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
This action is in response to the amendment filed on 4/3/2026. Claims 1-5, 8, 10-23 are pending. Claims 1, 3, 5, 8, 11-14 are amended. Claims 21-23 have been added. Claims 6, 7, 9 have been cancelled.
Response to Arguments
Applicant's arguments filed 4/3/2026 have been fully considered but they are not persuasive. The applicant has argued that “In particular, the amended independent claims now require, by way of the above-referenced limitations, training of at least two inference modes which are technical steps that cannot be performed within the human mind with just the aid of pen and paper. In fact, the Office's own patent eligibility examples (namely Example 39 that can be retrieved from https://www.uspto.gov/sites/default/files/documents/101_examples_37to42 20190107.pdf) recognize that the training of an inference model (e.g., a machine learning model) is NOT a mental process. For at least the above reasons, Applicant submits that the amended independent claims are not directed to a mental process and the test for eligibility should end at Step 2A, Prong One. Thus, The amended independent claims are patent eligible at Step 2A, Prong One.” The examiner respectfully disagrees. The claims still contain certain methods of organizing human activity and a mental process. The claims are directed to managing commercial and contractual relationships. The claims also involve observation, evaluation, and judgement which can be done in a human mind. The newly amended in training steps are claimed as broad limitations. The training a neural network is generic conventional data processing. Claim 1 recites obtaining, comparing, generating, and making a recommendation. None of the limitations recites technological implementation details for any of these steps. Training of the neural network, may improve the quality of the data output, but the models are invoked merely as tools.
Applicant’s claim 1 is distinguishable from Example 39. Most significantly, the hypothetical claim in Example 39 recites specific steps for training and retraining the neural network, including applying transformations, including “mirroring, rotating, smoothing, or contrast reduction,” to the input “digital facial images” to create a modified set of digital facial images. The instance of Example 39 the claims were found eligible because the additional elements beyond the training step integrated the abstract idea into a practical application. It was not the merely the existence of a training step that integrated the claim. Claim 1 recites that the neural networks are trained but does not disclose how they are trained. The claim does not delineate any specific steps, like those in Example 39, for training the category machine learning model to generate a plurality of categories associated with the plurality of clinical trial protocol titles or training the criteria machine learning model to generate eligibility criteria. Nor does claim 1 recite a step of retraining the model to make it more accurate as in Example 39.
The newly added training limitations do not alter the fact that the claims as a whole recite an abstract idea. The additional elements of the neural networks used to generate the inference models amount to no more than a general technical environment in which the abstract idea is implemented using a generic, well understood, off the shelf machine learning technique to generate inputs that are then used in the abstract process of comparing, evaluating, and recommending. The claims recitation of the training process does not integrate the abstract idea into a practical application. The claim does not recite any particular improvement to neural network training, any particular technical problem solved by the choice of using two networks rather than one. The additional elements considered individually and in combination do not integrate the judicial exception into a practical application and do not amount to significantly more than the exception itself. An updated rejection can be found below.
The applicant has amended the claims to overcome the previous 112(b), second paragraph rejections. The previous 112(b), second paragraph rejections are withdrawn.
