DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
[2] This communication is in response to the amendment filed 16 April 2026. It is noted that this application is a National Stage Entry for International Application Serial No. PCT/2020/055809 having an international filing date of 15 October 2020. Claims 3, 6, 10, 13, 16, and 19 have been cancelled. Claims 1, 8, and 15 have been amended. Claim 21 has been added. Claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, 20, and 21 are pending.
Response to Remarks/Amendment
[3] Applicant's remarks filed 16 April 2026 have been fully considered and are addressed as follows:
[i] In response to rejection(s) of claim(s) 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 (now claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, 20, and 21 as presented by amendment) under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 17 December 2025, Applicant provides the following remarks:
"…the December Memorandum provides updates…Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins…claim 1 recites training and re-training a machine learning model for fill rate prediction…as the training and retraining of the machine learning model based on generated training data recites improvements to computer functionality, in accordance with the Desjardins Appeals Review Panel Decision…the subject matter of claim 1 is not directed to an abstract idea…”
Applicant further remarks:
"…Like Example 47, claim 3, the present claims recite an improvement to these existing solutions by enabling prediction of fill rates based on seller-side inputs, which "does not require input from the vendors" and does not require "technological integration of information systems with all of [the seller's] vendors"…The features of claim 1 reflect a technical improvement to enable more efficient fill rate prediction by leveraging inputs that are readily available to the seller, and which do not require updating or maintaining by external parties. Thus, like Example 47, the present claim 1 integrates any alleged judicial exception into a practical application that improves upon machine learning model utilized in full delivery prediction generation such that claim 1 is not directed to the judicial exception…”
In response, Examiner respectfully disagrees. With respect to the functionality of the previously recited “machine learning model”, claim 8 has been amended to further clarify “…executing a machine learning model on the order attribute data, the seller-based rank, the center-based rank, and the recency data, to determine a probability of an in-full delivery of the at least one order from the vendor to the seller using gradient boosted decision trees…”.
With respect to any similarity between the functionality of the instant claims and the basis for concluding that the technical features/functions presented in example 47, claim 3 of the July 2024 Subject Matter Eligibility Guidelines Update, Examiner notes that limitations directed to the training and using the trained ANN were found to amount to mere instructions to apply the exception using a generic computer and do not integrate the abstract idea into a practical application. Notably, the subsequently claimed steps directed to identifying malicious packets and blocking network traffic from identified from the identified source address integrated the judicial exception into a practical application on the basis that the network intervention improves the functioning of the computer. While Applicant contends that the training and re-training of the claimed machine learning model using gradient boosted decisions trees of the instant claims as amended mirrors the integrating features of example 47, claim 3, Examiner respectfully submits that the instant claims are limited to the designated inputs and outputs provided to the machine learning model which more directly mimics the steps of example 47 claim 3 found to be limited to a general application of the exception using a generic computer.
With respect to any similarity between the functionality of the instant claims and the basis for concluding that the technical features/functions presented in Appeals Review Panel decision in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025), Examiner notes that basis for identifying an integrating technical element under Step 2 Prong 2 reside in the claimed functions of “…training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems”. While Applicant contends that the training and re-training of the claimed machine learning model using gradient boosted decisions trees of the instant claims as amended mirrors the integrating features of the claims at issue in Ex Parte Desjardins, Examiner respectfully submits that the instant claims are limited to the designated inputs and output of a generic machine learning model, absent any further clarification as to how the recited training and/or use of the machine learning model constitutes an improvement to underlying technology akin to the mechanisms overcoming ‘catastrophic forgetting’ of Ex Parte Desjardins.
While the instant claims serve to leverage machine learning to provide a more efficient business outcome, the instant claims as currently constructed do not provide for any improvement in the underlying locating technology. Rather, the managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers benefits from commercially available technology at the time of the invention, at least as presently claimed. The rejection of the pending claims under 35 U.S.C. 101 is respectfully maintained.
