DETAILED ACTION
This is a Non-Final Office Action in response to Claims on 12/19/2024. Claims 1-15, and 17-20 are pending. The effective filing date is 08/11/2021.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/26/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/21/2022 and 01/30/2023 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1- Claims 1-15, 17 and 18 are a method, which are patent eligible material. Claims 19-20 are a system, and are patent eligible material. Claims 1-15, and 17-20 pass step 1.
Step 2A, Prong 1-The independent claim 1, and similarly claim 19, recites;
receiving customer data related to a plurality of customer segments and a corresponding demand from each customer segment, wherein each customer segment includes a specified target service level (receiving information is collecting information, which is a limitation that can be performed practically in the human mind, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); additionally the information that is being collected relates to specific customers and their demand for a specific service level, which is how a customer relates to a business and is a business relation under the category of a commercial interaction, see MPEP 2106.05(a)(2)(II)(B) An example of a claim reciting business relations is found in Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017). The business relation at issue in Credit Acceptance is the relationship between a customer and dealer when processing a credit application to purchase a vehicle. The patentee claimed a "system for maintaining a database of information about the items in a dealer’s inventory, obtaining financial information about a customer from a user, combining these two sources of information to create a financing package for each of the inventoried items, and presenting the financing packages to the user." 859 F.3d at 1054, 123 USPQ2d at 1108.);
receiving material data related to a plurality of distribution centres corresponding to an amount of materials on stock in each distribution centre of the plurality of distribution centres, wherein at least one distribution centre comprises a main distribution centre supplying one or more other distribution centres of the plurality of distribution centres (receiving data is collecting information, which can be a mental process, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); additionally the information being received is stock information, the stock of a business is how a business is able to manage sales activities, see MPEP 2106.05(a)(2)(II)(B) Other examples of subject matter where the commercial or legal interaction is advertising, marketing or sales activities or behaviors include: i. structuring a sales force or marketing company, which pertains to marketing or sales activities or behaviors, In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009));
calculating, based on the customer data and the material data, a safety stock curve in a time-phased manner to cover an uncertainty until a demand is fulfilled based on the customer data and the material data, wherein the time until demand is fulfilled is defined as a demand fulfilment time, and wherein the safety stock curve is calculated as a function of the demand fulfilment time in order to meet the specified target service level for each customer segment and to minimize inventory levels in the plurality of distribution centres (calculating a curve for stock is using a mathematical operation to determine a variable, this is part of the enumerated grouping of mathematical calculation under MEP 2106.04(a)(2)(I)(C) For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. Examples of mathematical calculations recited in a claim include: i. performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018); ii. calculating a number representing an alarm limit value using the mathematical formula ‘‘B1=B0 (1.0–F) + PVL(F)’’, Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978); additionally the curve being calculated relates to inventory stock, and therefore the stock of a business is how a business is able to manage sales activities, see MPEP 2106.05(a)(2)(II)(B) Other examples of subject matter where the commercial or legal interaction is advertising, marketing or sales activities or behaviors include: i. structuring a sales force or marketing company, which pertains to marketing or sales activities or behaviors, In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009); and
outputting orders to the plurality of distribution centres in due time based on the safety stock curve (outputting information is a method of displaying information, and is a limitation that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III)(A) n contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
receiving, by the plurality of distribution centres comprising distribution centres in distinct geographical locations, the orders (receiving data is collecting information, which can be a mental process, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); additionally the information being received is stock information, the stock of a business is how a business is able to manage sales activities, see MPEP 2106.05(a)(2)(II)(B) Other examples of subject matter where the commercial or legal interaction is advertising, marketing or sales activities or behaviors include: i. structuring a sales force or marketing company, which pertains to marketing or sales activities or behaviors, In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009)); and
causing, based on the orders, transporting, manufacturing, assembling, or purchasing of corresponding materials in the plurality of distribution centres (outputting information is a method of displaying information, and is a limitation that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III)(A) n contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
The combination of elements, either alone or in combination, present abstract idea, and fail Step 2A, Prong 1.