With regards to the previous 102/103 rejections, the applicant has amended the claims to include additional limitations that required further search and consideration. An updated rejection can be found below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 8, 10-23 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-13 are directed to a method, claims 14-17 are directed to a non-transitory machine-readable medium, and claims 18-23 are directed to a system. Therefore, claims 1-5, 8, 10-23 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 14, and 18 recite managing contracts, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claim 1 recites abstract limitations including “generates a set of demand predictions for a product, the set of demand predictions including a sub-demand for multiple internal consumers of a single company;…generates a set of supply predictions to predict supply of the product; obtaining, using a set of demand predictions generated by a first … model, an aggregated demand prediction, the aggregated demand prediction being intended to predict demand for the product by the multiple internal consumers of the single company over a duration of time; comparing the aggregated demand prediction to an aggregated supply prediction, the aggregated supply prediction being based on a set of supply predictions generated by a second … model and being intended to predict supply of the products over the duration of time to obtain a difference; making a determination, using at least a portion of the difference and acceptability criteria, regarding whether the difference is deemed to be acceptable based on the acceptability criteria, the acceptability criteria including an uncertainty value as a threshold; in a first instance of the determination in which the difference is not deemed to be acceptable based on the acceptability criteria: generating an options clause using a rule set that is based on a risk tolerance of the single company, the risk tolerance corresponding to a percentage of the uncertainty value; recommending addition of an options clause to a contract of the contracts with a supplier of the products, the options clause indicating a quantity of the products to be provided by the supplier when the options clause is exercised, the quantity of product being an amount needed to hedge against uncertainty to reduce a likelihood of product supply not meeting product demand; and in a second instance of the determination in which the difference is deemed to be acceptable based on the acceptability criteria: recommending completion of the contract without the addition of the options clause.” Claim 14 recites abstract limitations including “generates a set of demand predictions for a product, the set of demand predictions including a sub-demand for multiple internal consumers of a single company;… generates a set of supply predictions to predict supply of the product; obtaining, using a set of demand predictions generated by a first … model, an aggregated demand prediction, the aggregated demand prediction being intended to predict demand for the product by the multiple internal consumers of the single company over a duration of time; comparing the aggregated demand prediction to an aggregated supply prediction, the aggregated supply prediction being based on a set of supply predictions generated by a second …model and being intended to predict supply of the products over the duration of time to obtain a difference; making a determination, using at least a portion of the difference and acceptability criteria, regarding whether the difference is deemed to be acceptable based on the acceptability criteria, the acceptability criteria including an uncertainty value as a threshold; in a first instance of the determination in which the difference is not deemed to be acceptable based on the acceptability criteria: generating an options clause using a rule set that is based on a risk tolerance of the single company, the risk tolerance corresponding to a percentage of the uncertainty value; recommending addition of an options clause to a contract of the contracts with a supplier of the products, the options clause indicating a quantity of the product to be provided by the supplier when the options clause is exercised, the quantity of product being an amount needed to hedge against uncertainty to reduce a likelihood of product supply not meeting product demand; and in a second instance of the determination in which the difference is deemed to be acceptable based on the acceptability criteria: recommending completion of the contract without the addition of the options clause.” Claim 18 recites abstract limitations including “generates a set of demand predictions for a product, the set of demand predictions including a sub-demand for multiple internal consumers of a single company… generates a set of supply predictions to predict supply of the product; obtaining, using a set of demand predictions generated by a first … model, an aggregated demand prediction, the aggregated demand prediction being intended to predict demand for the product by the multiple internal consumers of the single company over a duration of time; comparing the aggregated demand prediction to an aggregated supply prediction, the aggregated supply prediction being based on a set of supply predictions generated by a second… model and being intended to predict supply of the products over the duration of time to obtain a difference; making a determination, using at least a portion of the difference and acceptability criteria, regarding whether the difference is deemed to be acceptable based on the acceptability criteria, the acceptability criteria including an uncertainty value as a threshold; in a first instance of the determination in which the difference is not deemed to be acceptable based on the acceptability criteria: generating an options clause using a rule set that is based on a risk tolerance of the single company, the risk tolerance corresponding to a percentage of the uncertainty value; recommending addition of an options clause to a contract of the contracts with a supplier of the products, the options clause indicating a quantity of the products to be provided by the supplier when the options clause is exercised, the quantity of product being an amount needed to hedge against uncertainty to reduce a likelihood of product supply not meeting product demand; and in a second instance of the determination in which the difference is deemed to be acceptable based on the acceptability criteria: recommending completion of the contract without the addition of the options clause.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “a processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “a processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “a processor”/ generic technology language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 3, 5, 10-13, 16, 20, 22, 23, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 2, 4, 15, 17, 19, 21, will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 14, and 18 do not integrate the judicial exception into a practical application. Claim 1 is a method comprising “training, using first training data, a first neural network to obtain a first inference model … training, using second training data, a second neural network to obtain a second inference model …a first inference model, a second inference model.” Claim 14 is “A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing contracts, the operations comprising: training, using first training data, a first neural network to obtain a first inference model … training, using second training data, a second neural network to obtain a second inference model…a first inference model… a second inference model.” Claim 18 is “A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing contracts, the operations comprising:… training, using first training data, a first neural network to obtain a first inference model …training, using second training data, a second neural network to obtain a second inference model …a first inference model… a second inference model.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3, 5-7, 10-13, 16, 20, 22, 23, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 2, 4, 15, 17, 19, 21, include the additional element of an “inference model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 14, and 18 do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a method comprising “training, using first training data, a first neural network to obtain a first inference model … training, using second training data, a second neural network to obtain a second inference model …a first inference model, a second inference model.” Claim 14 is “A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing contracts, the operations comprising: training, using first training data, a first neural network to obtain a first inference model … training, using second training data, a second neural network to obtain a second inference model…a first inference model… a second inference model.” Claim 18 is “A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing contracts, the operations comprising:… training, using first training data, a first neural network to obtain a first inference model …training, using second training data, a second neural network to obtain a second inference model …a first inference model… a second inference model.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 3, 5, 10-13, 16, 20, 22, 23 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 2, 4, 15, 17, 19, 21 include the additional element of an “inference model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-5, 8, 10-23 are rejected under 35 USC 101.
