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 .
Continued Examination Under 37 CFR 1.114
[2] 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1 October 2025 has been entered.
Notice to Applicant
[3] This communication is in response to the Amendment and the Request for Continued Examination (RCE) filed 1 October 2025. 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. Claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 are pending.
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] Claims 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 are 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. 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.
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…” 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 two distinct rank elements, i.e., “seller-based rank” and “center-based rank”, 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 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 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, and 20 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 as the vendor, 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…” 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…” 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” 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…” 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, 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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
[6] Claim(s) 1-2, 4-5, 7-9, 11-12, 14-15, 17-18, and 20 is/are rejected 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).
With respect to (currently amended) claim 8, Bikumala et al. disclose a method comprising: obtaining order attribute data characterizing at least one order placed by a seller from a vendor; (Bikumala et al.; paragraphs [0039] [0041] [0044]; See at least order data including quantity, delivery date, and time to deliver, i.e., lead time. The suppliers and manufacturers are reasonably forms or “vendors” and “sellers”); obtaining rank data characterizing a supply performance of the vendor compared to supply performances of one or more other vendors (Bikumala et al.; paragraphs [0031]-[0033] [0037] [0057]-[0058]; See at least scores and ranked listing of suppliers); obtaining recency data characterizing a past supply performance of the vendor (Bikumala et al.; paragraphs [0041]-[0044]; See at least feature-specific supplier performance and scoring including measures of strengths related to quantity, quality, delivery schedule and average time to deliver based on order. See further the assessments are based on historical, i.e., recency data. The suppliers are reasonably forms of vendors); determining a probability of an in-full fill rate of the at least one order using a fill rate prediction model (Bikumala et al.; paragraphs [0041] [0044]-[0045]; See at least prediction of fulfillment rate of the supplier if the supplier were chosen to fulfill a part or whole of the product order. See further predictions reported as scores generated by machine-learning predictive model); and transmitting the probability to a computing device associated with a supply partner (Bikumala et al.; paragraphs [0045] [0055] [0057]; See at least listing and ranking based on product-specific predictive scores for each supplier sent to TAM generator for analyst review).
With respect to the indication that a probability of an in-full fill rate is determined, Bikumala et al. disclose that the predictive model calculates a fulfillment rate of a supplier is the supplier were chosen to fulfill a part or whole of the product order specified (Bikumala et al.; paragraphs [0041] [0044]-[0045]). Bikumala et al. further calculate feature specific scores associated with producer strength, delivery time, and relative scores indicative of supplier performance at different production locations). Lastly, Bikumala et al. disclose calculation of features specific scores including predictions of best supplier location score/rank, i.e., distribution center, and overall supplier score/rank including the predicted fulfillment rate (Bikumala et al.; paragraphs [0032] [0044] [0047]; See at least scores and ranks of suppliers including predictions of best supplier location score/rank, i.e., distribution center, and overall supplier score/rank including the predicted fulfillment rate).
While Examiner submits that the predicted part or whole fulfillment rate and listing of suppliers by calculated scoring/ranking constitutes a probability of a supplier fulfilling a particular order in comparison to the other suppliers, Bikumala et al. fail to specific indicate that the fulfillment rate is represented or displayed as a probability.
However, as evidenced by Melancon et al., it is well-known in the art to generate a percentage or probability of a successful fulfillment of an order based on a manufacturer/supplier predicted or historical fill rate (Melancon et al.; col. 19, lines 40-67 and col. 20, lines 1-15 and lines 37-67; See at least user interface/dashboard, predicted fill rates and calculations of percentage order orders by SKU and manufacturer successfully filled on time and associated calculated probabilities).
