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
This is a non-final rejection. Claims 1-20 are pending.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 10/24/2023 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Status of Claims
Applicant’s amendment date 02/11/2026, amending claim 1, 14, and 18..
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/19/2026 has been entered.
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 rejection is updated in light of the amendments.
Response to Arguments
Arguments regarding 35 USC 103 – the rejection is removed for the reason found in the “Allowable Subject Matter” section found below.
Applicant's arguments filed 02/11/2026 have been fully considered but they are not persuasive, moreover, any new grounds of rejection have been necessitated by applicant’s amendments to the claims.
Response to Argument under 35 USC 101:
Applicants argue: see applicant remarks pages 8-9
With regard to the §101 rejection, Applicant respectfully traverses on the ground that claims 1-20 as originally presented recite statutory subject matter. More particularly, the claims do not recite an abstract idea, and even if one were to assume for purposes of argument only that the claims did recite an abstract idea, the claims clearly integrate any such abstract idea into a practical application in the field of computer technology, in a manner that provides an improvement in computer technology.
…
Independent claim 1 is directed to an AI invention that provides a particular solution to an
important problem in the technological field of machine learning, … For the reasons set forth above, it is believed that this burden has not been met in the present § 101 rejection. It is therefore respectfully submitted that the § 101 rejection of the pending claims is improper and should be withdrawn.
Notwithstanding the foregoing traversal, independent claims 1, 14 and 18 have been
amended to further clarify that these claims recite a practical application in the form of a particular solution to a problem in a technological field.
Examiner respectfully disagree:
The Applicant's Specification titled "PRODUCT DESIGN PREDICTION USING MACHINE LEARNING" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user" (figure 14).
As the bolded claim limitations above demonstrate, independent claim1 recites the abstract idea for receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business offer of receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite for receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user. which pertain to "agreements in the form of contracts; legal obligation; behaviors; business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II).
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional element, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use exception, such that it is more than a drafting effort designed to monopolize the exception.
The claims recites the additional limitation of non-transitory an apparatus, a processing device, multiple output classification machine learning, device, memory are recited in a high level of generality and recited as performing generic computer functions routinely used in computer applications. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp. 134 S. Ct, at 2360,110 USPQ2d at 1984 (see MPEP 2106.05(f). The additional elements of a “machine learning”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning” is insufficient to show a practical application of the recited abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (step 2A-prong two: NO).
The Alice framework, we turn to step 2B (Part 2 of Mayo) to determine if the claim is sufficient to ensure that the claim amounts to “significantly more” than the abstract idea itself. These additional elements recite conventional computer components and conventional functions of:
Claims 1, 14 and 18 does not include my limitations amounting to significantly more than the abstract idea, along. Claims 1, 14, and 18 includes various elements that are not directed to the abstract idea. These elements include non-transitory an apparatus, a processing device, multiple output classification machine learning, device, memory. Examiner asserts that non-transitory an apparatus, a processing device, multiple output classification machine learning, device, memory are a generic computing element performing generic computing functions. (See MPEP 2106.05(f)).
Further, with data mining (i.e., searching over a network), receiving, processing, storing data, and parsing (i.e. extract, transform data) the courts have recognized the following computer function as well-understood, routing, and conventional functions when they are claimed in merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (i.e. “receiving, processing, transmitting, storing data”, etc.) are well-understood, routine, etc. (MPEP 2106.059d)).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices.
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, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-13 are directed to a method (process), claims 14-17 are directed to an apparatus (machine) and claims 18-20 are directed to an article of manufacturing comprising a non-transitory (machine). Thus, claims 1-20 fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-20 are directed toward the judicial exception of an abstract idea.
Independent claims 14, and 18 recites essentially the same abstract features as claim 1, thus are abstract for the same reason as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. A method comprising:
training a multiple output classification machine learning model with at least one dataset comprising historical product satisfaction data corresponding to respective ones of a plurality of products, wherein the training of the multiple output classification machine learning model comprises reading the at least one dataset, generating a data frame corresponding to the at least one dataset, wherein the data frame comprises a plurality of partitioned independent variables and a plurality of partitioned dependent variables, and identifying whether at least one of the plurality of partitioned independent variables and plurality of partitioned dependent variables affect a prediction of a plurality of scores for a plurality of satisfaction metrics for a product;
receiving a request to predict the plurality of scores for a plurality of satisfaction metrics for a product, wherein the request identifies a plurality of factors associated with the product;
inputting the request to the multiple output classification machine learning model; and
predicting, using the multiple output classification machine learning model, the plurality of scores in response to the request;
wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory.
The Applicant's Specification titled "PRODUCT DESIGN PREDICTION USING MACHINE LEARNING" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user" (figure 14).
As the bolded claim limitations above demonstrate, independent claim1 recites the abstract idea for receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business offer of receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite for receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user. which pertain to "agreements in the form of contracts; legal obligation; behaviors; business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II).