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.
Claim(s) 1-5, 8, 10-12, 14-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moore (US 20240289888 A9) in view of Agarwal et al. (US 20030101107 A1) in view of Mathews et al. (US 20050273379 A1) in view of Sharma et al. (US 20230351322 A1).
Regarding claim 1, Moore discloses a method of managing contracts, the method comprising:
training, using first training data, a first neural network to obtain a first inference model that generates a set of demand predictions for a product, the set of demand predictions including (¶ 58-61, 81, discloses a training machine learning engine using model training datasets to train and predict a volume of demand for supply chain insurance contracts that cover a particular product or group of similar products);
training, using second training data, a second network to obtain a second inference model that generates a set of supply predictions to predict supply of the product (¶ 70-73, 106-108, discloses training a machine learning engine to implement a volume module using training data comprising terms of existing supply chain agreements and information associated with supplier production capabilities and inventory data. The volume module being trained to determine appropriate min/max quantities of product based on supply chain data analysis. There is a distinct second training data set. ¶ 60);
obtaining, using a set of demand predictions generated by a first inference model, an aggregated demand prediction, the aggregated demand prediction being intended to predict demand for products over a duration of time (¶ 34, discloses a demand module that predicts a volume of demand. ¶ 80-82, discloses predicting a volume of demand for a supply chain. ¶ 7-9, 22, disclose a contract terms and times. ¶ 65, 93, 110, 22);
comparing the aggregated demand prediction to an aggregated supply prediction, the aggregated supply prediction being based on a set of supply predictions generated by a second inference model and being intended to predict supply of the products over the duration of time to obtain a difference (¶ 34, 73, discloses comparing the aggregated demand to an aggregated supply prediction. ¶ 80-82, discloses predicting volume over demand over. ¶ 110, 118, 22);
making a determination, using at least a portion of the difference and acceptability criteria, regarding whether the difference is deemed to be acceptable based on the acceptability criteria (¶ 65-68, discloses detected anomalies such as shortages. ¶ 69, 86, 89, discloses coming up with agreeable volume terms of a contract. ¶ 22, 76, 31);
in a first instance of the determination in which the difference is not deemed to be acceptable based on the acceptability criteria: generating an options clause using a rule set that is based on a risk tolerance of the single company (¶ 22, 61, discloses the supply chain insurance contracts are structured as option contracts where a buyer has an option to Purace a quantity of a certain product and have it delivered within a certain time frame. The SCI server generate these option contract terms using a pricing contract term module based on risk criteria.)
and recommending addition of the options clause to a contract of the contracts with a supplier of the product, the options clause indicating a quantity of the product to be provided by the supplier when the options clause is exercised, the quantity of product being an amount needed to hedge against uncertainty to reduce a likelihood of product supply not meeting product demand (¶ 22-25, discloses the use of an options clause to an options contracts. ¶ 70-72, discloses executing a contract option. The volume module generates a volume term included in a supply chain insurance contract where the volume term specifies a minimum and/ or maximum quantity of product and supplies agrees to supply if the option is exercised. ¶ 82, discloses desired but not realized options. ¶ 5, 87, 89, 108-110, 53);
and in a second instance of the determination in which the difference is deemed to be acceptable based on the acceptability criteria: recommending completion of the contract without the addition of the options clause (¶ 53, discloses contracts that do not have an options clause. ¶ 66, 82, 89, 108-110).
Moore does not specifically teach a sub-demand for multiple internal consumers of a single company.
However, Agarwal discloses a sub-demand for multiple internal consumers of a single company (¶ 18, 38, discloses combining demand requirements across multiple locations within a company. ¶ 27, discloses an internal relationship at a company over multiple warehouses.)
Moore discloses a time frame (¶ 22) but does not disclose the product by the multiple internal consumers of the single company.
