DETAILED ACTION
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 .
Status of Claims
Claims 21-42 are pending in this instant application per claim amendments and remarks filed on 08/27/2025, wherein all previous Claims 21-38 have been amended, and new Claims 39-42 have been added. Claims 21 and 30 are independent claims reciting platform and method claims. Claims 22-29/39-40 and 31-38/41-42 are respective dependent claims.
This Office Action is a final rejection on merits in response to the remarks and the claim amendments filed by the Applicant on 27 AUGUST 2025 for its original application of 02 DECEMBER 2022 that is titled: “Robotic Fleet Configuration Method for Additive Manufacturing Systems”.
Accordingly, amended claims 21-42 are now being rejected herein.
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.
(NOTE: Latest ‘amendments to the claims’ filed by the Applicant on 08/27/2025 are shown as underlined additions, and all deletions may not be shown.)
Claims 21-42 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more, wherein Claims 21 and 30 are independent platform and method claims respectively.
Exemplary Analysis.
Claim 30: Ineligible.
The claim recites a series of steps. The claim is directed to a method reciting a series of steps, which is a statutory category of invention (Step 1 -- YES).
The claim is analyzed to determine whether it is directed to a judicial exception. The claim recites computerized method limitations about futures contract orchestration comprised of: receiving, from a data source, an indication associated with a set of items that are provided at least one of by or within a value chain; predicting a baseline cost associated with the set of items at a future point in time based on the indication; retrieving a futures cost, at a current point in time, of a futures contract associated with the set of items; generating a risk threshold based on a predefined risk tolerance of an entity of the value chain, the risk threshold indicating a difference between the baseline cost and the futures cost; and executing a smart contract for the futures contract based on the baseline cost, the futures cost, and the risk threshold. In other words, the claim relates to information technology methods for management of value chain network entities, including supply chain and demand management entities, which relate to the field of enterprise management platforms, more particularly involving an edge-distributed database and query language for storing and retrieving value chain data (see FIELD, para [0002] of Specification). These limitations, as drafted, are steps of a method that, under its broadest reasonable interpretation, covers performance of the limitations via a method of organizing human activity such as fundamental economic principles or practices (including hedging, insurance, mitigating risk, per recitations of at least ‘risk threshold’ and ‘risk tolerance’), and/or commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations, per recitations of at least ‘smart contract’ and data in a ‘value chain’), and/or managing behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions, per recitations of at least ‘futures costs’, ‘current point in time’ and ‘futures contract’), but for the recitation of generic computer/s and/or computer component/s such as the devices/mobile devices. These limitations fall under the “certain methods of organizing human activity” group (Step 2A1 -- YES).
Next, the claim is analyzed to determine if it is integrated into a practical application. The claim recites additional elements of: a robotic process automation system; an edge-distributed database architecture; network as in ‘value chain network’ (and/or plural ‘value chain networks’ in Specification); predicting, using machine learning, a baseline cost associated with the set of items at a future point in time based on the indication, wherein the machine learning prediction is trained on at least one of value chain network data or operational parameters to enhance baseline cost prediction accuracy; and executing a smart contract ……… via autonomous execution using the robotic process automation system, wherein the executing the smart contract includes the robotic process automation system dynamically discovering smart contract configuration opportunities based on real-time analysis of value chain network conditions and the generated risk threshold. Thus, the machine learning, machine learning processes, autonomous execution using the automation system, edge distributed architecture are interpreted as using the devices as a tool to accomplish the abstract idea. This is “apply it” at a high level of generality. The processor/s, database, and network/s in the steps are recited at a high level of generality, i.e., as generic processors performing generic computer/s functions of processing data. These generic processors are no more than mere instructions to apply the exception using generic computer/s and/or computer component/s. Accordingly, these additional elements do not integrate the abstract idea into a practical application, because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to the abstract idea (Step 2A2 -- NO).
