DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/6/2026 has been entered.
Claims 1, 10 and 17 have been amended. Claims 1-20 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to because figures 7 and 8 contain shaded gray and black areas, and lines that are not uniformly thick and well defined. See MPEP §608.02, 37 CFR 1.84 (I), and 37 CFR 1.84 (m).
Corrected drawing sheets in compliance with 37 CFR 1.121 (d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.1 21 (d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, method claims 1-9 are directed to a series of steps, computer program product claims 10-16 are directed to one or more computer readable storage media having program instructions, and system claims 17-20 are directed to a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media. Thus the claims are directed to a process, manufacture, and machine, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite measuring and increasing operational maturity, including obtaining, generating, ranking, and preparing steps.
The limitations of obtaining, generating, ranking, and preparing, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite obtaining a plurality of data types from an aggregation of data sources related to use of cloud computing resources in one or more cloud computing environments; generating at least one capability maturity score by evaluating a capability of at least one data source of the aggregation of data sources and generating a capability data score based on the evaluating; generating at least one provider maturity score by evaluating the at least one data source of the aggregation of data sources associated with a specific provider and generating a provider data score based on the evaluating; generating a set of recommendations based on a combination of the at least one capability maturity score and the at least one provider maturity score by iteratively simulating a plurality of modified capabilities and determining one or more steps to increase an operational maturity; ranking the set of recommendations based at least partially on a predicted change in the operational maturity and at least one weighted contribution that correlates to the predicted change, in response to implementing the set of recommendations; and preparing a report for a user, the report comprising a determined explanation of the predicted change in the operational maturity in response to implementing the set of recommendations.
That is, other than reciting a computing device a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, and a support vector machine algorithm or a logistic regression algorithm, the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include a computing device a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, and a support vector machine algorithm or a logistic regression algorithm. The computing device a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, and a support vector machine algorithm or a logistic regression algorithm in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing device a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, and a support vector machine algorithm or a logistic regression algorithm amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claim 2 further describes the aggregation of data sources. Claims 3 and 4 recite and further describe an additional segmenting step. Claims 5-8 recite an additional determining step, and further describes the report for the user. Similarly, dependent claims 11-16 and 18-20 recite additional details that further restrict/define the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a computing device a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, and a support vector machine algorithm or a logistic regression algorithm.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraph 0052 of the specification, “In cloud computing node 10 there is a computer system/server 12, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand- held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jeffery et al (US 20210329033 A1), in view of Arora et al (US 20200090088 A1), in further view of Amit et al (US 10810106 B1).
As per claim 1, Jeffery et al disclose a computer-implemented method, comprising:
obtaining, by a computing device, a plurality of data types from an aggregation of data sources (i.e., By capturing data broadly and ingesting data from disparate systems, the host system can provide a more accurate and inclusive maturity value, ¶ 0036, wherein The resulting data may be accumulated or otherwise aggregated, ¶ 0037);
generating, by the computing device using a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm (i.e., information from the organization may automatically be uploaded or fed into the machine learning model 300A, wherein an Embeddings from Language Model (ELMo) is a deep contextualized word representation that models both complex characteristics of word use (e.g., systems and semantics) and how these uses vary across linguistic contexts (i.e., to model polysemy). The word vectors are learned functions of internal states of a deep bi-directional language model (biLM) which is pre-trained on a large text corpus, ¶ 0038), at least one capability maturity score by evaluating a capability of at least one data source of the aggregation of data sources (i.e., By capturing data broadly and ingesting data from disparate systems, the host system can provide a more accurate and inclusive maturity value, ¶ 0036, wherein outputs from the machine learning model 300A may be scaled to generate scores that are based on maturity levels shown in FIG. 3B, ¶ 0040) and generating a capability data score based on the evaluating (i.e., the model may establish two scores for each of capability and maturity by relating them to one another which provide precise numeration for capability and maturity by creating a range and placing accordingly. For example, maturity on a scale of 1-10 may be determined based on the score of a component and that item may receive placement accordingly. A similar determination and placement may occur with capability, ¶ 0051);