Step 2A, Prong 2-The additional element of independent claim 1, and similarly claim 19, include a computer.
This judicial exception is not integrated into a practical application because the combination of the additional elements with the information analysis are not more than using the computer as a tool to implement the abstract idea under MPEP 2106.05(f)(2) Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). The method describes receiving information and completing a calculation to make a business determination, and the process is implemented on a computer. The implementation on a computer does not create integration into a practical application because the computer is only a placeholder title to be used as a tool to implement the abstract idea. Mere instruction of an abstract idea, such as calculating the amount of stock required, being performed on a computer does not provide integration into a practical application.
Therefore, alone or as a combination, claim 1 and similarly claim 19 fail Step 2A, Prong 2.
Step 2B-The independent claim 1, and similarly claim 19, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because whether in combination or individually, the elements do not amount to significantly more than the abstract idea of a business calculation to determine the amount of stock necessary based on predictive data. The application on a computer is not more than the application of an abstract idea on a computer (MPEP 2106.05(f), using a computer to perform the business method, mathematical equations and mental processes described in step 2A, Prong 1).
The additional elements are recited in general terms, and does not provide a particular transformation that would fall under 2106.05(c). The additional elements in Diamond v. Diehr as a whole provided eligibility and did not merely recite calculating a cure time using the Arrhenius equation "in a rubber molding process". Instead, the claim in Diehr recited specific limitations such as monitoring the elapsed time since the mold was closed, constantly measuring the temperature in the mold cavity, repetitively calculating a cure time by inputting the measured temperature into the Arrhenius equation, and opening the press automatically when the calculated cure time and the elapsed time are equivalent. 450 U.S. at 179, 209 USPQ at 5, n. 5. These specific limitations act in concert to transform raw, uncured rubber into cured molded rubber. 450 U.S. at 177-78, 209 USPQ at 4.
Dependent Claims
further describe how a safety stock curve is calculated. Calculation is a mathematical concept, under MPEP 2106.04(a)(2)(I)(C). Using a calculation on a computer, is using that computer as a tool to implement the abstract idea, and fail to integrate the abstract idea into a practical application or provide significant more under MPEP 2106.05(f).
Dependent claim 4, further describe the information used for analysis. Analysis of business information is making a determination about sales activities, and is a commercial interaction under MPEP 2106.04(a)(2)(II)(B). Additional data to be analyzed does not provide integration of sales analysis into a practical application, but rather provides additional information to be applied on the computer presented in the independent claims. Under MPEP 2106.05(f) application of a commercial interaction on a computer is a tool to implement the abstract idea and fails to provide integration into a practical application or provide significant more.
Dependent claim 6, 15 further describes additional business steps after the calculation of desired safety stock amount, and is a commercial interaction under MPEP 2106.04(a)(2)(II)(B). Ordering of stock is part of business practice, and simply stating that an order is placed, does not provide how the abstract idea is more than a method to organize how or when a customer should place another order to restock inventory. Under MPEP 2106.05(f) application of a commercial interaction on a computer is a tool to implement the abstract idea and fails to provide integration into a practical application or provide significant more.
Dependent claim 12-14 further describe calculations of different curves based on inventory. The way in which a curve is calculated is additional details on how the abstract idea of performing analysis of sales behaviors is achieved. Calculation is a mathematical concept, under MPEP 2106.04(a)(2)(I)(C). Under MPEP 2106.05(f) application of a calculation on a computer is a tool to implement the abstract idea and fails to provide integration into a practical application or provide significant more.