[ii] Applicant’s remaining remarks in response to previous rejection(s) of claim(s) 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 (now claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, 20, and 21 as presented by amendment) under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 17 December 2025 are reasonably considered to have been fully addressed in the context of the revised rejection of the claims presented above responsive to the amendments to the subject claims and in consideration of the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
[4] Previous rejection(s) of claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 (now claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, 20, and 21 as presented by amendment) under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention has/have not been overcome by the amendments to the subject claims and is/are maintained. The revised statement of rejection presented below is necessitated by amendment and addresses the present amendments to the pending claims.
Claim 8 as presented by amendment recites “…executing a machine learning model on the order attribute data, the seller-based rank, the center-based rank, and the recency data, to determine a probability of an in-full delivery of the at least one order from the vendor to the seller using gradient boosted decision trees, wherein the machine learning model is trained using supervised machine learning…” and “…generating training data based on an update of at least one of the seller-based rank or the center-based rank and re-training the machine learning model using the training data…”.
Examiner notes that the claims as amended identify a general machine learning category/methodology (e.g., using gradient boosted decision trees) to be applied in an unspecified manner to predict the recited probabilities. However, the claim remains reliant on claimed functions in which two distinct rank elements, i.e., “seller-based rank” and “center-based rank” are used as specified inputs to the machine learning model. With respect to the machine learning model being trained and applied using inputs of the two distinct ranks, the Specification paragraph [0056] provides, generally, that rank data can be used to train the machine learning model and that rank data can include both an overall rank and a distribution center rank. Paragraphs [0060] and [0106] each indicate that the rank data can be used by the machine learning model to predict a probability of an in-full delivery for a specific order. None of the noted disclosure appears to provide a description of how the model applies the two independent ranks of vendor performance compared to other vendors to predict the initial vendor’s probability of delivering a present order in-full. Paragraph [0108] appears to provide the only description of how the consideration of vendor rank, generally, may be predictive of performance on a present order by stating that the rank elements were inputted into a machine learning model “exhibited 84% accuracy at the distribution level when predicted fill rates were compared to actual fil rate”. Accordingly, the description appears to be limited to an observed prediction accuracy based on inputting the noted rank elements into the predictive model. For purposes of further examination, Examiner considers the claimed invention to utilize vendor/supplier scoring/rank information, generally, as an input and output of the claimed model that is predictive of vendor/supplier performance in fulfilling placed orders for specific goods.
Independent claims 1 and 15 are similarly amended and are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Dependent claims 2, 4-5, 7, 9, 11-12, 14, 17-18, and 20-21 inherit and fail to remedy the deficiencies of their respective parent claims through dependency and are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
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.
[5] Previous rejection(s) of claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 (now claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, 20, and 21 as presented by amendment) under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more has/have not been overcome by the amendments to the subject claims and is/are maintained. The revised statement of rejection presented below is necessitated by amendment and addresses the present amendments to the pending claims.
The following analysis is based on the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024 and further clarified in the Reminders on Evaluating Subject Matter Eligibility of claims under 35 U.S.C. 101 guidance memorandum published 4 August 2025. Claim(s) 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, 20, and 21 as a whole is/are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might serve to impede, rather than promote, innovation. Still, inventions that integrate the building blocks of human ingenuity into something more by applying the abstract idea in a meaningful way are patent eligible (See MPEP 2106.04).
Consistent with the findings of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ineligible abstract ideas are defined in groups, namely: (1) Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) Mental Processes (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions); and (3) Certain Methods of Organizing Human Activity. Groupings of Certain Methods of Organizing Human Activity include three sub-categories within the group, namely: (1) fundamental economic principles or practices; (2) commercial or legal interactions (e.g., agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); (3) managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) (See MPEP 2106.04(a).