Next, the claim is analyzed to determine if there are additional elements in this claim that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements in the claim amount to no more than mere instructions to apply the exception using generic computer/s and/or computer component/s. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer and/or computer components over a network cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the additional elements of: network as in ‘value chain network’ (and/or plural ‘value chain networks’ in Specification); predicting, using machine learning, a baseline cost associated with the set of items at a future point in time based on the indication, wherein the machine learning prediction is trained on at least one of value chain network data or operational parameters to enhance baseline cost prediction accuracy; and executing a smart contract ……… via autonomous execution using the robotic process automation system, wherein the executing the smart contract includes the robotic process automation system dynamically discovering smart contract configuration opportunities based on real-time analysis of value chain network conditions and the generated risk threshold, were considered to be extra-solution activities in Step 2A, they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine and conventional in the field. The disclosure does not provide any indication that these devices (processors) are anything other than generic processors and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05 (d) (II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Also, at least paras [0069]-[0070] and [0138]-[0139] of the Applicant’s own Specification describe ---
{“[0069] In some embodiments, the quantum computing system is physically implemented using an analog approach. In some of these embodiments, the analog approaches may be selected from the list of: quantum simulation, quantum annealing, and adiabatic quantum computation. In some embodiments, the quantum computing system is physically implemented using a digital approach. In some embodiments, the quantum computing system is an error-corrected quantum computer. In some embodiments, the quantum computing system applies trapped ions to execute the quantum computing task. …………………………………………………………… [0070] In some embodiments, the quantum computing task relates to automatically discovering smart contract configuration opportunities in a value chain network. In some of these embodiments, the quantum-established smart contract applications are selected from the set of: booking a set of robots from a robotic fleet, booking a smart container from a smart container fleet, and executing transfer pricing agreements between subsidiaries. In some embodiments, the quantum computing task relates to risk identification or risk mitigation. In some embodiments, the quantum computing task relates to accelerated sampling from stochastic processes for risk analysis. In some embodiments, the quantum computing task relates to graph clustering analysis for anomaly or fraud detection. In some embodiments, the quantum computing task relates to generating a prediction. …………………………………………………..
……………………………………………………………………………………………………………………………………………… [0138] A computerized method for autonomous future contract orchestration includes receiving, from a data source, an indication associated with a product that relates to an entity that at least one of purchases or sells the product. The method includes predicting a baseline cost of at least one of purchasing or selling the product at a future point in time based on the indication. The method includes retrieving a futures cost, at a current point in time, of a futures contract for an obligation to the at least one of purchasing or selling the product for at least one of delivery or performance of the product at the future point in time. The method includes executing a smart contract for the futures contract based on the baseline cost and the futures cost. The method includes orchestrating the at least one of delivery or performance of the product at the future point in time. ………………………………………………………………………………………… [0139] In other features, the computerized method includes retrieving a risk data structure indicating an amount of risk the entity is willing to accept with respect to the baseline cost and the futures cost and executing the smart contract based on the risk data structure to at least one of manage or mitigate risk. In other features, the computerized method includes demand-side planning using a robotic process automation system and orchestrating the smart futures contract based on the demand-side planning. In other features, the computerized method includes derisking with respect to the futures contract and the smart contract using a robotic agent. In other features, the computerized method includes executing a system for performing circular economy optimization based on futures pricing of goods. In other features, the computerized method includes initializing a robotic process automation system trained to execute the smart contract and executing the smart contract using the robotic process automation system. In other features, retrieving the indication includes retrieving at least one of an event occurrence, a physical condition of an item, or a potential demand increase.”} ---
and indicate that the concept described by the extra-solution additional elements is conventional. Accordingly, a conclusion that the aforementioned extra-solution additional elements are well-understood, routine and conventional activity is supported under Berkheimer options 2 and 3, respectively.
Viewing the limitations as an ordered combination does not add anything further
than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional elements do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B -- NO), and the claim is not patent eligible.