Agarwal discloses the product by the multiple internal consumers of the single company (¶ 27, 39-40, discloses an aggregation performed across internal consumers/locations of a single company.) Agarwal also discloses a single company (¶ 27, 39-40, discloses an aggregation performed across internal consumers/locations of a single company.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform a sub-demand for multiple internal consumers of a single company, as taught/suggested by Agarwal. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to inventory management. One of ordinary skill in the art would have recognized that applying the known technique of Agarwal would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Agarwal to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sub-demand features into similar systems. Further, applying a sub-demand for multiple internal consumers of a single company would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for internal specific data points.
Moore does not specifically teach the acceptability criteria including an uncertainty value as a threshold or the risk tolerance corresponding to a percentage of the uncertainty value.
However, Mathews teaches the acceptability criteria including an uncertainty value as a threshold (¶ 53-61, discloses defining an uncertainty parameter for each time segment of a forecast, which is incorporated into the standard deviation of the price/demand distribution and used to determine whether modeled outcomes fall within a threshold. Fig. 22). Mathews discloses modeling both future demand value and future supply for a good as distributions and comparing the two by plotting them in Fig. 9a-9b, the difference can be seen between the modeled demand and supply.
Mathews also teaches the risk tolerance corresponding to a percentage of the uncertainty value (¶ 54-55, defining risk as a calculated linear function of a return/growth value and tabulating discrete uncertainty values as percentages associated with specific growth rate return values. See Table 1).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform the acceptability criteria including an uncertainty value, as taught/suggested by Mathews. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Mathews would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mathews to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying the acceptability criteria including an uncertainty value would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user a likelihood determination that could be limited and benchmarked and part of a risk based rule set.
The combination does not specifically teach a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply.
However, Sharma teaches wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply (¶ 4, 144-145, discloses predicting product supply and demand, ¶ 15-18, discloses predicting supply chain data. ¶ 47-48, discloses predicting supply and demand data using AI based algorithms. ¶ 70-75, disclose supply chain and demand forecast data. ¶ 79-82, disclose a neural network, ¶ 104-106).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply, as taught/suggested by Sharma. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to supply chain management. One of ordinary skill in the art would have recognized that applying the known technique of Sharma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Sharma to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such neural network features into similar systems. Further, applying wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional data to help the supplier the ability to meet the forecasted demand.
Regarding claims 2, 15, 19, Moore discloses
obtaining demand data (¶ 27, 29, 52, 63, obtaining supply chain data which includes demand data. ¶ 54, 58, 64-65, 67);
obtaining, using the first inference model and the demand data, the set of demand predictions (¶ 73, discloses predicting delivering a demand. ¶ 86, discloses transaction modeling which includes predicting agreeable terms. ¶ 92, 111);
and aggregating the set of demand predictions to obtain the aggregated demand prediction (¶ 34, discloses predicting the overall volume of demand. ¶ 80-82, disclose collecting demand data to predict demand. ¶ 86).
Regarding claims 3, 16, 20, Moore teaches wherein the set of demand predictions is stored as a list that specifies internal consumers and sub-demand for each of the internal consumers (¶ 80-81, discloses a list of buyers for specific products and contracts. ¶ 64, 34).
and the aggregate demand prediction comprises: a sum of the sub-demand for each of the internal consumers (¶ 34, 80-82, discloses a volume of demand for the buyers. ¶ 52, 111).
Moore does not specifically teach multiple internal customers.
Agarwal discloses each of the internal customers (¶ 27, 39-40, discloses an aggregation performed across internal consumers/locations of a single company.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform multiple internal consumers of a single company, as taught/suggested by Agarwal. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to inventory management. One of ordinary skill in the art would have recognized that applying the known technique of Agarwal would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Agarwal to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sub-demand features into similar systems. Further, applying multiple internal consumers of a single company would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for internal specific data points.
Moore does not specifically teach a level of uncertainty in the sum of the sub-demand for each of the internal consumers.
However, Mathews teaches a level of uncertainty in the sum of the sub-demand for each of the internal consumers (¶ 6, 50-51, 101, 112-113, discloses accounting for uncertainty in view of demand).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform a level of uncertainty in the sum of the sub-demand for each of the internal consumers, as taught/suggested by Mathews. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Mathews would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mathews to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying a level of uncertainty in the sum of the sub-demand for each of the internal consumers would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user an increased flexibility.
