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 .
DETAILED ACTION
1. This Final Office action is in reply to the Applicant amendment filed on 23 December 2025.
2. Claims 1, 9, 20 have been amended.
3. Claims 1-20 are currently pending and have been examined.
Response to Amendment
In the previous office action, Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (abstract idea). Applicants have not amended Claims 1-20 to provide statutory support and the rejection is maintained.
Applicant’s amendments necessitated the below new grounds of rejection.
Response to Arguments
Applicant’s arguments filed 23 December 2025 have been fully considered but they are not persuasive. In the remarks regarding the 35 USC § 101 rejection for Claims 1-20, Applicant argues that: (1) the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Still commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, and the Examiner details the maintained rejection under 35 U.S.C. 101 in the below rejection with further explanation. Applicant basically argues that as amended, Applicant states (for representative Claim 1): “…claim 1 is not directed to a judicial exception” through the abstract idea analysis (see Remarks/Arguments pages 11-24). However the Examiner respectfully disagrees. The claims are "directed to" the abstract idea of automated business intelligence and data optimization. The steps described are not "directed to" an improvement in the functioning of a computer itself. Here the Examiner provides additional clarification through the Alice Step process:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations. The use of artificial intelligence to analyze data, find "drivers," and optimize business metrics (BHI) is deemed an abstract concept if it simply applies conventional machine learning to data without providing a specific technological improvement to the AI model itself (i.e., Creating equations of drivers/metrics with static/dynamic weights; tracking and optimizing the BHI of the business in real-time by applying an optimization algorithm to the developed mathematical model; performing root cause analysis to identify underlying causes that influence the business; wherein the computer-implemented method uses an artificial intelligence module to organize the acquired data, standardize the acquired data, analyze the organized data, develop a mathematical model, optimize and protect the BHI of the business).
Certain methods of organizing human activity – fundamental economic principles or practices; marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Mapping to a unified data model is considered data gathering and organization, which is a classic "abstract idea" when done by computers. (tracking and optimizing the BHI of the business in real-time by applying an optimization algorithm to the developed mathematical model; performing root cause analysis to identify underlying causes that influence the business; organizing the acquired historical and real-time data in a data center; standardizing the acquired historical and real-time data in the data center; analyzing the organized and standardized historical and real-time data in the data center); and
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Detecting cause-and-effect and mapping it in a causal graph is analogous to human mental processes of analysis, or simply mathematical modeling, i.e., organizing the acquired historical and real-time data in a data center; standardizing the acquired historical and real-time data in the data center; analyzing the organized and standardized historical and real-time data in the data center). See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. In summary as indicated below with further detail through Steps 1-2B, the recitation of a computer (one or more processors) to perform the claim limitations amount to no more than mere instruction to apply the exception using generic computer components. 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. For at least these reasons, the rejection is maintained.
Applicants’ amendments have presented a new ground of rejection and the arguments for the prior art rejection are moot. It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969).
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 a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1-20 are each focused to a statutory category of invention, namely “method; system; computer program product embodied on a non-transitory computer readable medium” sets.
