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 final rejection. Claims 1-6, 8-13, and 15-20 are pending.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 08/21/2023 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Status of Claims
Applicant’s response date 04/03/2026, Amending claims 1, 6, 8, 13, 15, and 20.
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 rejection is updated in light of the amendments.
With regard to the rejection under 35 USC 103- No art rejection has been put forth in the rejection for the reason found in the “Allowable Subject Matter” section found below. Also, See applicant remarks pages 15-17 04/03/2026.
Response to Arguments
Applicant’s argument received on date 04/03/2026 have been fully considered, but they are not persuasive.
Response to Arguments under 35 USC 101:
Applicants argue: see remarks pages 11-12 (Abstract Idea)
With respect to the assertion that the claim recites an abstract idea, Applicant submits that the claims, at best, involve an exception and do not recite an exception. In this regard, the MPEP states that "Examiners should ... be careful to distinguish claims that recite an exception (which require further eligibility analysis) and claims that merely involve an exception (which are eligible and do not require further eligibility analysis)." MPEP § 2106.04(Il)(A)(l) (emphasis in original).
The present claims merely involve an exception, instead of reciting an exception. The claims, when taken as a whole, recite arrangements directed to training and executing a machine learning data set to analyze pre-processed data to efficiently generate requirements outputs, and continuously updating the machine learning model based on specific data received via a feedback loop to improve accuracy of the machine learning model. The claims recite a closed loop system of training, executing and updating a machine learning model based on data from registered entities having access to the data. Further, the claims recite controlling access to the requirements data based on validating credentials of an entity. Accordingly, while the claim may involve an abstract idea, when considered as a whole, the claim does not recite an abstract idea.
Examiner respectfully disagree:
The Applicant's Specification titled " Machine Learning-Based Requirements Prediction" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time" (Spec. figure 3).
As the claim limitations below demonstrate, independent claims 1, 8 and 15 are recites the abstract idea of outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time. 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 below provide a business solution of outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time. 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).
Applicants argue: see remarks pages 12-14 (Step 2A, Prong 2 and 2B)
The claims clearly recite a practical application of any alleged abstract idea. For example, claim 1 recites multiple, specific, detailed, unique steps performed at particular devices that train and execute a machine learning model to analyze pre-processed data to efficiently (e.g., preprocessed to reduce the number of computing resources needed to generate machine learning outputs) generate requirements outputs. The claims further recite continuously updating the machine learning model,
using specific data received via a feedback loop from particular entities, to improve the accuracy of the machine learning model for subsequent requirements prediction generation. Further, the claims recite controlling access to the requirements data based on validating credentials of an entity. The claims are necessarily rooted in computer technology and provide specific improvements to the field of machine learning.
….
The claims clearly recite a practical application of any alleged abstract idea. For example, claim 1 recites multiple, specific, detailed, unique steps performed at particular devices that train and execute a machine learning model to analyze pre-processed data to efficiently (e.g., preprocessed to reduce the number of computing resources needed to generate machine learning outputs) generate requirements outputs. The claims further recite continuously updating the machine learning model, using specific data received via a feedback loop from particular entities, to improve the accuracy of the machine learning model for subsequent requirements prediction generation. Further, the claims recite controlling access to the requirements data based on validating credentials of an entity. The claims are necessarily rooted in computer technology and provide specific improvements to the field of machine learning. For at least these reasons, Applicant submits that claim 1 is patent-eligible and requests that the rejection of claim 1 under 35 U.S.C. § 101 be withdrawn.
Examiner respectfully disagree:
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 a computing platform, processor, a communication interface, a memory, a machine learning model, a system, and a non-transitory computer-readable media 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 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 “machine learning model” is insufficient to show a practical application of the recited abstract idea. With regard to continuous update, Model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. Model retraining does not change the parameters and variables used in the model. It adapts the model to the current data so that the existing parameters give healthier and up-to-date outputs
The use of generic computer component to "receive historical data from a plurality of sources …. Receive, from a plurality of entities, current purchase data for a plurality of items …. Identifying a user associated with the current purchase data; …. A request to access the requirements prediction for a first item type …. Output a requirements prediction for each item type for a particular time period … receive a request to access the requirements prediction for a first item type …. Validate the request to access the requirements prediction … allow the entity to access the requirements prediction for the first item type" does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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).