Regarding claims 4, 17, 21, More discloses
obtaining supply data (¶ 27, 29, 52, 63, obtaining supply chain data which includes demand data. ¶ 54, 58, 64-65, 67);
obtaining, using the second inference model and the supply data, the set of supply predictions (¶ 73, discloses predicting delivering a demand. ¶ 86, discloses transaction modeling which includes predicting agreeable terms. ¶ 92, 111);
and aggregating the set of supply predictions to obtain the aggregated supply prediction (¶ 34, discloses predicting the overall volume of demand. ¶ 80-82, disclose collecting demand data to predict demand. ¶ 86).
Regarding claim 5, 22, Moore teaches wherein the difference comprises: a quantity of products needed for product supply to meet product demand over the duration of time (¶ 65-68, discloses the feasibility to ramp up to meet demand.).
Moore does not specifically teach a level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product over the duration of time.
However, Mathews teaches a level of uncertainty in the quantity of the product needed for the product supply to meet the product demand over the duration of time (¶ 6, 50-51, 101-103, discloses accounting for uncertainty of products in view of demand. ¶ 112-113, Fig. 22).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform a level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product over the duration of time, as taught/suggested by Mathews. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Mathews would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mathews to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying a level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product over the duration of time would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user more accurate data points.
Regarding claim 8, 23, Moore teaches wherein the rule set for options clause generation comprises rules keyed (¶ 25, 82, disclose options contracts rules).
Moore does not specifically teach a level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product over the duration of time.
However, Mathews teaches the level of the uncertainty in the quantity of the products needed for the product supply to meet the product demand over the duration of time (¶ 6, 50-51, 101-103, discloses accounting for uncertainty of products in view of demand. ¶ 112-113).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform a level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product over the duration of time, as taught/suggested by Mathews. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Mathews would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mathews to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying a level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product over the duration of time would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user more accurate data points.
Regarding claim 10, Moore teaches a sum of the sub-supply for each of the suppliers (¶ 4, discloses an amount of supplier supplies. ¶ 53, discloses supplier quantities of a product. ¶ 27, 41, 64, 68).
Moore does not specifically teach a level of uncertainty in the sum of the sub-supply for each of the suppliers.
However, Mathews teaches a level of uncertainty in the sum of the sub-supply for each of the suppliers (¶ 6, 50-51, 101-103, discloses accounting for uncertainty of products in view of supply. ¶ 75, 112-113).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform a level of uncertainty in the sum of the sub-supply for each of the suppliers, as taught/suggested by Mathews. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Mathews would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mathews to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying a level of uncertainty in the sum of the sub-supply for each of the suppliers would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user more accurate data points.
Regarding claim 11, Moore teaches wherein the demand data comprises at least one type of data selected from a group consisting of: historical data regarding demand for the product; and historical data regarding consumer spending (¶ 111, discloses the use of historical demand data. ¶ 27, 51, 72, discloses the use of historical transaction price data. ¶ 64, 81, discloses purchasing behavior).
Regarding claim 12, Moore teaches wherein the supply data comprises at least one type of data selected from a group consisting of: historical data regarding market availability of the product; historical data regarding supply of the product from a supplier of the suppliers; and historical data regarding a likelihood of contract fulfillment by the supplier of the suppliers (¶ 111, discloses the use of historical demand data. ¶ 27, 51, 72, discloses the use of historical transaction price data. ¶ 64, 81, discloses purchasing behavior).
Regarding claim 14, Moore discloses a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing contracts (¶ 8, 94-99, 100, 112-114);
training, using first training data, a first neural network to obtain a first inference model that generates a set of demand predictions for a product, the set of demand predictions including (¶ 58-61, 81, discloses a training machine learning engine using model training datasets to train and predict a volume of demand for supply chain insurance contracts that cover a particular product or group of similar products);
training, using second training data, a second network to obtain a second inference model that generates a set of supply predictions to predict supply of the product (¶ 70-73, 106-108, discloses training a machine learning engine to implement a volume module using training data comprising terms of existing supply chain agreements and information associated with supplier production capabilities and inventory data. The volume module being trained to determine appropriate min/max quantities of product based on supply chain data analysis. There is a distinct second training data set. ¶ 60);
obtaining, using a set of demand predictions generated by a first inference model, an aggregated demand prediction, the aggregated demand prediction being intended to predict demand for products over a duration of time (¶ 34, discloses a demand module that predicts a volume of demand. ¶ 80-82, discloses predicting a volume of demand for a supply chain. ¶ 7-9, 22, disclose a contract terms and times. ¶ 65, 93, 110, 22);
comparing the aggregated demand prediction to an aggregated supply prediction, the aggregated supply prediction being based on a set of supply predictions generated by a second inference model and being intended to predict supply of the products over the duration of time to obtain a difference (¶ 34, 73, discloses comparing the aggregated demand to an aggregated supply prediction. ¶ 80-82, discloses predicting volume over demand over. ¶ 110, 118, 22);
making a determination, using at least a portion of the difference and acceptability criteria, regarding whether the difference is deemed to be acceptable based on the acceptability criteria (¶ 65-68, discloses detected anomalies such as shortages. ¶ 69, 86, 89, discloses coming up with agreeable volume terms of a contract. ¶ 22, 76, 31);
in a first instance of the determination in which the difference is not deemed to be acceptable based on the acceptability criteria: generating an options clause using a rule set that is based on a risk tolerance of the single company (¶ 22, 61, discloses the supply chain insurance contracts are structured as option contracts where a buyer has an option to Purace a quantity of a certain product and have it delivered within a certain time frame. The SCI server generate these option contract terms using a pricing contract term module based on risk criteria.)