The analysis above applies to all statutory categories of the invention including independent platform Claim 21 (which recites ‘a set of one or more processors’ as the additional elements, instead of ‘value chain network’), which perform the steps similar to those of the independent method Claim 30. Furthermore, the limitations of dependent method Claims 31-38/41-42, further narrow the independent method Claim 30 with additional steps and limitations (e.g., the generating the risk threshold based on at least one of hedging for or providing improved outcomes after adverse contingencies; the generating the risk threshold based on at least one of: shortages in supply, supply chain disruptions, changes in demand, changes in prices of inputs, or changes in market prices as the adverse contingencies; the predicting the baseline cost includes predicting the baseline cost based on providing operational efficiencies; the predicting the baseline cost based on at least one of insuring availability of items based on plans or insuring availability of items based on availability predictions as the operational efficiencies; the executing the smart contract includes executing the smart contract based on improving returns; the executing the smart contract includes executing the smart contract based on obtaining inputs at more favorable prices than the baseline cost indicates; the executing the smart contract includes executing a smart contract that interacts with futures markets associated with the futures contract; the executing the smart contract to engage with at least one of futures or options involving at least one of commodities, equities, currencies, or energy associated with the futures contract; wherein the robotic process automation system dynamically discovering the smart contract configuration opportunities further comprises executing quantum processes via a quantum computing system to automatically discover the smart contract configuration opportunities; and performing quantum risk analysis using accelerated sampling from stochastic processes for risk assessment; etc.), and do not resolve the issues raised in rejection of the independent method Claim 30. Similarly, dependent platform Claims 22-29/39-40 also further narrow their independent Claim 21 respectively, which are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
Therefore, claims 21-42 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. The Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1,148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1.) Determining the scope and contents of the prior art.
2.) Ascertaining the differences between the prior art and the claims at issue.
3.) Resolving the level of ordinary skill in the pertinent art.
4.) Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-38/41-42 are rejected under 35 USC 103 as unpatentable over a combination of references (Lecomte, Cella, Rogerson and Notani) as described below for each claim/ limitation.
(NOTE: Latest ‘amendments to the claims’ filed by the Applicant on 08/27/2025 are shown as bold and underlined additions, and all deletions may not be shown, or may not be underlined when stricken through. Underlined amendments to the claims that are shown below are from previously submitted claim amendments by the Applicant.)
Exemplary Analysis for Rejection of Claims 30-38/41-42
Independent Claim 30 is rejected under 35 USC 103 as unpatentable over Pub.
No. US 2008/ 0243667 filed by Lecomte, Patrick P. (hereinafter “Lecomte”) in view of Pub. No. US 2021/ 0182995 filed by Cella et al. (hereinafter “Cella”), and further in view of Pub. No. US 2022/ 0318899 filed by Rogerson et al. (hereinafter “Rogerson”), and further in view of Pub. No. US 2015/0006427 filed by Notani et al. (hereinafter “Notani”), and as described below for each claim/ limitation.
Examiner notes that all claims have been copied as recited by the Applicant to keep them readable and whole, even if the limitations within a claim that are not taught explicitly by the primary/previous reference (are noted in parentheses), but these limitations are noted explicitly as taught by a secondary/new reference whenever a secondary/new reference has been used.
Examiner notes that, for brevity in this rejection, the motivation statement has not been repeated herein every time a secondary reference has been used.
With respect to Claim 30, Lecomte teaches ---
30. (Currently Amended) A computerized method for autonomous futures contract orchestration, using (a robotic process automation system), the computerized method comprising:
(see at least: Lecomte Abstract and Brief Summary of the Invention in paras [0027]-[0028]; and para [0046] about {“…… This new derivative defines the `smart bomb template` presented in this specification. The smart bomb template will take the shape of a futures contract based on a broadly defined sub-index with add-on factor based features (e.g. options), resulting in a customizable hybrid hedging vehicles made up of standardized (and hence tradable) derivatives products (see Drawings--Figure C).”}; and para [0052] about {“…… an interactive computer assisted platform with specific scroll menus and choices based on type of hedge and property type selected by users …”}; which together are the same as claimed limitations above to include ‘autonomous futures contract’ per BRI rules {aka ‘smart futures contract’ taught by Lecomte}, and ‘computerized method’)
Lecomte teaches as disclosed above, but it may be argued that it may not explicitly disclose about ‘(a robotic process automation system)’. However, Cella teaches them explicitly.