Step 2A: Prong One: Claims 1-20 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention is directed to an abstract idea without significantly more. The claims are "directed to" the abstract idea of automated business intelligence and data optimization. The steps described are not "directed to" an improvement in the functioning of a computer itself, but rather a possible improvement in a business process using a computer. The claims recite steps for:
“acquiring, by an adaptor module, historical data and real-time data from a plurality of sources;
organizing, by an artificial intelligence module, the acquired historical data and the acquired real-time data in a data center using a unified data model that maps the acquired historical data and the acquired real-time data to a common language;
standardizing, by the artificial intelligence module, the acquired historical data and the acquired real-time data in the data center by filtering dirty, redundant or unwanted data and aligning codification and formats according to the unified data model;
validating, via an edge computing client agent at a data-entry source, the acquired historical data and the acquired real-time data against the unified data model and identifying, recommending rectifications for, and performing actions to prevent data-entry errors at the source prior to persistence;
analyzing the organized and standardized historical data and the organized and standardized real-time data in the data center;
identifying drivers/metrics for the business by detecting cause-and-effect relationships and representing the relationships in a driver or metrics casual graph;
developing a mathematical model in real-time based on the analysis of the historical data and the real-time data and the identified drivers/metrics, wherein the mathematical model comprising an equation of direct and nested industry-specific drivers/metrics with static and/or dynamic driver/metric weights;
tracking and optimizing the BHI of the business in real-time by applying an optimization algorithm to the developed mathematical model;
performing root cause analysis to identify underlying causes that influence the business, and presenting notification to a user/business on an interface in form of signals, events, noises and actions, wherein the computer-implemented method uses an artificial intelligence module to organize the acquired data, standardize the acquired data, analyze the organized data, develop a mathematical model, optimize and protect the BHI of the business”
These abstract idea limitations identified above under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as: The 2019 PEG explains that the abstract idea exception includes the following groupings of subject matter:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations. The use of artificial intelligence to analyze data, find "drivers," and optimize business metrics (BHI) is deemed an abstract concept if it simply applies conventional machine learning to data without providing a specific technological improvement to the AI model itself (i.e., Creating equations of drivers/metrics with static/dynamic weights; tracking and optimizing the BHI of the business in real-time by applying an optimization algorithm to the developed mathematical model; performing root cause analysis to identify underlying causes that influence the business; wherein the computer-implemented method uses an artificial intelligence module to organize the acquired data, standardize the acquired data, analyze the organized data, develop a mathematical model, optimize and protect the BHI of the business).
Certain methods of organizing human activity – fundamental economic principles or practices; marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Mapping to a unified data model is considered data gathering and organization, which is a classic "abstract idea" when done by computers. (tracking and optimizing the BHI of the business in real-time by applying an optimization algorithm to the developed mathematical model; performing root cause analysis to identify underlying causes that influence the business; organizing the acquired historical and real-time data in a data center; standardizing the acquired historical and real-time data in the data center; analyzing the organized and standardized historical and real-time data in the data center); and
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Detecting cause-and-effect and mapping it in a causal graph is analogous to human mental processes of analysis, or simply mathematical modeling, i.e., organizing the acquired historical and real-time data in a data center; standardizing the acquired historical and real-time data in the data center; analyzing the organized and standardized historical and real-time data in the data center).
See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception.
Prong Two: Claims 1-20: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 12 recite additional elements directed to “data processing unit; various modules; computer program product; artificial intelligence module” (e.g., see Applicants’ published Specification ¶’s 56-60). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h).
Step 2B: As explained in MPEP § 2106.05, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “data processing unit; various modules; computer program product; artificial intelligence module”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ published Specification ¶’s 56-60) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “environment 1000” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Claims 1-8 do not recite any computer architecture components support within the body of at least independent Claim 1 for proper computer statutory of invention support and thus fail this step of the analysis. Accordingly, Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (Sharma) (US 2024/0283719) in view of Kolekar (US 2023/0422038).