and recommending addition of the options clause to a contract of the contracts with a supplier of the product, the options clause indicating a quantity of the product to be provided by the supplier when the options clause is exercised, the quantity of product being an amount needed to hedge against uncertainty to reduce a likelihood of product supply not meeting product demand (¶ 22-25, discloses the use of an options clause to an options contracts. ¶ 70-72, discloses executing a contract option. The volume module generates a volume term included in a supply chain insurance contract where the volume term specifies a minimum and/ or maximum quantity of product and supplies agrees to supply if the option is exercised. ¶ 82, discloses desired but not realized options. ¶ 5, 87, 89, 108-110, 53);
and in a second instance of the determination in which the difference is deemed to be acceptable based on the acceptability criteria: recommending completion of the contract without the addition of the options clause (¶ 53, discloses contracts that do not have an options clause. ¶ 66, 82, 89, 108-110).
Moore does not specifically teach a sub-demand for multiple internal consumers of a single company.
However, Agarwal discloses a sub-demand for multiple internal consumers of a single company (¶ 18, 38, discloses combining demand requirements across multiple locations within a company. ¶ 27, discloses an internal relationship at a company over multiple warehouses.)
Moore discloses a time frame (¶ 22) but does not disclose the product by the multiple internal consumers of the single company.
Agarwal discloses the product by the multiple internal consumers of the single company (¶ 27, 39-40, discloses an aggregation performed across internal consumers/locations of a single company.) Agarwal also discloses a single company (¶ 27, 39-40, discloses an aggregation performed across internal consumers/locations of a single company.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform a sub-demand for multiple internal consumers of a single company, as taught/suggested by Agarwal. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to inventory management. One of ordinary skill in the art would have recognized that applying the known technique of Agarwal would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Agarwal to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sub-demand features into similar systems. Further, applying a sub-demand for multiple internal consumers of a single company would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for internal specific data points.
Moore does not specifically teach the acceptability criteria including an uncertainty value as a threshold or the risk tolerance corresponding to a percentage of the uncertainty value.
However, Mathews teaches the acceptability criteria including an uncertainty value as a threshold (¶ 53-61, discloses defining an uncertainty parameter for each time segment of a forecast, which is incorporated into the standard deviation of the price/demand distribution and used to determine whether modeled outcomes fall within a threshold. Fig. 22). Mathews discloses modeling both future demand value and future supply for a good as distributions and comparing the two by plotting them in Fig. 9a-9b, the difference can be seen between the modeled demand and supply.
Mathews also teaches the risk tolerance corresponding to a percentage of the uncertainty value (¶ 54-55, defining risk as a calculated linear function of a return/growth value and tabulating discrete uncertainty values as percentages associated with specific growth rate return values. See Table 1).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform the acceptability criteria including an uncertainty value, as taught/suggested by Mathews. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Mathews would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mathews to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying the acceptability criteria including an uncertainty value would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user a likelihood determination that could be limited and benchmarked and part of a risk based rule set.
The combination does not specifically teach a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply.
However, Sharma teaches wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply (¶ 4, 144-145, discloses predicting product supply and demand, ¶ 15-18, discloses predicting supply chain data. ¶ 47-48, discloses predicting supply and demand data using AI based algorithms. ¶ 70-75, disclose supply chain and demand forecast data. ¶ 79-82, disclose a neural network, ¶ 104-106).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply, as taught/suggested by Sharma. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to supply chain management. One of ordinary skill in the art would have recognized that applying the known technique of Sharma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Sharma to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such neural network features into similar systems. Further, applying wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional data to help the supplier the ability to meet the forecasted demand.