Eligibility Step 1: Four Categories of Statutory Subject Matter (See MPEP 2106.03): Independent claims 1, 8, and 15 are directed to a system, a method, and non-transitory computer-readable storage medium, respectively, and are reasonably understood to be properly directed to one of the four recognized statutory classes of invention designated by 35 U.S.C. 101; namely, a process or method, a machine or apparatus, an article of manufacture, or a composition of matter. While the claims, generally, are directed to recognized statutory classes of invention, each of method/process, system/apparatus claims, and computer-readable media/articles of manufacture are subject to additional analysis as defined by the Courts to determine whether the particularly claimed subject matter is patent-eligible with respect to these further requirements. In the case of the instant application, each of claims 1, 8, and 15 are determined to be directed to ineligible subject matter based on the following analysis/guidance:
Eligibility Step 2A prong 1: (See MPEP 2106.04): In reference to claim 8, the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do/does not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers, which is reasonably considered to be method of Organizing Human Activity. In particular, the general subject matter to which the claims are directed serves to predict or forecast a fill rate for an order of marketable goods by a seller using a prediction model and further rank vendors/supplier based on fill rate performance, which is an ineligible concept of Organizing Human Activity, namely: commercial interactions (e.g., directing marketing or sales activities or behaviors and business relations) and managing personal behavior or relationships or interactions between people (e.g., commercial interactions between sellers and vendors).
In support of Examiner’s conclusion, Examiner respectfully directs Applicant’s attention to the claim limitations of representative claim 8. In particular, claim 8 as presented by amendment includes:
“…obtaining order attribute data characterizing at least one order placed by a seller from a vendor… obtaining a seller-based rank characterizing a first rank of the vendor compared to all other vendors associated with the seller; obtaining a center-based rank characterizing a second rank of the vendor compared to other vendors that deliver orders to a same distribution center of the seller, obtaining recency data characterizing a past supply performance…”, “…determining a probability of an in-full fill rate of the at least one order from the vendor to the seller…”
Considered as an ordered combination, the steps/functions of claim 8 are reasonably considered to be representative of the inventive concept and are further reasonably understood to be series of actions or activities directed to a general process of managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers, which is an ineligible concept of Organizing Human Activity, namely: commercial interactions (e.g., directing marketing or sales activities or behaviors and business relations) and managing personal behavior or relationships or interactions between people (e.g., commercial interactions between sellers and vendors) (See MPEP 2106.04(a)(2)).
The technical elements and the recited functions constitute technical features which have been considered at each step of Examiner’s analysis but are determined to constitute generic computing structures executing generic computing functions previously identified by the courts, as further analyzed under Step 2A prong 2 and Step 2B below.
Eligibility Step 2A prong 2: (See MPEP 2106.04(d)): Under step 2A prong two, Examiners are to consider additional elements recited in the claim beyond the judicial exception and evaluate whether those additional elements integrate the exception into a practical application. Further, to be considered a recitation of an element which integrates the judicial exception into a practical application, the additional elements must apply, rely on, or use the judicial exception in a manner that imposes meaningful limits on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
Additional elements of claim 8 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject matter include:
The technical elements identified in claim 8 are limited to: “computing device”, “machine-learning model”. Claims 1 and 15 further introduce a “processor/device”, “memory”, and computer-executable “instructions” With respect to these potential additional elements:
(1) The “processor/device”, “memory”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “computing device” is identified as of the supply partner and receiving the transmitted probability.
(3) The “machine-learning model” as presented by amendment is identified as: “…executing a machine learning model on the order attribute data, the seller-based rank, the center-based rank, and the recency data, to determine a probability of an in-full delivery of the at least one order from the vendor to the seller using gradient boosted decision trees…” and “…generating training data based on an update of at least one of the seller-based rank or the center-based rank and re-training the machine learning model using the training data…”.
With respect to the above noted functions attributable to the identified additional elements, MPEP 2106.05 stipulates that: (1) There are no additional elements in the claim; (2) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f); (3) Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g); and/or (4) Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) serve as indications that the use of the technology recited does not indicate integration into a practical application of the judicial exception.