Further, with regard to 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 functions as well-understood, routing, and conventional functions when they are claimed in a 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 2106.05(d))
The Alice framework, step 2B (Part 2 of Mayo) 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, 8, and 15 does not include my limitations amounting to significantly more than the abstract idea, along. Claims 1, 8, and 15 includes various elements that are not directed to the abstract idea. These elements include a computing platform, processor, a communication interface, a memory, a machine learning model, a system, and a non-transitory computer-readable media.
Examiner asserts that a computing platform, processor, a communication interface, a memory, a machine learning model, a system, and a non-transitory computer-readable media are a generic computing element performing generic computing functions, (see MPEP 2106.05(f))
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.
In addition, fig. 5 of the specifications detail any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
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 1-6, 8-13, and 15-20 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 1-6 are directed toward a method (process). Claims 8-13 are directed to a system (machine) and Claims 15-23 are directed to a non-transitory (machine). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-6, 8-13, and 15-20 are directed toward the judicial exception of an abstract idea. Independent claims 8, and 15 recites essentially the same abstract features as claim 1, thus are abstract for the same reasons as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. A computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
register a plurality of entities eligible to access requirements predictions, wherein registering the plurality of entities includes receiving validation credentials from each entity of the plurality of entities;
store the validation credentials;
receive historical data from a plurality of sources, the historical data including purchase data, time of year, time of day and natural disaster data;
train, based on the received historical data, a machine learning model to identify patterns or sequences in purchase data to output one or more requirements predictions;
receive, from the plurality of registered entities eligible to access requirements predictions, current purchase data for a plurality of items;
segment the purchase data based on item type of items purchased within the current purchase data to reduce a number of computing resources needed to generate requirements predictions;
anonymize the current purchase data, wherein anonymizing the current purchase data includes masking information identifying a user associated with the current purchase data;
execute, on segmented anonymized current purchase data for each item type,
execute, on segmented anonymized current purchase data for each item type, the machine learning model, wherein executing the machine learning model includes inputting the segmented, anonymized current purchase data to the machine learning model to output a requirements prediction for each item type for a particular time period;
receive, from a first external entity system of a first registered entity of the plurality of registered entities, a request to access the requirements prediction for a first item type, wherein the request includes validation credentials of the first registered entity;
validate the request to access the requirements prediction for the first item type by the first external entity system, wherein validating the request to access the requirements prediction for the first item type by the first external entity system includes comparing the received validation credentials to stored validation credentials of the first registered entity;
responsive to validating the request to access the requirements prediction for the first item type by the first external entity system by determining that the received validation credentials match the stored validation credentials: allow the first external entity system to access the requirements prediction for the first item type;
continuously update, based on a feedback loop providing inventory data received from the plurality of registered entities that are eligible to access the requirements prediction, the machine learning model to improve accuracy of the machine learning model for generation of subsequent requirements predictions; and
responsive to not validating the request by determining that the received validation credentials do not match the stored validation credentials of the first entity, deny the request to access the requirements prediction for the first item type.
The Applicant's Specification titled " Machine Learning-Based Requirements Prediction" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time" (Spec. [19]).
As the bolded claim limitations above demonstrate, independent claims 1, 8 and 15 are recites the abstract idea of outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time. 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 solution of outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time. 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 2-6, 8-13, and 16-20 further reiterate the same abstract ideas with further embellishments, such as
claim 2 (Similarly Claims 9 and 16) wherein the requirements prediction for the first item type includes a predicted number of the first item type to manufacture for the particular time period.
claim 3 (Similarly Claims 10 and 17) further analyze the current purchase data and requirements prediction for the first item type to identify secondary external entities associated with the first external entity system and additional material requirements associated with the identified secondary external entities; and
transmit the additional material requirements to the identified secondary external entities, wherein transmitting the additional material requirement to the identified secondary external entities causes display of the additional material requirements by a display of a computing device associated with each identified secondary external entity.
claim 4 (Similarly Claims 11 and 18) wherein the additional material requirements include an amount of raw materials to manufacture a number of the first item type in the requirements prediction for the first item type.
claim 5 (Similarly Claims 12 and 19) the secondary external entities are suppliers of an entity associated with the first external entity system.
claim 6 (Similarly Claims 13 and 20) wherein validating the request to access the requirements prediction for the first item type by the first external entity system is further based on a public/private key pair.
claim 7 (Similarly Claims 14 and 21) Cancelled
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 8 and 15.