(see at least: Cella Abstract and Summary in paras [0007]-[0000]; and paras [0039], [0051] & [0061] about {“…… In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system. …”}; and para [0356] about {“……… a product configuration application 870 (such as for allowing a product manager and/or automated product configuration process (optionally using robotic process automation) to determine a configuration for a product 650, …”}; which together are the same as claimed limitations above to include ‘a robotic process automation system’)
It would have been obvious prior to the time of the effective filing date of the claimed invention to have an ordinary person of skill in the art to modify the teachings of Lecomte with the teachings of Cella. The motivation to combine these references would be to provide current property derivatives that are meant for hedging at the aggregate level, but property derivatives usually offer poor hedging effectiveness, especially in the context of individual buildings and small, under-diversified portfolios of assets (see para [0004] of Lecomte), and to fulfill a need for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations (see para [0006] of Cella).
Lecomte and Cella teach ---
receiving, from a data source via an edge-distributed database architecture, (an indication associated with a set of items) that are provided at least one of by or within (a value chain network);
(see at least: Lecomte ibidem and citations listed above; and para [0059] about {“General principles of the Market: The Market for Hedging Effectiveness will make massive use of modern information technologies. Databases containing historical and real time information about pure factors are fed into a platform (called system in the specification). …”}; which together are the same as claimed limitations above to include ‘a data source’)
(see at least: Cella ibidem and citations listed above; and paras [0039], [0051] & [0061] about {“…… In embodiments, the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. …”}; which together are the same as claimed limitations above to include ‘an edge-distributed database architecture’)
Lecomte and Cella teach as disclosed above, but they may not explicitly disclose about ‘(an indication associated with a set of items)’. However, Rogerson teaches them explicitly.
(see at least: Rogerson Abstract; and para [0228] about {“…… the transaction parameters of each of the data items specifying an expiration time period of a set of future expiration time periods, e.g. a number of months, and a proposed transaction price indicative of an interest rate value to be determined at the end of the specified expiration time period based on a set of daily interest rates set prior thereto by a governing authority, each matched group of electronic data transaction request messages being associated with a prevailing transaction price indicative of an expected interest rate value to be determined at the end of the specified expiration time period based on the set of daily interest rates set prior thereto by the governing authority.”}; and para [0250] about {“…… for the same one of the data items based on multiple transaction parameters, received from different client computers over a data communications network, the transaction parameters of each of the data items specifying an expiration time period of a set of future expiration time periods, e.g. a number of consecutive months, and a proposed transaction price indicative of an interest rate value to be determined at the end of the specified expiration time period based on a set of daily interest rates set prior thereto by a governing authority, each matched group of electronic data transaction request messages being associated with a prevailing transaction price indicative of an expected interest rate value to be determined at the end of the specified expiration time period based on the set of daily interest rates set prior thereto by the governing authority.”}; and Claims 1, 16 and 31; which together are the same as claimed limitations above including ‘an indication associated with a set of items’)
It would have been obvious prior to the time of the effective filing date of the claimed invention to have an ordinary person of skill in the art to modify the teachings of Lecomte and Cella with the teachings of Rogerson. The motivation to combine these references would be to provide current property derivatives that are meant for hedging at the aggregate level, but property derivatives usually offer poor hedging effectiveness, especially in the context of individual buildings and small, under-diversified portfolios of assets (see para [0004] of Lecomte), and to fulfill a need for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations (see para [0006] of Cella), and to provide the underlying survey methodology at its core render the process for inter-bank overnight interest rate which cannot be fully automated and objectively calculated and will necessarily always comprise a bias-able and/or manipulatable underlying component necessitating significant regulation and oversight (see para [0019] of Rogerson).
Lecomte, Cella and Rogerson teach as disclosed above, but they may not explicitly disclose about ‘(a value chain network)’. However, Notani teaches it explicitly.