With respect to the recitation of “…executing a machine learning model on the order attribute data, the seller-based rank, the center-based rank, and the recency data, to determine a probability of an in-full delivery of the at least one order from the vendor to the seller using gradient boosted decision trees…” and “…generating training data based on an update of at least one of the seller-based rank or the center-based rank and re-training the machine learning model using the training data…”, Examiner notes the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register on 17 July 2024. In particular, Examiner respectfully directs Applicant’s attention to Example 47, claim 2. Specifically, the instant recitations of “executing a model… using gradient boosted decision trees” and “training the model” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training. Accordingly, the recited machine-learning processes and associated training are performable using mental observations/decisions to adjust and apply known mathematical processes, but fail to specify any technical steps in obtaining the results other than to state that the model is trained.
Each of the above noted limitations states a result (e.g., data is obtained, a probability is determined using a defined mathematical model, a probability is sent etc.) as associated with a respective “computing device” or “machine learning model”. Beyond the general statement that data and probabilities are obtained and sent and a machine learning model is trained, the limitations provide no further clarification with respect to the functions performed by the “computing device” and “machine learning model” in producing the claimed result. A recitation of “by a device” or “by a model”, absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The identified functions performed by the recited technology are limited to: (1) receiving and sending data via a computer network (e.g., order attribute, rank and recency data, probabilities); (2) storing and retrieving information and data from a generic computer memory (e.g., models and data); and (3) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., determining a probability using a defined model) (See MPEP 2106.05(f)).
Accordingly, claim 8 is reasonably understood to be conducting standard, and formally manually performed process of managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers, using the generic devices as tools to perform the abstract idea. The identified functions of the recited additional elements reasonably constitute a general linking of the abstract idea to a generic technological environment. The claimed managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers, benefits from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception.
Eligibility Step 2B: (See MPEP 2106.05): Analysis under step 2B is further subject to the Revised Examination Procedure responsive to the Subject Matter Eligibility Decision in Berkheimer v. HP, Inc. issued by the United States Patent and Trademark Office (19 April 2018). Examiner respectfully submits that the recited uses of the underlying computer technology constitute well-known, routine, and conventional uses of generic computers operating in a network environment. In support of Examiner’s conclusion that the recited functions/role of the computer as presented in the present form of the claims constitutes known and conventional uses of generic computing technology, Examiner provides the following:
In reference to the Specification as originally filed, Examiner notes paragraphs [0033]-[0037] and [0041]-[0048]. In the noted disclosure, the Specification provides listings of generic computing systems, e.g., a general computing platform including exemplary servers, network configurations and various processor configuration which are identified as capable and interchangeable for performing the disclosed processes. The disclosure does not identify any particular modifications to the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
While the above noted disclosure serves to provide sufficient explanation of technical elements required to perform the inventive method using available computing technology, the disclosure does not appear to identify any particular modifications or inventive configurations of the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that the disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Further, absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
The claims specify that the above identified generic computing structures and associated functions/routines include:
(1) The “processor/device”, “memory”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “computing device” is identified as of the supply partner and receiving the transmitted probability.
(3) The “machine-learning model” as presented by amendment is identified as: “…executing a machine learning model on the order attribute data, the seller-based rank, the center-based rank, and the recency data, to determine a probability of an in-full delivery of the at least one order from the vendor to the seller using gradient boosted decision trees…” and “…generating training data based on an update of at least one of the seller-based rank or the center-based rank and re-training the machine learning model using the training data…”.
While Examiner acknowledges that the noted limitations are computer-implemented, Examiner respectfully submits that, in aggregate (e.g., “as a whole”) they do not amount to significantly more than the abstract idea/ineligible subject matter to which the claimed invention is primarily directed.
While utilizing a computer, the claimed invention is not rooted in computer technology nor does it improve the performance of the underlying computer technology. The computer-implemented features of the claimed invention noted above are reasonably limited to: (1) receiving and sending data via a computer network (e.g., order attribute, rank and recency data, probabilities); (2) storing and retrieving information and data from a generic computer memory (e.g., models and data); and (3) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., determining a probability using a defined model).
The above listed computer-implemented functions are distinguished from the generic data storage, retrieval, transmission, and data manipulation/processing capacities of the generic systems identified in the Specification solely by the recited identification of particular data elements that are of utility to a user performing the specific method of managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers, because it has been programmed to store, retrieve, and transmit specific data elements and/or instructions that is/are of utility to the user. The non-technical functions of managing commercial interactions between sellers and vendors/suppliers ranking vendors and suppliers, benefit from the use of computer technology, but fail to improve the underlying technology.
