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps and arguments at pg. 12-13:
“Further, as recited, the heterogeneous data relates, at least in part, "...to the functional objective and to at least one environmental sustainability metric of the computing environment." Further, amended claim 1 recites, in part "training, using the normalized data, an artificial intelligence model to learn dynamic key performance indicators relating, at least in part, computing environment performance of the functional objective to environmental sustainability" and "using the artificial intelligence model to learn a set of dynamic key performance indicators to relate current computing environment performance of the functional objective to environmental sustainability".”
seems to describe a “particular way” of managing of data are part of the abstract idea. “ The Applicant is basically relying on the system elements as integrating the abstract idea into a practical application but those system elements aren't really utilized in any particular manner. The Examiner wants to point out that claims do not show or break down how the steps and arguments at pg. 12-13 are actually executed, at this point it’s just an aspirational statement.
Additionally, the Examiner did consider “the Kim Memo” and all examination falls in line with the memo. The Examiner would like to point the Applicant to the 2019 PEG, in which managing of data will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Regarding the 35 U.S.C. 101 rejection, at pg. 8-13 Applicant argues “For instance, Applicant's amended claim 1 recites a computer- implemented method of "autonomous environmental sustainability control related to a computing environment performing a functional objective". No similar teaching is suggested or provided by Franchitti. For instance, a careful reading of Franchitti fails to uncover any discussion of "environmental sustainability" per se, let alone to a method of "autonomous environmental sustainability control related to a computing environment performing a functional objective," as Applicant recites…”
In response the Examiner respectfully disagrees. Franchitti teaches the analyzing of cloud and the environments, which includes evaluation of functional performance objectives and autonomous sustained controls in a computing environment, which is similar to Applicant’s spec at 0003 and 0151.
At pg. 15, Applicant argues “Applicant respectfully submits that a careful reading of Franchitti fails to uncover any teaching or suggestion of, for instance, "the normalized data being obtained using swarm intelligence- based data normalization processing", as Applicant recites in amended claim 1.”
In response the Examiner respectfully disagrees. Bose teaches using swarm intelligence-based data within artificial intelligence and blockchain, Additionally, Bose teaches the classifying data, which is similar to Applicant’s spec at 0006 for disclosing normalizing data. Lastly, the Examiner Franchitti used to teach normalizing data.
At pg. 15, Applicant argues “amended claim 1 recites "training, using the normalized data, an artificial intelligence model to learn dynamic key performance indicators relating, at least in part, computing environment performance of the functional objective to environmental sustainability" and "using the artificial intelligence model to learn a set of dynamic key performance indicators to relate current computing environment performance of the functional objective to environmental sustainability." As recited, the method also includes "identifying, using the learned set of dynamic key performance indicators, an anomaly" and "generating one or more actions to remediate a risk associated with the anomaly, the one or more actions facilitating the autonomous environmental sustainability control related to the computing environment performing the functional objective." Applicant respectfully submits that no similar teaching or suggestion is provided in Franchetti.”
In response the Examiner respectfully disagrees. Regarding the training limitation, Franchitti teaches the analyzing of cloud and the environments, which includes the training and normalizing of data by classifying data within machine learning that learns KPI for evaluating of functional performance objectives and autonomous sustained controls in a computing environment, which is similar to Applicant’s spec at 0003 and 0151.
Regarding the using limitation, Franchitti teaches the analyzing of cloud and the environments, which includes artificial intelligence and the use of machine learning that learns KPI for evaluating of functional performance objectives and autonomous sustained controls in a computing environment, which is similar to Applicant’s spec at 0003 and 0151.
Regarding the identifying limitation, Franchitti teaches the analyzing of cloud and the environments, which includes artificial intelligence and the use of machine learning that learns KPI for evaluating of functional performance in a computing environment, and consider anomalies.
Regarding the generating limitation, Franchitti teaches determining various risk and how to correct and leverage risk while considering anomalies and using autonomous sustained controls in a computing environment.
At pg. 15, 16, Applicant argues “Cormier fails to uncover any teaching or suggestion of swarm intelligence-based normalization as Applicant recites. Further, Applicant respectfully submits that a careful reading of Bose fails to uncover any teaching or suggestion of obtaining normalized data "using a swarm intelligence-based data normalization data processing" as Applicant recites. In addition, Cormier nor Bose cure Franchetti's deficiencies regarding autonomous environmental sustainability control of a computing environment as recited in amended claim 1.”
In response the Examiner respectfully disagrees. Bose teaches using swarm intelligence-based data within artificial intelligence and blockchain. Additionally, Bose teaches the classifying data, which is similar to Applicant’s spec at 0006 for disclosing normalizing data. Lastly, the Franchitti I used to teach normalizing data. Then, Franchitti teaches the analyzing of cloud and the environments, which includes evaluation of functional performance objectives and autonomous sustained controls in a computing environment, which is similar to Applicant’s spec at 0003 and 0151.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella et al., U.S. Pub. 20190146474, (discussing the operation and managing of a neural network).
Cella et al., W.O. Pub. 2022133330, (discussing the operation and managing of a neural network).
Panimalar et al., A Review Of Churn Prediction Models Using Different Machine Learning And Deep Learning Approaches In Cloud Environment, https://ph04.tci-thaijo.org/index.php/JCST/article/download/211/12, Journal of Current Science and Technology, 2023 (discussing the use of machine learning in different environments).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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