Dependent claims 17-18 further describe a machine learning engine to analyze the data. Receiving and training is using data to create a model to perform calculation about stocks. A model using an algorithm is a mathematical concept under MPEP 2106.04(a)(2)(I)(C). A Machine learning engine is additional computer element, but this remains an element that is being used as a tool to implement the abstract idea. The machine learning is named as the processor to receive information and perform calculation, and therefore is the computer tool to perform the abstract idea. Using a more specific name, like machine learning, rather than a generic computer still does not provide technical details of how the abstract idea is integrated into a practical application. This fails to integrate the abstract idea into a practical application or provide significantly more under MPEP 2106.05(f).
Dependent claim 20 describe the abstract method of claim 1 stored on as a computer program product having a computer and data storage means. The exact same abstract ideas discussed with claim 1 apply to claim 20. The additional element of a computer program and data storage is the use of computer elements to implement the abstract idea. Under MPEP 2106.05(f) implementation of an abstract idea stored as a computer program to be implemented on a computer does not provide more than a tool to implement the abstract idea, and therefore remain ineligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-9, 11-15, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2015/0379536 A1 Gopinath et al. ("Gopinath").
Regarding claim 1, Gopinath teaches a computer-implemented method for controlling materials in a supply chain (Gopinath Abstract, method and system to create plan for inventory), the method comprising:
receiving customer data related to a plurality of customer segments and a corresponding demand from each customer segment, wherein each customer segment includes a specified target service level (Gopinath [0027-0028] the point of sale data includes data relating to consumer demands from various stores; Fig. 1A1, the order from Store A, Store B, Kiosk and Online Store showcase a plurality of customer orders, and order correspond to necessary demands for each specific store, and therefore creates different customer segments; [0039] the quantity required to meet the demand of the store is the target service level, since the goal is to predict the demand to have enough stock to met that demand, and not lose on profits by not having enough quantity, or ordering too much leading to waste, see Fig. 1A1 where the point of sale sends information to the distribution centers and in turner order from suppliers; [0093] showcases a more in depth description of how the calculations to determine the target demand based on the amount of supply for a specific target number of days);
receiving material data related to a plurality of distribution centres corresponding to an amount of materials on stock in each distribution centre of the plurality of distribution centres, wherein at least one distribution centre comprises a main distribution centre supplying one or more other distribution centres of the plurality of distribution centres (Gopinath [0037-0039] Fig. 1A1, the distribution centers also include a materials in storage and materials in transit, the distribution channels filter product through a main distribution center (122), which then sends information to suppliers, and the suppliers then send to other storage (114) which can either be delivered to the stores or the distribution center (122) which may also then send to the stores; when a location stores materials, and then distribute to a store or a different location, it is a location that distributes products, even if it is not called a distribution center; the bulk delivery line 116 showcases how the quantity in transit is known and is delivered to the distribution center; [0058] the details of what information is passed between the systems in Fig. 1A1 are show in Fig. 1B1, where the order management system receives information about inventory (quantity) of items within the wholesale and distribution channels);
calculating, based on the customer data and the material data, a safety stock curve in a time-phased manner to cover an uncertainty until a demand is fulfilled based on the customer data and the material data, wherein the time until demand is fulfilled is defined as a demand fulfilment time, and wherein the safety stock curve is calculated as a function of the demand fulfilment time in order to meet the specified target service level for each customer segment and to minimize inventory levels in the plurality of distribution centres (Gopinath [0057] the predictor uses point of sale data to calculate a prediction for inventory demand, and the inventory time period will be set to the desired time period, whether hours, daily or other; it uses time and quantity model to generate a model; Fig. 4A, using an axis to determine the distribution center quantity, planned delivery over time, and any potential jump in inventory needed at a later date to create a safety stock; [0049] safety stock strategy); and
outputting orders to the plurality of distribution centres in due time based on the safety stock curve (Gopinath [0057-0058] the predictor is able to determine if the normal order, or if a supplemental order should be placed to create a safety stock based on the prediction model),
receiving, by the plurality of distribution centres comprising distribution centres in distinct geographical locations, the orders (Gopinath [0037-0039] Fig. 1A1 includes Store A and Store B, indicating two separate stores); and
causing, based on the orders, transporting, manufacturing, assembling, or purchasing of corresponding materials in the plurality of distribution centres (Gopinath [0040-0041] the supply of stock may also include sub-manufacturers).