Regarding claim 18, Moore a data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing contracts (¶ 8, 94-99, 100, 112-114);
training, using first training data, a first neural network to obtain a first inference model that generates a set of demand predictions for a product, the set of demand predictions including (¶ 58-61, 81, discloses a training machine learning engine using model training datasets to train and predict a volume of demand for supply chain insurance contracts that cover a particular product or group of similar products);
training, using second training data, a second network to obtain a second inference model that generates a set of supply predictions to predict supply of the product (¶ 70-73, 106-108, discloses training a machine learning engine to implement a volume module using training data comprising terms of existing supply chain agreements and information associated with supplier production capabilities and inventory data. The volume module being trained to determine appropriate min/max quantities of product based on supply chain data analysis. There is a distinct second training data set. ¶ 60);
obtaining, using a set of demand predictions generated by a first inference model, an aggregated demand prediction, the aggregated demand prediction being intended to predict demand for products over a duration of time (¶ 34, discloses a demand module that predicts a volume of demand. ¶ 80-82, discloses predicting a volume of demand for a supply chain. ¶ 7-9, 22, disclose a contract terms and times. ¶ 65, 93, 110, 22);
comparing the aggregated demand prediction to an aggregated supply prediction, the aggregated supply prediction being based on a set of supply predictions generated by a second inference model and being intended to predict supply of the products over the duration of time to obtain a difference (¶ 34, 73, discloses comparing the aggregated demand to an aggregated supply prediction. ¶ 80-82, discloses predicting volume over demand over. ¶ 110, 118, 22);
making a determination, using at least a portion of the difference and acceptability criteria, regarding whether the difference is deemed to be acceptable based on the acceptability criteria (¶ 65-68, discloses detected anomalies such as shortages. ¶ 69, 86, 89, discloses coming up with agreeable volume terms of a contract. ¶ 22, 76, 31);
in a first instance of the determination in which the difference is not deemed to be acceptable based on the acceptability criteria: generating an options clause using a rule set that is based on a risk tolerance of the single company (¶ 22, 61, discloses the supply chain insurance contracts are structured as option contracts where a buyer has an option to Purace a quantity of a certain product and have it delivered within a certain time frame. The SCI server generate these option contract terms using a pricing contract term module based on risk criteria.)
and recommending addition of the options clause to a contract of the contracts with a supplier of the product, the options clause indicating a quantity of the product to be provided by the supplier when the options clause is exercised, the quantity of product being an amount needed to hedge against uncertainty to reduce a likelihood of product supply not meeting product demand (¶ 22-25, discloses the use of an options clause to an options contracts. ¶ 70-72, discloses executing a contract option. The volume module generates a volume term included in a supply chain insurance contract where the volume term specifies a minimum and/ or maximum quantity of product and supplies agrees to supply if the option is exercised. ¶ 82, discloses desired but not realized options. ¶ 5, 87, 89, 108-110, 53);
and in a second instance of the determination in which the difference is deemed to be acceptable based on the acceptability criteria: recommending completion of the contract without the addition of the options clause (¶ 53, discloses contracts that do not have an options clause. ¶ 66, 82, 89, 108-110).
Moore does not specifically teach a sub-demand for multiple internal consumers of a single company.
However, Agarwal discloses a sub-demand for multiple internal consumers of a single company (¶ 18, 38, discloses combining demand requirements across multiple locations within a company. ¶ 27, discloses an internal relationship at a company over multiple warehouses.)
Moore discloses a time frame (¶ 22) but does not disclose the product by the multiple internal consumers of the single company.
Agarwal discloses the product by the multiple internal consumers of the single company (¶ 27, 39-40, discloses an aggregation performed across internal consumers/locations of a single company.) Agarwal also discloses a single company (¶ 27, 39-40, discloses an aggregation performed across internal consumers/locations of a single company.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform a sub-demand for multiple internal consumers of a single company, as taught/suggested by Agarwal. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to inventory management. One of ordinary skill in the art would have recognized that applying the known technique of Agarwal would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Agarwal to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sub-demand features into similar systems. Further, applying a sub-demand for multiple internal consumers of a single company would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for internal specific data points.