With regard to Claims 1, 9, 20, Sharma teaches a computer-implemented method executed by one or more processors and non-transitory computer readable medium/system/computer program product embodied on a non-transitory computer readable medium for computing (computing), monitoring (Monitoring), optimizing (calculating a health score; improving; analyzed) and protecting (health, security, performance, availability, responsiveness) business health index (BHI) of a business in real-time (computer-implemented method; program product; system; for determining at least one health score for a business service, in accordance with one embodiment, includes a hardware processor and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor) (see at least paragraphs 3-5, 30, 120-150, 260), the computer-implemented method/system/non-transitory computer readable medium comprising: a data Processing Unit, an adaptor module, said adaptor module is configured to configure a plurality of plug-ins (see at least paragraphs 27, 42-45, 288-309);
acquiring, by an adaptor module, historical data (An entity (e.g. resource) network model may be created using historical data (e.g. 90 days (including multiple updates on each pertinence per day)); In operation 1206, the various metrics are analyzed, e.g., loop thorough each metric, and a metric value is determined for each metric based on the analysis. Each metric value is normalized, e.g., to a percentage. For example, based on the historical data, the maximum and minimum metric value is known. Using that, the percentage of the metric can be found) and real-time data from a plurality of sources (Time series data; the data is gathered in real time, for enabling an ongoing health assessment. In some approaches, customized adaptors may be used to pull the required time series data in raw format) (see at least paragraphs 3, 244, 287-289);
organizing, by an artificial intelligence module (Artificial Intelligence (AI) model), the acquired historical and acquired real-time data in a data center using a unified data model that maps the acquired historical data and the acquired real-time data to a common language (receiving a taxonomy specifying entities of a business service and levels of said entities. Time series data about the entities is collected and stored. Impactor propagation paths between entities are identified. A territory of a health impact of each entity is also identified. A health score for each of the entities, considering impacts on a health of the entity by at least one other entity, is computed based on the data, the propagation paths, and the territories of the entities. At least one of the health scores is output; the data is gathered in real time, for enabling an ongoing health assessment. In some approaches, customized adaptors may be used to pull the required time series data in raw format) (see at least paragraphs 3, 25, 67, 100, 244, 287-289);
standardizing, by the artificial intelligence module, the acquired historical data and the acquired real-time data in the data center (the business service pipeline architecture 400 includes modules to define structure, prepare information, process the information, analyze interconnections and the associated effects of entities on other entities and applications, and predict and report the health of the service. The business service pipeline architecture 400 is generic in that it may be used for business services running in full or in part on a public cloud, a private cloud, a hybrid cloud, and/or on premises) (see at least paragraphs 3, 25, 67, 100, 244, 287-289);
the acquired historical data and the acquired real-time data against the unified data model and identifying, recommending rectifications for, and performing actions to prevent data-entry errors at the source prior to persistence (business service health is accurately estimated, thereby enabling assessment of the possibility of an application or associated system going down, detection of other major problem in the system, etc. This in turn allows operators to mitigate risks, pull resources to problematic areas before errors become too severe, and ensure that the portions of the applications running on a particular computer are functioning properly thereby improving operation of the computer. A peripheral benefit is that the improved stability helps users to reduce costs by enabling pre-emption of problems) (see at least paragraph 50);
analyzing the organized and standardized historical data and the organized and standardized real-time data in the data center (the business service pipeline architecture 400 includes modules to define structure, prepare information, process the information, analyze interconnections and the associated effects of entities on other entities and applications, and predict and report the health of the service. The business service pipeline architecture 400 is generic in that it may be used for business services running in full or in part on a public cloud, a private cloud, a hybrid cloud, and/or on premises) (see at least paragraphs 3, 25, 67, 244, 287-289);