Regarding claim 2, Gopinath teaches the method according to claim 1, wherein the safety stock curve is calculated as a function of the demand fulfilment time across the supply chain (Gopinath [0081] the demand fulfillment time being one axis and the quantity, or supply being another axis; Fig. 4A).
Regarding claim 3, Gopinath teaches the method according to claim 1, wherein the safety stock curve is calculated as a function of the demand fulfilment time across the supply chain from one or more customers segments of the plurality of customer segments of the main distribution centre (Gopinath [0081] the demand fulfillment time being one axis and the quantity, or supply being another axis; Fig. 4A, there are specific time frames showcased as T1, T2 and T3, this is similar to the customer segments, in that the demand for each particular segment is different; Fig. 1A1 showcases that the point of sale may be multiple stores, and therefore the desired quantity can be based on a variety of stores and their specific needs; a customer segment is the demand required for a specific customer over time, therefore, when multiple stores are able to input their demand as showcased in Fig. 1A1, and the demand is mapped over time in Fig. 4A, the curve is able to show one customer segment of the plurality of customer segments sent to the distribution center, a different curve as showcased in Fig. 4A should be able to be calculated for each different customer).
Regarding claim 4, Gopinath teaches the method according to claim 1, wherein one or more customer segments of the plurality of customer segments includes independent demands (Gopinath [0081] the demand fulfillment time being one axis and the quantity, or supply being another axis; Fig. 4A, there are specific time frames showcased as T1, T2 and T3, this is similar to the customer segments, in that the demand for each particular segment is different; a customer segment is the demand required for a specific customer over time, Fig. 4A is the curve of a specific customer segment that is for their independent demands).
Regarding claim 5, Gopinath teaches the method according to claim 1, wherein the safety stock curve is calculated independent of a specific lead time (Gopinath Fig. 4A, the curve is only based on total time and the quantity needed, and the point which they cross is able to showcase when the potential for loss would need to be addressed).
Regarding claim 7, Gopinath teaches the method according to claim 1, wherein the calculation of the safety stock curve depends on whether the demand can be modelled as a continuous demand (Gopinath [0081] the time periods are continuous, and the curve will continue based on the demand over time).
Regarding claim 8, Gopinath teaches the method according to claim 7, wherein the demand is modelled based on a normal distribution or Gamma distribution (Gopinath [0071] the information may be normalized to create consistent format).
Regarding claim 9, Gopinath teaches the method according to claim 1, wherein the calculation of the safety stock curve depends on whether the demand can be modelled as a discrete demand (Gopinath Fig. 4A, each segment has a specific demand, and during T3 there is a discrete demand and therefore a different peak for stock purchase).
Regarding claim 11, Gopinath teaches the method according to claim 1, wherein the safety stock curve comprises a reorder point curve (Gopinath [0087-0090] a replenishment plan generator used to create a point to reorder inventory; Fig. 4A, shows reorder as stars within the graph).
Regarding claim 12, Gopinath teaches the method according to claim 1, further comprising: calculating a buffer curve for the supply chain, the buffer curve being calculated so that replenishment orders are fixed in time and/or quantity, if a projected stock is positioned between the safety stock curve and the buffer curve (Gopinath [0085-0087] the safety stock curve is calculated, and the safety stock curve is used to calculate whether the estimated stock is either too conservative or too liberal, the conservative line and liberal line are above and below the safety stock curve, creating a zone of reordering that falls between the maximum and minimum re-ordering around the safety stock; Fig. 4B).