Claim 8 has been amended with respect to the previously recited probability and model to further specify “…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 as the vendor;… 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… 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 these elements, as noted above, Bikumala et al. disclose that a machine learning model calculates a fulfillment rate of a supplier based on ranking of suppliers, historical order data, i.e., attribute data, and are based on historical, i.e., recency data and further dynamically training the model based on historical outputs of the model including fulfillment ranking (Bikumala et al.; paragraphs [0033] [0041] [0044]-[0045]). Bikumala et al. further calculate feature specific scores associated with producer strength, delivery time, and relative scores indicative of supplier performance at different production locations). Lastly, Bikumala et al. disclose calculation of features specific scores including predictions of best supplier location score/rank, i.e., distribution center, and overall supplier score/rank including the predicted fulfillment rate (Bikumala et al.; paragraphs [0032] [0044] [0047]; See at least scores and ranks of suppliers including predictions of best supplier location score/rank, i.e., distribution center, and overall supplier score/rank including the predicted fulfillment rate). Bikumala et al. fail to specific indicate that the fulfillment rate is represented or displayed as a probability and further fail to specify that the calculated supplier score is directed to “delivery” of a proposed order. While Bikumala discloses ranking of vendors/suppliers, Bikumala fails to specify a ranking of scoring of suppliers to defined distribution center locations. Melancon et al. fail to remedy the deficiencies of Bikumala et al.
However, Glick et al. disclose a predictive model that utilizes a supplier rank based on delivery to a specified distribution center based on historical order data and order attribute information where the model generates a score/rank that is probability that the supplier will be able to delivery the order in the defined timeline (Glick et al.; paragraphs [0014] [0064]-[0065] [0070]-[0071]; See at least model adjusts supplier score based on occurrence of ranking associated with fulfillment probabilities to a specified distribution center location).
Regarding the combination that includes Melancon et al., it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the predictive fill rates by supplier and rankings Bikumala et al. by further including conversion of fill rates to probabilities and displaying values in supplier analysis dashboard as taught Melancon et al. The instant invention is directed to a system and method of determining optimal suppliers for orders of goods. As Bikumala et al. disclose the use of predictive fill rates by supplier in the context of a system and method for determining optimal suppliers for orders of goods and Melancon et al. similarly disclose the utility generating a percentage or probability of a successful fulfillment of an order based on a manufacturer/supplier predicted or historical fill rate in the context of a system and method for determining optimal suppliers for orders of goods, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by the simple substitution of one known element (fill rate) for another/equivalent (probability of order fulfillment) to obtain the predictable result of optimizing supplier selections for a given order thereby improving product production and improving inventory controls and cost effectiveness of a supply chain.
Regarding the combination that includes Glick et al., it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the predictive fill rates by supplier and rankings Bikumala et al. by further including scoring/ranking of suppliers based on probabilities of fulfillment to a specified distribution center location of delivery as taught Glick et al. The instant invention is directed to a system and method of determining optimal suppliers for orders of goods. As Bikumala et al. disclose the use of predictive fill rates by supplier in the context of a system and method for determining optimal suppliers for orders of goods and Glick et al. similarly disclose the utility of scoring/ranking of suppliers based on probabilities of fulfillment to a specified distribution center location in the context of a system and method for determining optimal suppliers for orders of goods, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by the simple substitution of one known element (fill rate) for another/equivalent (probability of order fulfillment) to obtain the predictable result of optimizing supplier selections for a given order thereby improving product production and improving inventory controls and timeliness of component delivery within a supply chain.
With respect to claim 9, Bikumala et al. disclose a method wherein the order attribute data comprises quantity of items ordered, date of placement of the at least one order and lead time (Bikumala et al.; paragraphs [0039] [0041] [0044]; See at least order data including quantity, delivery date, and time to deliver, i.e., lead time).
Claim 10 has been cancelled.
With respect to claim 11, Bikumala et al. disclose a method wherein the recency data comprises an average fill rate for a predetermined number of previously placed orders and an overall average fill rate for each item in the at least one order (Bikumala et al.; paragraphs [0041]-[0044]; See at least feature-specific supplier performance and scoring including measures of strengths related to quantity, quality, delivery schedule and average time to deliver based on order. See further the assessments are based on historical, i.e., recency data).