In support, the courts have previously found that utilization of a computer to receive or transmit data and communications over a network and/or employing generic computer memory and processor capacities store and retrieve information from a computer memory are insufficient computer-implemented functions to establish that an otherwise unpatentable judicial exception (e.g. abstract idea) is patent eligible. With respect to the determinations of the Courts regarding using a computer for sending and receiving data or information over a computer network and storing and retrieving information from computer memory, see at least: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; sending messages over a network OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); receiving and sending information over a network buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 and see performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) with respect to the performance of repetitive calculations does not impose meaningful limits on the scope of the claims.
Independent claims 1 and 15, directed to an apparatus/system and computer-executable instructions stored on computer-readable media for performing the method steps are rejected for substantially the same reasons, in that the generically recited computer components in the apparatus/system and computer readable media claims add nothing of substance to the underlying abstract idea.
Dependent claims 2, 4-5, 7, 9, 11-12, 14, 17-18, 20, and 21 when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claimed invention is not directed to an abstract idea.
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.
In accordance with all relevant considerations and aligned with previous findings of the courts, the technical elements imparted on the method that would potentially provide a basis for meeting a “significantly more” threshold for establishing patent eligibility for an otherwise abstract concept by the use of computer technology fail to amount to significantly more than the abstract idea itself. For further guidance and authority, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al. 573 U.S.____ (2014)) (See MPEP 2106).
Claim Rejections - 35 USC § 103
[6] Previous rejection(s) of claim(s) 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (United States Patent Application Publication No. 2021/0158236) in view of Melancon et al. (United States Patent No. 11,429,927) and further in view of Glick et al. (United States Patent Application Publication No. 2022/0036305) has/have been overcome by the amendments to the subject claims and is/are withdrawn.
Subject Matter Overcoming the Art of Record
[7] Claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, 20, and 21 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The most closely applicable prior art of record is referred to in the Office Action mailed 17 December 2025 as Bikumala et al. (United States Patent Application Publication No. 2021/0158236). Bikumala provides system and method which utilizes AI to analyze supplier characteristics. The system and method include calculation of supplier specific scores and rankings to evaluate supplier performance with respect to delivery of an order of goods. The system and method further utilize the supplier scores to select the best, highest scoring, suppliers for a desired product.
While Bikumala is similar to the instant application in many respects, there are clear patentable distinctions. Initially, while the scoring and ranking of suppliers is, broadly, a mechanism of assessing a supplier’s reliability with respect to fulfillment of orders for specified products, Bikumala et al. fail to indicate that the fulfillment rate is represented or displayed as a probability with respect to a specific order and further fail to specify that the calculated supplier score/ranking and/or probability is generated from the order attribute data, the seller-based rank, the center-based rank, and the recency data, to determine a probability of an in-full delivery of the at least one order from the vendor to the seller.
Conclusion
[8] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cited NON-PATENT Literature:
Yang et al., Research and application of BP Neural Networks in Collaborative Supply Chain VMI's customer demanding, 2010-12-01, 5th International Conference on Pervasive Computing and Applications (2010, Page(s): 348-353): Relevant Teachings: Yang discloses a system/method that provides a vendor-managed inventory method for managing supply chains. The publication establishes that at least monitoring of vendor inventory levels by merchant partners is common practice in the art.
Cited PATENT Literature:
Herman et al, RISK SCORING OF SUPPLIERS AND TRIGGERING PROCUREMENT WORKFLOW IN RESPONSE THERETO, United States Patent Application Publication No. 2020/0293962, paragraphs [0025]-[0030]: Relevant Teachings: Herman discloses a system/method that includes steps/functions determining relative supplier risk in the form of calculated scores. Herman utilizes the scores to determine ordering practices including fulfillment lead time.
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).
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/ROBERT D RINES/Primary Examiner, Art Unit 3625
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