(see at least: Notani Abstract and Summary of the Invention in paras [0009]-[0010]; and Abstract for ‘value chain network’; and paras [0003], [0006]-[0010], [0012]-[0020], [0024], [0026]-[0028] and [0051] for ‘value chain network’; and paras [0029], [0031], [0033]-[0035], [0037]-[0040], [0042]-[0043], [0048] and [0050] for ‘federated value chain network 100’; which together are the same as claimed limitations above to include ‘a value chain network’)
It would have been obvious prior to the time of the effective filing date of the claimed invention to have an ordinary person of skill in the art to modify the teachings of Lecomte, Cella and Rogerson with the teachings of Notani. The motivation to combine these references would be to provide current property derivatives that are meant for hedging at the aggregate level, but property derivatives usually offer poor hedging effectiveness, especially in the context of individual buildings and small, under-diversified portfolios of assets (see para [0004] of Lecomte), and to fulfill a need for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations (see para [0006] of Cella), and to provide the underlying survey methodology at its core render the process for inter-bank overnight interest rate which cannot be fully automated and objectively calculated and will necessarily always comprise a bias-able and/or manipulatable underlying component necessitating significant regulation and oversight (see para [0019] of Rogerson), and to provide improvements to resolve existing deficiencies that are associated with enterprise value chain replenishment, order and logistics planning and execution, and, in particular, with a global transaction manager in a federated value chain network (see para [0008] of Notani).
Lecomte, Cella, Rogerson and Notani teach ---
predicting, using machine learning, a baseline cost associated with the set of items at a future point in time based on the indication, wherein the machine learning prediction is trained on at least one of value chain network data or operational parameters to enhance baseline cost prediction accuracy;
(see at least: Lecomte ibidem and citations listed above)
(see at least: Cella ibidem and citations listed above; and paras [0018], [0047] & [0048] about {“……configuration of a set of inputs for machine learning, …”}; and para [0363] for “machine learning systems, deep learning systems, supervised learning systems”; and para [0366] about {“…… The cross-application nature of the platform layer 604 thus facilitates convenient organization of all of the necessary infrastructure elements for adding intelligence to any given application, such as by supplying machine learning on outcomes across applications, providing enrichment of automation of a given application via machine learning based on outcomes from other applications or other elements of the platform 604, …… outputs and outcomes 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated.”}; and para [0393] about {“……… such as a classification-adapted neural network, a prediction-adapted neural network and the like. As an example of hybrid adaptive intelligence systems 614, a machine learning-based artificial intelligence system may be provided for the set of demand management applications 824 and a neural network-based artificial intelligence system may be provided for the set of supply chain applications 812. ……… a hybrid artificial intelligence system3060 may provide two types of artificial intelligence to different applications, such as different demand management applications 824 (e.g., a sales management application and a demand prediction application) or different supply chain applications 812 (e.g., a logistics control application and a production quality control application).”}; and para [0394] about {“.....
coordinated intelligence through a hybrid artificial intelligence capabilities may be provided to a demand planning application by a feed-forward neural network, to a demand prediction application by a machine learning system, to a sales application by a self-organizing neural network, to a future demand aggregation application by a radial basis function neural network, …”}; and para [0395] for “predictions 3070” and “machine learning systems, deep learning systems, supervised learning systems”; and para [0539] about {“Referring to FIG. 43, the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652. The machine learning model 3000 is an algorithm and/or statistical model that performs specific tasks without using explicit instructions, relying instead on patterns and inference. The machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The sensor data input to the machine learning model 3000 may be used to train the machine learning model 3000 to perform the analytics, simulation, decision making, and prediction making relating to the data processing, data analysis, simulation creation, and simulation analysis of the one or more of the value chain entities 652. …”}; and para [0556] about {“....The training data may be represented in the machine learning model 3000 by a matrix. The machine learning model 3000 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new inputs. …”}; and para [0632] about “machine learning system 2002” that train “machine learned models 2004” and “predictions on behalf of value chain system 2030”; which together are the same as claimed limitations above to include ‘using machine learning’ and ‘the machine learning prediction is trained on ......’)