identifying drivers/metrics for the business (for defining the taxonomy, the client administrator defines the level-based details, e.g., application level, environment level, region level, etc. The entities of the business service are listed and associated with their respective levels. For each entity, attributes (metrics) are defined, and the impactors from different attributes are chosen. Exemplary attributes/metrics include CPU utilization, memory utilization, available storage, etc. The administrator preferably assigns weightage to each attribute/metric. The administrator also defines rules such as key performance indicators (KPIs) for scoring logic for each attribute chosen as an impactor. For example, scoring logic may include rules such as CPU utilization >90% (score 90)—Red, memory utilization <10% (score 10)—green. This score is for each entity and not the edges. The health interpretation for scoring may be an edge) by detecting cause-and-effect relationships (by improving the health of the overall business service, as well as its underlying entities, the functioning of the computer(s) running the entities, as well as hardware entities themselves, are improved. For example, if an entity is unhealthy because it is consuming excessive CPU cycles and/or memory of the computer it is running on, diagnosis and resolution of the health issue results in improvement of operation of the computer. Similarly, where an otherwise healthy entity is unable to run efficiently on a computer because an impactor entity is unhealthy and exerting an influence on the entity that diminishes the entity's health and/or efficiency, the foregoing methodologies are able to diagnose the unhealthy impactor entity, enabling repair of the unhealthy impactor entity and diminution of its unhealthy impact on the other entity) and representing the relationships in a driver or metrics casual graph (Using the taxonomy input by the administrator, source and target details are extracted from the level grouping data. Source, target, and impactors details are used to construct a network graph. See description of module 404 below. After setting the topology for each node and impactors, data can be collected. See description of submodules 406-410 below. A job may be run, or data streams accessed to collect the metrics data for each impactor defined in the topology. See description of submodules 406-410 below. Rules are applied to the impactors. The rules are applied to get the health of the entities in the topology and assign weightage to the entities) (see at least paragraphs 77-83, 309);
developing a mathematical model (regression model; Algorithm for Identifying Propagation Path) in real-time (real time; time series data) based on the analysis of the historical data and the real-time data and the identified drivers/metrics, wherein the mathematical model comprising an equation of direct and nested industry-specific drivers/metrics with static and/or dynamic driver/metric weights (metrics; weightage) (Using the algorithm below, all possible propagation paths of any health score from 0-100 can be identified; (Using the taxonomy input by the administrator, source and target details are extracted from the level grouping data. Source, target, and impactors details are used to construct a network graph. See description of module 404 below. After setting the topology for each node and impactors, data can be collected. See description of submodules 406-410 below. A job may be run, or data streams accessed to collect the metrics data for each impactor defined in the topology. See description of submodules 406-410 below. Rules are applied to the impactors. The rules are applied to get the health of the entities in the topology and assign weightage to the entities)) (see at least paragraphs 77-83, 87, 228-239);
tracking and optimizing the BHI (A data ingestion module 410 uses the adapters to pull the data defined by module 408 from the various entities in each level from the taxonomy for analytics. The data can be any relevant data that can be used in a health assessment, such as logs, performance metrics (such as CPU usage, memory usage, etc. metrics), platform/DevOps related metrics, error logs, warnings, etc. Preferably, the data is gathered in real time, for enabling an ongoing health assessment. In some approaches, customized adaptors may be used to pull the required time series data in raw format) of the business in real-time by applying an optimization algorithm (Algorithm for Calculating the Health Score; A health discovery analytics module 412 performs analytics and machine learning (ML) processing on the data gathered by the data ingestion module 410, e.g., on the raw data and/or equivalently on derivatives of such data) to the developed mathematical model (one or more of the submodules noted above may be deployed in a trained state of a trained Artificial Intelligence (AI) model. Training of the AI model, in some approaches, may be performed by applying a predetermined training data set to learn how to process the data from the data lake. Initial training may include reward feedback that may in some approaches be implemented using a subject matter expert (SME) that understands how the data from the data lake should be processed with respect to the training data. In another approach, the reward feedback may be implemented using techniques for training a BERT model) (see at least paragraphs 86-102, 120-150);
performing root cause analysis (the root entity health score) to identify underlying causes that influence the business (for deriving the health of a business service. For the first time, business service health is accurately estimated, thereby enabling assessment of the possibility of an application or associated system going down, detection of other major problem in the system, etc. This in turn allows operators to mitigate risks, pull resources to problematic areas before errors become too severe, and ensure that the portions of the applications running on a particular computer are functioning properly thereby improving operation of the computer. A peripheral benefit is that the improved stability helps users to reduce costs by enabling pre-emption of problems), and presenting notification (anomaly detector) to a user/business on an interface in form of signals, events, noises and actions, wherein the computer-implemented method uses an artificial intelligence module to organize the acquired data, standardize the acquired data, analyze the organized data, develop a mathematical model, optimize and protect the BHI of the business (The resultant health score(s), or information derived therefrom, are output. For example, the root entity health score may be output to a user interface module 416. Moreover, all health scores for a single application or multiple applications, e.g., from root to leaf entity may be output) (see at least paragraphs 50, 97-111);