Regarding claim 13, Gopinath teaches the method according to claim 1, further comprising: calculating a negative safety stock value for one or more upstream stock points to reduce total safety stock by utilizing a portfolio effect of the downstream demand variation sources (Gopinath [0061] a calendar may be used to superimpose when seasonal items would be in higher demand, downstream, a holiday may be coming up, meaning the need to order seasonal items comes earlier, and if a season has passed, the value for those seasonal items will be reduced, creating a negative safety stock since it will no longer be of use after the seasonal time frame; [0080-0081] the predicted demand is used to determine the amount of restock based on demand, but other factors may also alter the need for restock beyond demand, see factors in [0061]; Fig. 4A).
Regarding claim 14, Gopinath teaches the method according to claim 13, wherein the safety stock in the supply chain increases across possible stock points for storing material until a decoupling stock point independent of a negative safety stock is reached (Gopinath [0051] the stock has a safety stock, but the analysis is done to avoid excessive stock that may go to waste by spoiling before they may be consumed).
Regarding claim 15, Gopinath teaches the method according to claim 1, wherein the order released to a production, supplier or transportation entity when supply constraints exist at the plurality of distribution centres (Gopinath [0037] ordering may be from a supplier through distribution centers; [0040] if a store orders from a supplier, but the supplier does not have the total amount necessary, a distribution center may then handles the excess need, additionally, the distribution center may place order with suppliers if the quantity in the distribution center does not meet the needs of the stores).
Regarding claim 19, Gopinath teaches a computer-implemented planning system for controlling materials in a supply chain on one or more computers (Gopinath Abstract, method and system to create plan for inventory; [0058] the data is analyzed using computers), the system comprising:
a computer configured or adapted to (Gopinath [0058] the data is analyzed using computers):
receive customer data related to a plurality of customer segments and a corresponding demand from each customer segment, wherein each customer segment includes a specified target service level (Gopinath [0027-0028] the point of sale data includes data relating to consumer demands from various stores; Fig. 1A1, the order from Store A, Store B, Kiosk and Online Store showcase a plurality of customer orders, and order correspond to necessary demands for each specific store, and therefore creates different customer segments; [0039] the quantity required to meet the demand of the store is the target service level, since the goal is to predict the demand to have enough stock to met that demand, and not lose on profits by not having enough quantity, or ordering too much leading to waste, see Fig. 1A1 where the point of sale sends information to the distribution centers and in turner order from suppliers; [0093] showcases a more in depth description of how the calculations to determine the target demand based on the amount of supply for a specific target number of days);
receive material data related to a plurality of distribution centres corresponding to an amount of materials on stock in each distribution centre of the plurality of distribution centres, wherein at least one distribution centre comprises a main distribution centre supplying one or more other distribution centres of the plurality of distribution centres (Gopinath [0037-0039] Fig. 1A1, the distribution centers also include a materials in storage and materials in transit, the distribution channels filter product through a main distribution center (122), which then sends information to suppliers, and the suppliers then send to other storage (114) which can either be delivered to the stores or the distribution center (122) which may also then send to the stores; when a location stores materials, and then distribute to a store or a different location, it is a location that distributes products, even if it is not called a distribution center; the bulk delivery line 116 showcases how the quantity in transit is known and is delivered to the distribution center; [0058] the details of what information is passed between the systems in Fig. 1A1 are show in Fig. 1B1, where the order management system receives information about inventory (quantity) of items within the wholesale and distribution channels);
calculate, based on the customer data and the material data, a safety stock curve in a time-phased manner to cover an uncertainty until a demand is fulfilled based on the customer data and the material data, wherein the time until demand is fulfilled is defined as a fulfilment time, and wherein the safety stock curve is calculated as a function of the demand fulfilment time in order to meet the specified target service level for each customer segment and to minimize inventory levels in the plurality of distribution centres (Gopinath [0057] the predictor uses point of sale data to calculate a prediction for inventory demand, and the inventory time period will be set to the desired time period, whether hours, daily or other; it uses time and quantity model to generate a model; Fig. 4A, using an axis to determine the distribution center quantity, planned delivery over time, and any potential jump in inventory needed at a later date to create a safety stock; [0049] safety stock strategy) and
output orders to the plurality of distribution centres in due time based on the safety stock curve (Gopinath [0057-0058] the predictor is able to determine if the normal order, or if a supplemental order should be placed to create a safety stock based on the prediction model);
and a plurality of distribution centres comprising distribution centres in distinct geographical locations, the plurality of distribution centres configured to receive the orders (Gopinath [0037-0039] Fig. 1A1 includes Store A and Store B, indicating two separate stores), and
wherein the system causes, based on the orders, transporting, manufacturing, assembling, or purchasing of corresponding materials in the plurality of distribution centres (Gopinath [0040-0041] the supply of stock may also include sub-manufacturers).