(see at least: Rogerson ibidem and citations listed above to include ‘a set of items’; and para [0035] about {“Therefore, the disclosed embodiments leverage the price of Fed funds futures contracts for different months so as to determine how the market expects the federal funds rate to move over time and automatically predict the cost of money beyond the current date.”}; which together are the same as claimed limitations above to include ‘predict a baseline cost’ and ‘at a future point in time’)
(see at least: Notani ibidem and citations listed above)
Lecomte, Cella, Rogerson and Notani teach ---
retrieving a futures cost, at a current point in time, of a futures contract associated with the set of items;
(see at least: Lecomte ibidem and citations listed above)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above to include ‘futures contract’ and ‘a set of items’; and para [0002] about {“…… A forward interest rate is the price, determined at the time the rate is set, of money for a future time period, e.g. the rate represents today's cost of future money. Short term interest rates are interest rates typically used for debt with future maturities less than one year from the current date and are typically administered by the central banks of nations, …”}; which together are the same as claimed limitations above to include ‘a futures cost’ and ‘a current point in time’)
(see at least: Notani ibidem and citations listed above)
Lecomte, Cella, Rogerson and Notani teach ---
generating a risk threshold based on a predefined risk tolerance of an entity of the value chain network, the risk threshold indicating a difference between the baseline cost and the futures cost; and
(see at least: Lecomte ibidem and citations listed above; and para [0062] about {“….. Tailor-made factor hedges will create a market with no cross hedge basis risk, no mismatch of maturity, and no risk of manipulation in underlying real estate markets. The market will allow inter asset class counterparty of `pure` factors.”}; which together are the same as claimed limitations above to include ‘a predefined risk tolerance’ per BRI rules)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above to include ‘a baseline cost’ and ‘a futures cost’; and para [0080] about {“As an intermediary, the Exchange bears a certain amount of risk in each transaction that takes place. To that end, risk management mechanisms protect the Exchange via the Clearing House. The Clearing House establishes performance bonds (margins) for all Exchange products and establishes minimum performance bond requirements for customers of Exchange products. …”}; and para [0137] about {“A risk management module 114 may be included to compute and determine a user's risk utilization in relation to the user's defined risk thresholds. The risk management module 114 may also be configured to determine risk assessments or exposure levels in connection with positions held by a market participant. The risk management module 114 may be configured to administer, manage or maintain one or more margining mechanisms implemented by the exchange computer system 100. …”}; which together are the same as claimed limitations above to include ‘a risk threshold’ and ‘a predefined risk tolerance’)
(see at least: Notani ibidem and citations listed above to include ‘a value chain network’)
Lecomte, Cella, Rogerson and Notani teach ---
executing a smart contract for the futures contract based on the baseline cost, the futures cost, and the risk threshold via autonomous execution using the robotic process automation system, wherein the executing the smart contract includes the robotic process automation system dynamically discovering smart contract configuration opportunities based on real-time analysis of value chain network conditions and the generated risk threshold.