Dependent claims 2-13, 15-17, and 19-20 further reiterate the same abstract ideas with further embellishments (the bolded limitations), such as
claims 2 wherein the plurality of satisfaction metrics comprise two or more of a value metric, a functionality metric, a usability metric, a performance metric, a learnability metric, a reliability metric and an appearance metric.
claim 3 creating from the at least one dataset one or more independent variable datasets and one or more dependent variable datasets.
claim 4 wherein the one or more dependent variable datasets correspond to at least one of the value metric, the functionality metric, the usability metric, the performance metric, the learnability metric, the reliability metric and the appearance metric.
claim 5 wherein the plurality of factors comprise two or more of product type, domain, programming language, corresponding database, region and deployment type.
claim 6 (Similarly claims 15 and 19) wherein the multiple output classification machine learning model comprises a neural network having a plurality of parallel processing branches corresponding to respective ones of the plurality of satisfaction metrics, and wherein the plurality of parallel processing branches are connected to a same input layer.
claim 7 (Similarly claims 16 and 20) wherein the multiple output classification machine learning model comprises a plurality of output layers, wherein respective ones of the plurality of output layers correspond to respective ones of the plurality of processing branches, and wherein the respective ones of the plurality of output layers comprise a plurality of neurons respectively corresponding to possible values for the plurality of scores.
claim 8 wherein respective ones of the plurality of neurons use a Softmax activation function to classify respective ones of the plurality of scores.
claim 9 (Similarly claim 17) identifying one or more of the plurality of partitioned independent variables to remove from the at least one dataset based at least in part on whether the one or more of the plurality of partitioned independent variables affect the prediction of the plurality of scores; and removing the identified one or more of the plurality of partitioned independent variables from the at least one dataset; wherein the multiple output classification machine learning model is trained with the at least one dataset following the removal of the identified one or more of the plurality of partitioned independent variables.
claim 10 extracting the historical product satisfaction data from at least one of a storage system of an enterprise and one or more Internet sources.
claim 11 generating a report comprising the plurality of scores for the plurality of satisfaction metrics based at least in part on the prediction; and cause transmission of the report to one or more devices associated with a product development system.
claim 12 receiving feedback regarding the prediction; and generating at least one additional dataset based at least in part on the feedback; wherein the multiple output classification machine learning model is re-trained with the at least one additional dataset.
claim 13 comprising tagging one or more releases of the product with metadata corresponding to one or more of the plurality of satisfaction metrics.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 14, and 18.
Regarding Step 2A [prong 2]
Claims 1-20 fail to integrate the abstract idea into a practical application. Independent claims 1, 14, and 18 include the following additional elements which do not amount to a practical application:
Claim 1 multiple output classification machine learning, device, memory.
Claim 14 an apparatus, a processing device, multiple output classification machine learning, device, memory.
Claim 18 non-transitory, multiple output classification machine learning, device, memory.
The bolded limitations recited above in independent claims pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of non-transitory an apparatus, a processing device, multiple output classification machine learning, device, memory. which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, (fig. 1). Nothing in the Specification describes the specific operations recited in claims 1, 14 and 18 as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The additional elements of a “multiple classification machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “multiple classification machine learning model” is insufficient to show a practical application of the recited abstract idea.
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-13, 15-17, and 19-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, 14, and 18 respectively, claims 6, 15, and 19 recite a Neural Network but, these features only serve to further limit the abstract idea of independent claims
The additional elements of a “a Neural Network feature”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “a Neural Network feature” is insufficient to show a practical application of the recited abstract idea.
furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of the independent claims, include a non-transitory an apparatus, a processing device, multiple output classification machine learning, device, memory. Further, claims 1, 14, and 18 respectively, claims 6, 15, and 19 recite a Neural Network. The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed for of receiving a request to predict a plurality of scores for a plurality of satisfaction metrics and outputting the product to a user.
Claims 1-20 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Allowable Subject Matter
Regarding the 35 USC 103 rejection, Examiner has fully considered applicant’s argument and amendments. See applicant remarks pages 22-24.
The closest prior art of record are Sethuraman et al. US 2023/0206287: Machine learning product development life cycle model, Harang et al. US 2021/0241175: Methods and apparatus for management of a machine-learning model to adapt to changes in landscape of potentially malicious artifacts, Weller US 2015/0186334: System and method for automated generation of meaningful data insights, Colakoglu, Fatima Nur, Ali Yazici, and Alok Mishra. "Software product quality metrics: A systematic mapping study." IEEE Access 9 (2021): 44647-44670. None of the prior art of record, taken individually or in combination, teach, inter alia, teaches the claimed invention as detailed in independent claims, “training a multiple output classification machine learning model with at least one dataset comprising historical product satisfaction data corresponding to respective ones of a plurality of products, wherein the training of the multiple output classification machine learning model comprises reading the at least one dataset, generating a data frame corresponding to the at least one dataset, wherein the data frame comprises a plurality of partitioned independent variables and a plurality of partitioned dependent variables, and identifying whether at least one of the plurality of partitioned independent variables and plurality of partitioned dependent variables affect a prediction of a plurality of scores for a plurality of satisfaction metrics for a product;”. The reason for withdrawn the art rejection under 35 USC 103 rejection of claims 1-20 in the instant application is not apply because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Joung, Junegak, and Harrison Kim. "Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews." International Journal of Information Management 70 (2023): 102641.
Shye, Alex, et al. "Learning and leveraging the relationship between architecture-level measurements and individual user satisfaction." ACM SIGARCH Computer Architecture News 36.3 (2008): 427-438.
Chowdhury, Istehad, and Mohammad Zulkernine. "Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities." Journal of systems architecture 57.3 (2011): 294-313.
Siebel WO 2022/271686: Methods, processes, and systems to deploy artificial intelligence (AI)-based customer relationship management (CRM) system using model-driven software architecture.
Lyer US 2022/0301031: Machine learning based automated product classification.
Yang et al. US 2022/0261331: Feature deployment readiness prediction.
Hanna et al. US 2021/0383229: machine learning systems for location classification methods for using same.
Mauro et al. US 2017/0221080: Brand analysis.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624
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