Moore does not specifically teach the acceptability criteria including an uncertainty value as a threshold or the risk tolerance corresponding to a percentage of the uncertainty value.
However, Mathews teaches the acceptability criteria including an uncertainty value as a threshold (¶ 53-61, discloses defining an uncertainty parameter for each time segment of a forecast, which is incorporated into the standard deviation of the price/demand distribution and used to determine whether modeled outcomes fall within a threshold. Fig. 22). Mathews discloses modeling both future demand value and future supply for a good as distributions and comparing the two by plotting them in Fig. 9a-9b, the difference can be seen between the modeled demand and supply.
Mathews also teaches the risk tolerance corresponding to a percentage of the uncertainty value (¶ 54-55, defining risk as a calculated linear function of a return/growth value and tabulating discrete uncertainty values as percentages associated with specific growth rate return values. See Table 1).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform the acceptability criteria including an uncertainty value, as taught/suggested by Mathews. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Mathews would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mathews to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying the acceptability criteria including an uncertainty value would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user a likelihood determination that could be limited and benchmarked and part of a risk based rule set.
The combination does not specifically teach a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply.
However, Sharma teaches wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply (¶ 4, 144-145, discloses predicting product supply and demand, ¶ 15-18, discloses predicting supply chain data. ¶ 47-48, discloses predicting supply and demand data using AI based algorithms. ¶ 70-75, disclose supply chain and demand forecast data. ¶ 79-82, disclose a neural network, ¶ 104-106).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply, as taught/suggested by Sharma. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to supply chain management. One of ordinary skill in the art would have recognized that applying the known technique of Sharma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Sharma to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such neural network features into similar systems. Further, applying wherein the first inference model is a neural network trained using first training data to predict product demand, and the second inference model is a neural network trained using second training data to predict product supply would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional data to help the supplier the ability to meet the forecasted demand.
Claim(s) 13, is/are rejected under 35 U.S.C. 103 as being unpatentable over Moore (US 20240289888 A9) in view of Agarwal et al. (US 20030101107 A1) in view of Mathews et al. (US 20050273379 A1) in view of Sharma et al. (US 20230351322 A1) in further view of Kakouros et al. (US 7249068 B1).
Regarding claim 13, Moore teaches a quantity of product (¶ 4, discloses an amount of supplier supplies. ¶ 53, discloses supplier quantities of a product. ¶ 27, 41, 64, 68).
Mathews teaches a level of uncertainty in the quantity of the product needed for the product supply to meet the product demand (¶ 6, 50-51, 101-103, discloses accounting for uncertainty of products in view of supply. ¶ 75, 112-113).
The combination does not specifically teach the quantity of products needed to hedge against the uncertainty to reduce a likelihood of the quantity of products not meeting the product demand.
However, Kakouros teaches wherein an increase in the level of uncertainty in the quantity of products needed for the product supply to meet the product demand indicates an increase in a probability that a quantity of the product provided by the supplier according to the contract will not be sufficient to allow product supply to meet the product demand (col. 2, line 22- col. 3, line 13, col. 5, lines 5-42, disclose a probability that a quantity of products would not meet product demand. Col. 11, lines 24-40).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Moore to include/perform wherein an increase in the level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product indicates an increase in a probability that a quantity of the products provided by the supplier according to the contract will not be sufficient to allow product supply to meet product demand, as taught/suggested by Kakouros. This known technique is applicable to the system of Moore as they both share characteristics and capabilities, namely, they are directed to modeling future demand. One of ordinary skill in the art would have recognized that applying the known technique of Kakouros would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Kakouros to the teachings of Moore would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such uncertainty features into similar systems. Further, applying wherein an increase in the level of uncertainty in the quantity of products needed for the supply of the product to meet the demand for the product indicates an increase in a probability that a quantity of the products provided by the supplier according to the contract will not be sufficient to allow product supply to meet product demand would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional data to allow the supplier to meet the forecasted demand.
Other pertinent prior art includes Hunn et al. (US 20180315141 A1) which discloses making contract related data accessible to programmable clauses. Beltran-Guerrero et al. (US 20220051318 A1) which discloses constructing micro-contracts to match suppliers with project operators, anticipating their demand needs. Al Katheer et al. (US 20250061527 A1) which discloses determining predicted contract data for the contract using a machine-learning model, the supply data, and the historical contract data. Flunkert et al. (US 10936947 B1) discloses demand forecasting using recurrent neural network models are described.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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