generating, by the computing device using the DL algorithm or the EGBoost algorithm (¶ 0038), at least one provider maturity score by evaluating the at least one data source of the aggregation of data sources associated with a specific provider (i.e., the cognitive system 122 may detect the output maturity values for the entity on a plurality of topics from the machine learning model (e.g., the algorithm ensemble processed in steps 143 and 144), scale the maturity values into user friendly scores, and output the scaled maturity values, ¶ 0034, wherein outputs from the machine learning model 300A may be scaled to generate scores that are based on maturity levels shown in FIG. 3B, ¶ 0040) and generating a provider data score based on the evaluating (i.e., updating the data from the plurality of sources with new data, and determining, via the one or more machine learning models, updates to the maturity values of the entity, ¶ 0057);
generating, by the computing device using a machine learning algorithm, a set of recommendations based on a combination of the at least one capability maturity score and the at least one provider maturity score by iteratively simulating a plurality of modified capabilities and determining one or more steps to increase an operational maturity (i.e., the cognitive system may use an ensemble of machine learning algorithms, wherein the cognitive system may determine recommendations for improving the maturity of an organizations cyber security system based on best practices within the industry, and output the determined recommendations with the maturity scores, ¶ 0024);
ranking, by the computing device, the set of recommendations based at least partially on a predicted change in the operational maturity, in response to implementing the set of recommendations (i.e., Each category includes its current score, a projected score for one year into the future, and a projected store for three years into the future. The projected scores may be predicted by the machine learning model based on the suggested recommendations by the system being complied with by the organization, ¶ 0052); and
preparing, by the computing device, a report for a user, the report comprising a determined explanation of the predicted change in the operational maturity in response to implementing the set of recommendations (i.e., FIG. 4A illustrates a user interface 400A displaying maturity values 410 and recommendations 420, ¶ 0045).
Jeffery et al does not disclose obtaining, by a computing device, a plurality of data types from an aggregation of data sources related to use of cloud computing resources in one or more cloud computing environments, and ranking, by the computing device, the set of recommendations based at least one weighted contribution that correlates to the predicted change.
Arora et al disclose management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources (¶ 0106).
Additionally, Arora et al disclose the transformation matrix is implemented as mapping table of measures from ITOA (e.g. Incident resolution rate) to a scale of maturity assessment scores used to represent the enterprise architecture (EA) assessment maturity state. In one embodiment, the scale may range from 1 to 5, in which a value of 5 at the maximum value of the scale represents best practice, i.e., high maturity, and a value of 1 at the minimum of the sale represents poor maturity. The deviation between newly calculated maturity assessment score and previous EA assessment maturity state is transformed to a scale between 0% and 100% of operational risk. For the risk evaluation weighting parameters and a transformation function/graph is used to provide a prescriptive method of operational risk evaluation (¶ 0066).
Additionally, Jeffery et al does not disclose using a support vector machine algorithm or a logistic regression algorithm.
Amit et al disclose the machine learning engine 328 may use one or more machine learning algorithms to perform and/or various functions of the security and maturity service such as determining correlations between data points, security risks, and security scores. The one or more machine learning algorithms may include a decision tree algorithm, a probabilistic classifier algorithm, a least-squares regression algorithm, a support vector machine, or various supervised and unsupervised learning algorithms (column 9, lines 13-21).
Jeffery et al, Arora et al and Amit et al are concerned with effective data maturity management and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include obtaining, by a computing device, a plurality of data types from an aggregation of data sources related to use of cloud computing resources in one or more cloud computing environments, and ranking, by the computing device, the set of recommendations based at least one weighted contribution that correlates to the predicted change; and using a support vector machine algorithm or a logistic regression algorithm in Jeffery et al, as seen in Arora et al and Amit et al, respectively, 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.