Sharma does not specifically teach by filtering dirty, redundance, or unwanted data and aligning codification and formats according to the unified data model; validating, via an edge computing client agent at a data-entry source, the acquired historical data and the acquired real-time data against the unified data model and identifying, recommending rectifications for, and performing actions to prevent data-entry errors at the source prior to persistence. Kolekar teaches by filtering dirty, redundance, or unwanted data and aligning codification and formats according to the unified data model (a training data selection/filtering functional block 620. The training data selection/filter functional block 620 may be configured to generate training, validation, and testing datasets for model training. Training data may be extracted from the data repository 615. Data may be selected/filtered based on the specific AI/ML model to be trained. Data may optionally be transformed/augmented/pre-processed (e.g., normalized) before being loaded into datasets. The training data selection/filter functional block 620 may label data in datasets for supervised learning. The produced datasets may then be fed into model training the model training functional block 625); validating, via an edge computing client agent at a data-entry source (the 5GC 240 may enable edge computing by selecting operator/3rd party services to be geographically close to a point that the UE 202 is attached to the network. This may reduce latency and load on the network. To provide edge-computing implementations, the 5GC 240 may select a UPF 248 close to the UE 202 and execute traffic steering from the UPF 248 to data network 236 via the N6 interface. This may be based on the UE subscription data, UE location, and information provided by the AF 260. In this way, the AF 260 may influence UPF (re)selection and traffic routing. Based on operator deployment, when AF 260 is considered to be a trusted entity, the network operator may permit AF 260 to interact directly with relevant NFs. Additionally, the AF 260 may exhibit an Naf service-based interface) in analogous art of anomalous pattern occurrences for the purposes of; “reduce latency and load on the network” (Kolekar-see at least paragraphs 18, 60, 97-102).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the cyber attack detection function as taught by Kolekar in the system of Sharma, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With regard to Claims 2, 12, 13, Sharma teaches wherein the mathematical model comprises set of interfaces/an equation having plurality of direct and nested industry specific performance drivers/metrics with a static/dynamic driver/metric weight (see at least paragraphs 78, 82, 102, 249).
With regard to Claims 3, 14, Sharma teaches: forecasting each performance driver/metric involved in business by utilizing historical and real-time data and forecasting opportunities, risks, and threats of the business by analyzing past incidents to avoid repeat mistakes and present informed decisions to the business to optimize the BHI of the business (see at least paragraphs 67, 98, 110-115).
With regard to Claims 4, 15, Sharma teaches: automating future steps of the business based on past user actions for improving the performance drivers/metrics to optimize the BHI of the business (see at least paragraphs 50, 92, 111-117).
With regard to Claims 5, 16, Sharma teaches: integrating external knowledge comprising industry benchmarks and aggregated actions across various industries, business units, and geographical locations, integrating cross-organizational learning capabilities, detect patterns and devise accurate, actionable strategies based on collective organizational experiences to optimize the BHI of the business (see at least paragraphs 3, 59, 67, 82, 98).
With regard to Claims 6, 18, Sharma teaches: suggesting and assigning tasks based on identified business opportunities, risks, or threats, with an automated follow-up mechanism to ensure task completion and track individual performance for accountability and rewards (see at least paragraphs 50, 100, 309).
With regard to Claims 7, 10, Sharma teaches: identifying, recommending rectifications and performing actions to prevent data entry errors at a source, mistakes and inefficiencies to guide actions that may adversely affect business outcomes (see at least paragraphs 50, 87).
With regard to Claims 8, 19, Sharma teaches: facilitating internal and external industry benchmark comparisons (see at least paragraphs 82, 104, 109).
With regard to Claim 11, Sharma teaches comprising a suite of interfaces enabling users to define or edit causal relationships, either by starting from scratch or by using pre-designed templates, facilitated through a specialized graph editor, tailored to accommodate specific needs and scenarios of the business, thereby simplifying a process of customizing causal relationships to optimize the business health index (see at least paragraphs 45, 77-85, 309).
With regard to Claim 17, Sharma teaches wherein the system integrates cross-organizational learning capabilities, detect patterns and devise accurate, actionable strategies based on collective organizational experiences to optimize the BHI of the business (see at least paragraphs 46, 310).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Cella et al. (US 2023/0281527)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624