With respect to claim 12, Bikumala et al. disclose a method further comprising: determining at least one predicted future order; and determining a probability of an in-full delivery of the at least one predicted future order using the machine learning model (Bikumala et al.; paragraphs [0041] [0044]; See at least predictions applied to specific future product order); and determining a probability of an in-full fill rate for the at least one predicted future order using the fill rate prediction model (Bikumala et al.; paragraphs [0041] [0044]-[0045]; See at least prediction of fulfillment rate of the supplier if the supplier were chosen to fulfill a part or whole of the product order. See further predictions reported as scores generated by machine-learning predictive model).
Claim 13 has been cancelled.
With respect to claim 14, Bikumala et al. disclose a method further comprising displaying the score on a fill rate user interface, wherein the supply partner is one of: the vendor, another vendor, a supply analyst, a retailer or a distribution center (Bikumala et al.; paragraphs [0045] [0055] [0057]-[0058]; See at least listing and ranking based on product-specific predictive scores for each supplier sent to TAM generator for analyst review and selections of suppliers for percentages of production).
Bikumala et al. disclose calculation of features specific scores including predictions of best supplier location score/rank, i.e., distribution center, and overall supplier score/rank including the predicted fulfillment rate. Bikumala et al. further specifies that listed rankings of suppilers are generated (Bikumala et al.; paragraphs [0032] [0044] [0047]; See at least scores and ranks of suppliers including predictions of best supplier location score/rank, i.e., distribution center, and overall supplier score/rank including the predicted fulfillment rate). While the listed rankings and scores are generated and delivered to the TAM generator to allocate to suppliers, Bikumala et al. fail to specify that the information is displayed on an interface.
However, as evidenced by Melancon et al., it is well-known in the art to generate a percentage or probability of a successful fulfillment of an order based on a manufacturer/supplier predicted or historical fill rate (Melancon et al.; col. 19, lines 40-67 and col. 20, lines 1-15 and lines 37-67; See at least user interface/dashboard, predicted fill rates and calculations of percentage order orders by SKU and manufacturer successfully filled on time and associated calculated probabilities).
Regarding claim 14, the conclusions obviousness and rationale to modify as discussed above with respect to claim 8 are applicable to claim 14 and are incorporated by reference herein.
[7] Claims 1-2, 4-5, 7, 17-18, and 20 substantially repeat the subject matter addressed above with respect to claims 8-9, 11-12, 14 as directed to the enabling system and CRM storing executable instructions. With respect to these elements, Bikumala et al. disclose enabling the disclosed method employing analogous systems and executable instructions (See at least Bikumala et al. paragraphs [0031]-[0033]). Accordingly, claims 1-2, 4-5, 7, 17-18, and 20 are rejected under the applied teachings, conclusions obviousness, and rationale to modify as discussed above with respect to claims 8-9, 11-12, 14.
Response to Remarks/Amendment
[8] Applicant's remarks filed 1 October 2025 have been fully considered and are addressed as follows:
[i] Applicant’s remarks in response to 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. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 1 July 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) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update),
published in the Federal Register, 17 July 2024. Additionally, Applicant substantially rehashes arguments previously presented in the prior response. These arguments are addressed in accordance with Examiner’s response in the prior Office Action(s) mailed 1 July 2025 and 19 December 2024, incorporated in their entirety in response.
[ii] Applicant’s remarks directed to 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(a) as being unpatentable as set forth in the previous Office Action mailed 1 July 2025 have been fully considered and are moot in light of newly added grounds of rejection responsive to the amendments to the subject claims. See revised rejection under 35 U.S.C. 103(a) presented below. See further claim interpretation under the rejection of the claims under 35 U.S.C. 112(a).
Conclusion
[9] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pace et al., SUPPLY CHAIN OPTIMIZATION AND INVENTORY MANAGEMENT SYSTEM, United States Patent Application Publication No. 2020/0311657, paragraphs [0069]-[0073]: Relevant Teachings: Pace discloses a system/method that provides a supplier performance report. The system/method generates a ranking of suppliers based on specified item delivery.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT D RINES whose telephone number is (571)272-5585. The examiner can normally be reached M-F 9am - 5pm.
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, Beth V Boswell can be reached at 571-272-6737. 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.
/ROBERT D RINES/Primary Examiner, Art Unit 3625