As per claim 2, Jeffery et al does not disclose the aggregation of data sources comprises one or more cloud computing environments.
Arora et al disclose management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources (¶ 0106).
Jeffery et al and Arora et al are concerned with effective data maturity management and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the aggregation of data sources comprises one or more cloud computing environments in Jeffery et al, as seen in Arora et al, 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.
As per claim 3, Jeffery et al disclose segmenting, by the computing device, the plurality of data types (i.e., The data sources 110 may also include audio, video, images and the like. There is no limit to the data sources 110 or the type of information they can provide. The cognitive system 122 may analyze the data from the data sources 110 and cluster the data into smaller sets (subsets) based on topic, field, area, etc. which is simply referred to as topics, ¶ 0028).
As per claim 4, Jeffery et al disclose segmenting the plurality of data types according to one or more of industry, age, and volume (i.e., recommendations 420 may be triggered by rules that are based on the best practices or guidelines within an industry that is associated with and includes the entity, ¶ 0048).
As per claim 5, Jeffery et al disclose determining, by the computing device, at least one weighted contribution that correlates to the predicted change in the operational maturity in response to implementing the set of recommendations (i.e., the keywords may be weighted, where some keywords have more weight on the overall maturity value determination, ¶ 0033).
As per claim 6, Jeffery et al does not disclose the at least one weighted contribution is determined based on a distribution weight that comprises distributions of features sampled across organizations at each stage of operational maturity.
Arora et al disclose the transformation matrix is implemented as mapping table of measures from ITOA (e.g. Incident resolution rate) to a scale of maturity assessment scores used to represent the enterprise architecture (EA) assessment maturity state. In one embodiment, the scale may range from 1 to 5, in which a value of 5 at the maximum value of the scale represents best practice, i.e., high maturity, and a value of 1 at the minimum of the sale represents poor maturity. The deviation between newly calculated maturity assessment score and previous EA assessment maturity state is transformed to a scale between 0% and 100% of operational risk. For the risk evaluation weighting parameters and a transformation function/graph is used to provide a prescriptive method of operational risk evaluation (¶ 0066).
Jeffery et al and Arora et al are concerned with effective data maturity management and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the at least one weighted contribution is determined based on a distribution weight that comprises distributions of features sampled across organizations at each stage of operational maturity in Jeffery et al, as seen in Arora et al, 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.
As per claim 7, Jeffery et al disclose the report for the user further comprises an explanation of how the at least one weighted contribution was assigned and how the at least one weighted contribution affects the operational maturity (i.e., the keywords may be weighted, where some keywords have more weight on the overall maturity value determination, ¶ 0033).
As per claim 8, Jeffery et al disclose the at least one weighted contribution comprises a change in one or more features of the plurality of data types (i.e., the keywords may be weighted, where some keywords have more weight on the overall maturity value determination. In 144, the cognitive system 122 may perform a sentiment analysis using a second algorithm within the machine learning model, ¶ 0033).
As per claim 9, Jeffery et al disclose the plurality of data types comprises account data, cost data, and usage data (i.e., the data sources 110 may include text data (e.g., unstructured text data) from various company-based sources such as emails, messages, training manuals, user conversations with a chatbot, user inputs via a user interface, log files logging network data, log files of computing systems, employee skill descriptions, process information, application patch management data, and the like. The data sources 110 may also include audio, video, images and the like. There is no limit to the data sources 110 or the type of information they can provide, ¶ 0028).
Claims 10-16 are rejected based upon the same rationale as the rejection of claims 1-8, respectively, since they are the computer program product claims corresponding to the method claims.
Claims 17-20 are rejected based upon the same rationale as the rejection of claims 1, 5, 6 and 8, respectively, since they are the system claims corresponding to the method claims.