Regarding claim 20, Gopinath teaches a computer program product being adapted to enable a computer system comprising at least one computer having data storage means in connection therewith to control a computer-implemented planning system according to claim 19 (Gopinath [0058] the data is analyzed using computers; [0071] data stored at storage system).
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 6 is rejected under 35 U.S.C. 103 as being unpatentable over Gopinath in view of US 2009/0187468 A1 Krech ("Krech").
Regarding claim 6, Gopinath teaches the method according to claim 1. Gopinath fails to explicitly disclose wherein the order is initiated when a projected stock of material in one or more distribution centres of the plurality of distribution centres is below the safety stock curve. Krech is in the field of inventory management (Krech Abstract, method for inventory management) and teaches wherein the order is initiated when a projected stock of material in one or more distribution centres of the plurality of distribution centres is below the safety stock curve (Krech [0038-0039] automatically reordering when a safety stock dips below the suggested level). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the safety stock analysis of Gopinath with the automatic re-ordering as taught by Krech. The motivation for doing so would be to provide a computer system that would complete transaction based on facts to maximize efficiency, increasing profits (Krech [0002[ using logic maximizes net income; [0026] the amount of products is in excess, and needs logic based re-ordering)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gopinath in view of US 2008/0300844 A1 Bagchi et al. ("Bagchi").
Regarding claim 10, Gopinath teaches the method according to claim 9. Gopinath fails to explicitly disclose wherein the demand is modelled based on Compound Poisson distribution. Bagchi is in the field of estimating deliverables (Bagchi Abstract, estimating performance for delivery) and teaches wherein the demand is modelled based on Compound Poisson distribution (Bagchi [0025] the demand modelled using Poisson process). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the curve calculation of Gopinath with specific model taught in Bagchi. The motivation for doing so would be to use established modeling processes to create a curve based on the desired attributes based on the weights of attributes most important to the users (Bagchi [0025] process to model demands).
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gopinath in view of US 2019/0130425 A1 Lei et al. ("Lei").
Regarding claim 17, Gopinath teaches the method according to claim 1. Gopinath teaches using datasets comprising a plurality of customer data and a plurality of material data, and calculating the safety stock curve (Gopinath [0057] the predictor uses point of sale data to calculate a prediction for inventory demand, and the inventory time period will be set to the desired time period, whether hours, daily or other; it uses time and quantity model to generate a model; Fig. 4A, using an axis to determine the distribution center quantity, planned delivery over time, and any potential jump in inventory needed at a later date to create a safety stock; [0049] safety stock strategy).
Gopinath fails to explicitly disclose further comprising: receiving, by a machine learning engine, a plurality of datasets comprising a plurality of customer data and a plurality of material data; training, by the machine learning engine, a machine learning model according to the plurality of datasets, wherein the calculating the safety stock curve is output from the machine learning model.
Lei is in the field of forecasting demand (Lei Abstract, using period sets to generate a model for demands) and teaches receiving, by a machine learning engine, a plurality of datasets comprising a plurality of customer data and a plurality of material data (Lei [0030] machine learning to analyze data; [0037] using performance and historical sales data as input into machine learning);
training, by the machine learning engine, a machine learning model according to the plurality of datasets, wherein the calculating the safety stock curve is output from the machine learning model (Lei [0041] each new data set is used to train the machine learning model, continuously for nth amount of times). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the computer processing of safety stock found in Gopinath with the use of machine learning to train the processing as taught by Lei. The motivation for doing so would be to provide analysis of the data using a system that is able to be trained and updated on a regular basis for a more accurate analysis (Lei [0014-0015] using machine learning to process data is preferred to users).