Regarding Step 2A [prong 2]
Claims 1-6, 8-13, and 15-20 fail to integrate the abstract idea into a practical application. Independent claims 1, 8 and 15 include the following additional elements which do not amount to a practical application:
Claim 1.
A computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
train … a machine learning
execute a machine learning model, wherein executing the machine learning model a first external entity system
Claim 8. a computing platform, the computing platform having at least one processor and memory train … a machine learning
a machine learning model a first external entity system
Claim 15 One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to: train … a machine learning
a machine learning model a first external entity system
The bolded limitations recited above in independent claims 1, 8 and 15 pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of a computing platform, processor, a communication interface, a memory, a machine learning model, a system, and a non-transitory computer-readable media 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. 5). Nothing in the Specification describes the specific operations recited in claim 1 (Similarly claims 8 and 15) 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 “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 “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 outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time 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 output a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time. 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 2-6, 9-13, and 16-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, 8 and 15 respectively, for example, claim 3 (similarly claims 10 and 17), claim 6 (similarly claims 13 and 20) recite a display of a computing device, and a public/private key pair but these features only serve to further limit the abstract idea of independent claims 1, 8 and 15, 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 1-6, 8-13, and 15-20 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 claims 1, 8 and 15 include a computing platform, processor, a communication interface, a memory, a machine learning model, a system, and a non-transitory computer-readable media. 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 to outputting a requirements prediction or needs prediction, wherein the requirement prediction may include a number or volume of units of a particular items type that should be manufactured to satisfy a predicted need over a period of time.
Claims 1-6, 8-13, and 15-20 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
Claims 1-6, 8-13, and 15-20 are allowable over the prior art, however, these claims remain rejected under 35 USC 101.
Closest prior art to the invention include Nandan et al. US 2021/0272387 disclose Machine learning classification and prediction system, Reily et al US 2021/0209252 disclose Methods, systems, and media for data anonymization, Cella et al. US 2023/0281533: Demand-responsive raw material management system, Wang, Wenbo, et al. "A proactive manufacturing resources assignment method based on production performance prediction for the smart factory." IEEE Transactions on Industrial Informatics 18.1 (2021): 46-55.None of the prior art of record, taken individually or in combination, teach, inter alia, teaches the claimed invention as detailed in the independent claims, “segment the purchase data based on item type of items purchased within the current purchase data to reduce a number of computing resources needed to generate requirements predictions; …. validate the request to access the requirements prediction for the first item type by the first external entity system, wherein validating the request to access the requirements prediction for the first item type by the first external entity system includes comparing the received validation credentials to stored validation credentials of the first registered entity; …. continuously update, based on a feedback loop providing inventory data received from the plurality of registered entities that are eligible to access the requirements prediction, the machine learning model to improve accuracy of the machine learning model for generation of subsequent requirements predictions; and responsive to not validating the request by determining that the received validation credentials do not match the stored validation credentials of the first entity, deny the request to access the requirements prediction for the first item type. ” The reason for withdrawn the 35 USC 102/103 rejection of claims 1-6, 8-13, and 15-20 in the instant application is 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.
Wang, Wenbo, et al. "A proactive manufacturing resources assignment method based on production performance prediction for the smart factory." IEEE Transactions on Industrial Informatics 18.1 (2021): 46-55.
Maikhuri et al. US 2024/0152862: intelligent item management in an information processing system.
Zagorin et al. US 2024/0144171: Procurement modeling system for predicting price reasonableness.
Gosain et al. US 2024/0135313: Intelligent management of inventory items in an information processing system.
Muthusamy et al. US 11,853,938: System and method of reinforced machine-learning retail allocation.
Mukherjee US 2022/0414739: Recommendation engine accounting for preference drift.
Walczak et al. US 11,321,580: Item type discovery and classification using machine learning.
Novak et al. US 11,080,727: Global optimization of inventory allocation.
Cella WO2024/025863: Systems and methods for providing process automation and artificial intelligence, market aggregation, and embedded marketplaces for a transactions platform.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAMZEH OBAID whose telephone number is (313)446-4941. The examiner can normally be reached M-F 8 am-5 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HAMZEH OBAID/Primary Examiner, Art Unit 3624
Prosecution Insights