Response to Arguments
In the Remarks, Applicant argues independent claim 1 includes claim language, which differentiates the claims from certain methods of organizing human activity. Independent claims 10 and 17 include similar subject matter as claim 1, and should be patent-eligible for similar reasons.
Specifically, claim 1 recites the steps of: obtaining, by a computing device, a plurality of data types from an aggregation of data sources related to use of cloud computing resources in one or more cloud computing environments; generating, by the computing device using a support vector machine algorithm or a logistic regression algorithm, a set of recommendations based on a combination of the at least one capability maturity score and the at least one provider maturity score by iteratively simulating a plurality of modified capabilities and determining one or more steps to increase an operational maturity; ranking, by the computing device, the set of recommendations based at least partially on a predicted change in the operational maturity and at least one weighted contribution that correlates to the predicted change, in response to implementing the set of recommendations; and preparing, by the computing device, a report for a user, the report comprising a determined explanation of the predicted change in the operational maturity in response to implementing the set of recommendations.
These steps, individually and in combination, differentiate the claims from being part of any method of organizing human activity. According to MPEP §2106.04(a)(2)(II), the phrase "methods of organizing human activity" is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances.
Applicant submits that the Examiner's interpretation of the claim limitations fails to consider the context and nature of the claimed subject matter-operational management of cloud computing processes and resources, through measurement of operational maturity metrics-that is inherently related to a technological environment. MPEP §2106.04(a)(2)(II)B provides an example of business relations as a form of commercial interactions-"processing information through a clearing-house, where the business relation is the relationship between a party submitted a credit application (e.g., a car dealer) and funding sources (e.g., banks) when processing credit applications". The recited claim limitations, in the present case, are clearly distinguishable over such commercial interactions, because they do not establish any business or commercial relationship. Instead, the claim limitations recite how operational maturity metrics are measured based on data from cloud providers and a set of recommendations are generated based on such metrics, and subsequently ranked.
According to MPEP §2106.05(a) and the August 2025 USPTO Memo, in computer- related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. Id. In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database, and held that the claim "was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts." Id. at 1691. In McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), the Federal Circuit relied on the specification's explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. Id. at 1313-14.
In the present application, independent claim 1 is directed towards an improvement in measuring maturity metrics in the technical field of cloud computing operational management within computer technology, and more specifically to provision of a technical solution "measuring operational maturity, creating Key Performance Indicators (KPIs), and providing recommendations to improve operational maturity" of cloud computing systems. See paragraph [0016] of the Specification. In particular, the claims improve the technical field of cloud computing operational management by providing a method and a system that can (i) generate, using a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, at least one capability maturity score by evaluating a capability of at least one data source of the aggregation of data sources and generating a capability data score based on the evaluating, and (ii) generate, using the DL algorithm or the EGBoost algorithm, at least one provider maturity score by evaluating the at least one data source of the aggregation of data sources associated with a specific provider and generating a provider data score based on the evaluating, in order to generate a set of recommendations. Applicant's specification provides a technical explanation as to how to implement the invention with sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in measuring maturity metrics in the technical field of cloud computing operational management.
The subject matter of claim 1 recites a method to "improve an organization's ability to measure and improve operational maturity (i.e., save on costs and better utilize the available cloud computing resources)". See paragraph [0021] of the Specification. Accordingly, this method overcomes limitations of conventional techniques of measuring maturity metrics in cloud computing operation management through the technical solution provided. See paragraph [0019]-[0020] of the Specification. The technical solution is recited in the features of claim 1, which integrate any alleged exception, therefore, into a practical application by providing an improvement to the technical field of cloud computing operations management that is necessarily rooted in computer technology. The Examiner respectfully disagrees.