(see at least: Lecomte ibidem and citations listed above to include ‘futures contract’; and para [0032] about {“……… Combinative Derivatives are the first type of instruments covered. In its simplest form, a combinative derivative could be made up of a futures contract tied to a property type sub-index and add-on features linked to selected economic indicators. This model follows what this specification calls the `smart bomb template`. Components of these aggregate hedges are individually tradable. …”}; and para [0046] already cited above; which together are the same as claimed limitations above to include ‘a smart contract’)
(see at least: Cella ibidem and citations listed above to include ‘a robotic process automation
system’; and paras [0039], [0051] & [0061] for “smart contract system”; and para [0356] for “smart contracts”; and para [0353] for “autonomous vehicles” and “features …… that are executed using intelligence capabilities on an intelligent product 650” as well as “a robotic/ autonomous vehicle system” and “a smart contract that is configured to execute an arbitrage transaction,”; and para [0445] about {“……The robotic process automation 1442 may be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442(e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people. …”}; and para [1116] for “real-time data received via an API” and “real-time data received via the API system 8014” and “real-time updating” as well as “dynamic allocation of use of cellular and other wireless spectrum, adaptive, ad-hoc, cognitive management of wireless mesh network nodes” and “smart-contract-implemented network resource allocation”; and para [0353] about {“……… and a smart contract application, solution, or service (referred to collectively herein as a smart contract application 848, such as, without limitation, any of the smart contract types referred to in this disclosure or in the documents incorporated herein by reference, such as a smart contract for sale of goods, a smart contract for an order for goods, a smart contract for a shipping resource, a smart contract for a worker, a smart contract for delivery of goods, a smart contract for installation of goods, a smart contract using a token or cryptocurrency for consideration, a smart contract that vests a right, an option, a future, or an interest based on a future condition, a smart contract for a security, commodity, future, option, derivative, or the like, a smart contract for current or future resources, a smart contract that is configured to account for or accommodate a tax, regulatory or compliance parameter, a smart contract that is configured to execute an arbitrage transaction, or many others). …”}; which together are the same as claimed limitations above and highlighted above to include ‘smart contract for futures contract’ and ‘smart contract’ and ‘smart contract configuration opportunities’ as well as ‘autonomous execution’ per BRI rules)
(see at least: Rogerson ibidem and citations listed above to include ‘a baseline cost’, ‘a futures cost’ and ‘a risk threshold’)
(see at least: Notani ibidem and citations listed above)
Dependent Claims 31-38 are rejected under 35 USC 103 as unpatentable over Lecomte in view of Cella, Rogerson and Notani as applied to the rejection of independent Claim 30 above, and as described below for each claim/ limitation.
With respect to Claim 31, Lecomte, Cella, Rogerson and Notani teach ---
31. (New) The computerized method of claim 30 wherein generating the risk threshold includes generating the risk threshold based on at least one of hedging for or providing improved outcomes after adverse contingencies.
(see at least: Lecomte ibidem and citations listed above)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above to include ‘a risk threshold’; and para [0035] about {“…… Fed Funds futures contracts are a way for one, via the price they pay for the contract, to assess or hedge the future Fed Funds overnight rate, e.g., to hedge against or speculate on changes in short term interest rates. …”}; and para [0082] about {“…… e.g., to gain or hedge exposure to future interest rate changes, and for different durations over which the subject interest rate is calculated, …… e.g. for the purpose of hedging or mitigating risk, etc.”}; and para [0084] about {“……… Fed Funds futures provide trading opportunities and hedging resources for the management of risk exposures associated with a variety of money market interest rates. …”}; and para [0085] about {“……… Fed Funds futures can be used either speculatively to anticipate changes in monetary policy or more conservatively to hedge inventory financing risk across many different markets.”}; which together are the same as claimed limitations above to include ‘hedging for or providing improved outcomes after adverse ….’)
(see at least: Notani ibidem and citations listed above)
With respect to Claim 32, Lecomte, Cella, Rogerson and Notani teach ---
32. (New) The computerized method of claim 31 wherein generating the risk threshold includes generating the risk threshold based on at least one of:
shortages in supply, supply chain disruptions, changes in demand, changes in prices of inputs, or changes in market prices as the adverse contingencies.
(see at least: Lecomte ibidem and citations listed above)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above to include ‘a risk threshold’; and para [0030] about {“… i.e., other than for reasons related to supply/demand, such as due to governing authority policy changes. …”}; and para [0038] about {“…… in manner that indicated a signi-ficant change in supply/demand conditions so as to affect the baseline rate. …”}; and para [0035] about {“…… a contract expiring in the month after a fed meeting more fully expresses market expectations as, for deferred expirations, the price is based on the average expected fed rate for the expiration month, …”}; and para [0040] about {“The baseline rate, in combination with the turn rates accounting for seasonal irregularities in supply and demand as evidenced by deviations in the interest rates from baseline values, creates a model for what the forward rates will be including adjustments. This allows for the creation of a short-term forward rate,…”}; and para [0041] about {“…… The orange line 702 depicts the constant overnight rate during each futures period and depicts jumps wherever there are futures prices changing at the end of every month. …”}; and para [0080] about {“…… and debits from those clearing members holding open short positions, the pecuniary value of the change in contract price. If the mark-to-market records a decrease in the price of the contract, …”}; which together are the same as claimed limitations above to include ‘shortages in supply’, ‘changes in demand’, ‘changes in prices of inputs’ or ‘changes in market prices’)
(see at least: Notani ibidem and citations listed above to include ‘a value chain network’)
With respect to Claim 33, Lecomte, Cella, Rogerson and Notani teach ---
33. (New) The computerized method of claim 30 wherein predicting the baseline cost includes predicting the baseline cost based on providing operational efficiencies.