As described in paragraph 0002 of Applicant’s specification, “Operational management is a public cloud management discipline that enables organizations to get maximum value from the cloud by helping technology, finance, and business teams to collaborate on data-driven spending decisions. Many businesses use operational management to try to reduce overall cloud computing costs while still achieving business goals.” Additionally, paragraph 0016 of Applicant’s specification recites that “According to aspects of the invention the system may be generally configured to measure operational maturity, make recommendations for improving/increasing an organization's operational maturity, and explain the recommendations and/or a score associated with the operational maturity. As an example, the operational maturity comprises financial operational maturity.” Moreover, paragraph 0019 of Applicant’s specification recites that “Over time, more and more companies and organizations rely on cloud computing to carry out their business purposes.”
Contrary to Applicant’s assertion that “these paragraphs generally detail the benefits of the invention, rather than technical explanations of the invention,” the paragraphs actually describe the invention concept
Similarly, the independent claims recite, inter alia, “generating, by the computing device using a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, at least one capability maturity score by evaluating a capability of at least one data source of the aggregation of data sources; generating, by the computing device using a machine learning algorithm support vector machine algorithm or a logistic regression algorithm, a set of recommendations based on a combination of the at least one capability maturity score and the at least one provider maturity score by iteratively simulating a plurality of modified capabilities and determining one or more steps to increase an operational maturity; ranking, by the computing device, the set of recommendations based at least partially on a predicted change in the operational maturity and at least one weighted contribution that correlates to the predicted change, in response to implementing the set of recommendations; and preparing, by the computing device, a report for a user, the report comprising a determined explanation of the predicted change in the operational maturity in response to implementing the set of recommendations”
As a result, and contrary to Applicant’s assertion, other than reciting a computing device and a support vector machine algorithm or a logistic regression algorithm, the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, and importantly the claimed use of a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, and a support vector machine algorithm or a logistic regression algorithm does not seem to involve anything other than the application of a known technique in its normal, routine, and ordinary capacity.
Following, under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55. Besides the abstract idea, the claims include a computing device, a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm and a support vector machine algorithm or a logistic regression algorithm.
The computing device, a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm and a support vector machine algorithm or a logistic regression algorithm in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Even when viewed in combination, the additional elements in the claims do no more than use computer components as a tool (i.e., a computing device, a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm and a support vector machine algorithm or a logistic regression algorithm). There is no change to the computers and/or other technology recited in the claims, thus the claims do not improve computer functionality or other technology. See, e.g., Trading Technologies Int’l v. IBG, Inc., 921 F.3d 1084, 1093 (Fed. Cir. 2019) (using a computer to provide a trader with more information to facilitate market trades improved the business process of market trading, but not the computer) and the cases discussed in MPEP 2106.05(a)(I), particularly FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095 (Fed. Cir. 2016) (accelerating a process of analyzing audit log data is not an improvement when the increased speed comes solely from the capabilities of a general-purpose computer) and Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055 (Fed. Cir. 2017) (using a generic computer to automate a process of applying to finance a purchase is not an improvement to the computer’s functionality). Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
The Federal Circuit in Enfish stated that certain claims directed to improvements in computer related technology, including claims directed to software, are not necessarily abstract (Step 2A). The court specifically noted that some improvements in computer-related technology, such as chip architecture or an LED display, when appropriately claimed, are undoubtedly not abstract. Explaining that software can make non-abstract improvements to computer technology just as hardware can, the court noted that claims directed to software, as opposed to hardware, also are not inherently abstract. In particular, a claim directed to an improvement to computer-related technology (e.g., computer functionality) is likely not similar to claims that have previously been identified as abstract by the courts.
Here, contrary to the claims as seen in Enfish, the claims provide no improvements in computer-related technology, such as chip architecture or an LED display.
Specifically, the claims of the patents at issue in Enfish describe the steps of configuring a computer memory in accordance with a self-referential table, in both method claims and system claims that invoke 35 U.S.C. § 112(t). The court asked whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database), or instead on a process that qualifies as an "abstract idea" for which computers are invoked merely as a tool. To make the determination of whether these claims are directed to an improvement in existing computer technology, the court looked to the teachings of the specification.