(see at least: Lecomte ibidem and citations listed above)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above to include ‘a baseline cost’; and para [0047] for ‘main ‘efficient’ cause’; and para [0113] about {“…… to offer a more efficient, fair and balanced market where market prices reflect a true consensus of the value of traded products among the market participants, …”}; which together are the same as claimed limitations above to include ‘operational efficiencies’)
(see at least: Notani ibidem and citations listed above)
With respect to Claim 34, Lecomte, Cella, Rogerson and Notani teach ---
34. (New) The computerized method of claim 33 wherein predicting the baseline cost includes predicting the baseline cost based on at least one of insuring availability of items based on plans or insuring availability of items based on availability predictions as the operational efficiencies.
(see at least: Lecomte ibidem and citations listed above)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above to include ‘a baseline cost’ and ‘operational efficiencies’; and para [0080] about {“…… for the purpose of insuring the broker or Clearing House against loss due to breach of contract on open futures or options contracts. …”}; which together are the same as claimed limitations above to include ‘insuring ……’)
(see at least: Notani ibidem and citations listed above)
With respect to Claim 35, Lecomte, Cella, Rogerson and Notani teach ---
35. (New) The computerized method of claim 30 wherein executing the smart contract includes executing the smart contract based on improving returns.
(see at least: Lecomte ibidem and citations listed above to include ‘a smart contract’)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above; and para [0047] about {“…… As described above, this represents the total interest rate returned or expected to be returned. …”}; which together are the same as claimed limitations above to include ‘improving returns’)
(see at least: Notani ibidem and citations listed above)
With respect to Claim 36, Lecomte, Cella, Rogerson and Notani teach ---
36. (New) The computerized method of claim 35 wherein executing the smart contract includes executing the smart contract based on obtaining inputs at more favorable prices than the baseline cost indicates.
(see at least: Lecomte ibidem and citations listed above to include ‘a smart contract’)
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above; and para [0195] about {“……wherein each identified order is contra to the incoming order and has a favorable price relative to the incoming order. …”}; and para [0202] about {“…… and that have an identical price which is favorable to the price of the incoming order, …”}; which together are the same as claimed limitations above to include ‘more favorable prices’)
(see at least: Notani ibidem and citations listed above)
With respect to Claim 37, Lecomte, Cella, Rogerson and Notani teach ---
37. (New) The computerized method of claim 30 wherein executing the smart contract includes executing a smart contract that interacts with futures markets associated with the futures contract.
(see at least: Lecomte ibidem and citations listed above to include ‘futures contract’ and ‘a smart contract’; and para [0032] about {“…… a combinative derivative could be made up of a futures contract tied to a property type sub-index and add-on features linked to selected economic indicators. …”}; and para [0046] about {“……The smart bomb template will take the shape of a futures contract based on a broadly defined sub-index with add-on factor based features (e.g. options), …”}; which together are the same as claimed limitations above to include ‘futures markets’ {associated with the futures contract})
(see at least: Cella ibidem and citations listed above)
(see at least: Rogerson ibidem and citations listed above; and paras [0105]-[0106] about {“…...
A futures contract is a legally binding agreement to buy or sell a commodity or other underlier, such as a financial instrument, at a specified price at a predetermined future time. An option is the right, but not the ob