Specifically, the court identified the specification's teachings that the claimed invention achieves other benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. It was noted that the improvement does not need to be defined by reference to "physical" components. Instead, the improvement here is defined by logical structures and processes, rather than particular physical features.
Contrarily, here, there are no specification “teachings that the claimed invention achieves other benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements,” nor is there any improvement here defined by logical structures and processes. The Federal Circuit stated that the Enfish claims were not ones in which general-purpose computer components are added after the fact to a fundamental economic practice or mathematical equation, but were directed to a specific implementation of a solution to a problem in the software arts, and concluded that the Enfish claims were thus not directed to an abstract idea (under Step 2A).
However, in this case, general-purpose computer components (i.e., a computing device as discussed in paragraph 0052 of the specification) added after the fact to an abstract idea, do not amount to significantly more than the recited judicial exception.
Moreover, and contrary to Applicant's assertion, the claims here are not similar to the claims in McRO, which were determined to be an improvement to computer technology. In McRO, the Federal Circuit held the claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules patent eligible under 35 U.S.C. § 101, because they were not directed to an abstract idea (Step 2A of the USPTO's SME guidance). The basis for the McRO court's decision was that the claims were directed to an improvement in computer-related technology (allowing computers to produce "accurate and realistic lip synchronization and facial expressions in animated characters" that previously could only be produced by human animators), and thus did not recite a concept similar to previously identified abstract ideas.
The McRO court thus relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated when determining that the claims were directed to improvements in computer animation instead of an abstract idea. The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.
Here, there is no improvement to any computer technology per se here, nor is there a set of "rules" (basically mathematical relationships) that improve computer-related technology by allowing computer performance of a function not previously performable by a computer. Further, the specification does not describe a teaching about how the claimed invention improves a computer or other technology Rather, the abstract idea of measuring and increasing operational maturity is merely implemented on a computing device. Following, here, the focus of the claims are not an improvement in computers as tools, but implementing the abstract idea via a computing device.
Applicant also argues that that Jeffery, Arora, and Amit, alone or in combination, do not teach show or suggest the features of "generating, by the computing device using a deep learning (DL) algorithm or an extreme gradient boosting (EGBoost) algorithm, at least one capability maturity score by evaluating a capability of at least one data source of the aggregation of data sources and generating a capability data score based on the evaluating" and "generating, by the computing device using the DL algorithm or the EGBoost algorithm, at least one provider maturity score by evaluating the at least one data source of the aggregation of data sources associated with a specific provider and generating a provider data score based on the evaluating" (emphasis added) that are recited in amended independent claim 1.
Finally, Applicant further submits that Jeffery cannot be combined with Arora and Amit to arrive at the features of the present invention because they pertain to different fields of endeavor. The present invention is related to operational management of cloud computing resources, and has no relevance to cybersecurity. On the other hand, the primary reference Jeffery is related to maturity determination of an organization's cybersecurity capability, and therefore, a person having ordinary skill in the art would not look to Jeffery to solve the problem that the present invention solves. Arora relates to determining maturity levels in enterprise architecture frameworks and no relevance to cybersecurity (see Arora at paragraph [0001]), either. Amit related to a security and maturity service that generates security scores and recommended security actions for a computer application. The Examiner respectfully disagrees.
As discussed in the updated rejection, Jeffery et al indeed disclose Applicant’s amended claim language. Additionally, and contrary to Applicant’s assertion, Jeffery et al, Arora et al and Amit et al are concerned with effective data maturity management and analysis. Arora et al disclose community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises (¶ 0098), thus combinable with Jeffery et al and Amit et al.
Similarly, paragraph 0047 of Applicant’s specification recites “Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.” Additionally, paragraph 0064 recites “Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met.”
As a result, and contrary to Applicant’s assertion, Jeffery is indeed combinable with Arora and Amit to arrive at the features of the present invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 April 18, 2026
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