Regarding claim 18, modified Gopinath teaches the method according to claim 17. Gopinath teaches receiving an additional one or more datasets comprising customer segments and material data; adjusting the safety stock curve (Gopinath [0057] the predictor uses point of sale data to calculate a prediction for inventory demand, and the inventory time period will be set to the desired time period, whether hours, daily or other; it uses time and quantity model to generate a model; Fig. 4A, using an axis to determine the distribution center quantity, planned delivery over time, and any potential jump in inventory needed at a later date to create a safety stock; [0049] safety stock strategy).
Gopinath fails to explicitly disclose further comprising: receiving, by the machine learning engine, an additional one or more datasets comprising customer segments and material data; and retraining, by the machine learning engine, the machine learning model based on the additional one or more datasets; and adjusting the safety stock curve according to the retrained machine learning model.
Lei teaches receiving, by the machine learning engine, an additional one or more datasets comprising customer segments and material data (Lei [0030] machine learning to analyze data; [0037] using performance and historical sales data as input into machine learning); and
retraining, by the machine learning engine, the machine learning model based on the additional one or more datasets (Lei [0041] each new data set is used to train the machine learning model, continuously for nth amount of times); and
adjusting the safety stock curve according to the retrained machine learning model (Lei [0057] the information is used to vary the safety stock based on updated data).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the computer processing of safety stock found in Gopinath with the use of machine learning to train the processing as taught by Lei. The motivation for doing so would be to provide analysis of the data using a system that is able to be trained and updated on a regular basis for a more accurate analysis (Lei [0014-0015] using machine learning to process data is preferred to users).
Response to Arguments
Applicant's arguments filed 09/26/2025 have been fully considered but they are not persuasive.
Regarding 101, Examiner discussed the amended claim limitation with QAS Kevin Flynn.
The limitation only states “or purchasing” of which the BRI would fall into the abstract idea. This particular level of breadth, specifically only using the language “transporting, manufacturing, or assembling” would meet the “particular transformation” as described in MPEP 2106.05(c).
Regarding transporting: “Changing to a different state or thing usually means more than simply using an article or changing the location of an article.”
There is also this regarding transformation:
“2. The degree to which the recited article is particular. A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception. A transformation that can be specifically identified, or that applies to only particular articles, is more likely to provide significantly more (or integrates a judicial exception into a practical application).” I believe your claim applies to “any and all articles” based on its level of breadth.
Instead, at this level of breadth I think the following would apply:
“Examiners may find it helpful to evaluate other considerations such as the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)), and the field of use and technological environment consideration (see MPEP § 2106.05(h)), when making a determination of whether a claim satisfies the particular transformation consideration.”
Applicant focuses on the improvement to a technology or technical field under MPEP 2106.05(d)(I). The improvement discussed is to a supply chain and inventory logistics, described in the instant specification [0003-0011]. The technical problem is the optimization of a complex inventory control, and the proposed solution is using a calculation, which is a function of different variables, to plan out stock for the future. Examiner notes that MPEP 2106.04(d)(I) describes that the improvement to a technology that must demonstrate that it improves the relevant existing technology (the computer). The additional elements presented in claim 1, is a computer, and everything else being applied are the abstract steps of mathematical concepts, mental processes and commercial interactions. Inventory management is not a technology, it is an abstract idea, the abstract idea of a commercial interaction. Therefore, when the solution presented is more efficient stock management, it is an improvement to an abstract idea, and therefore does not provide integration into a practical application. Under MPEP 2106.05(f) the use of a computer, and especially if the computer is used to improve efficiency, but making the process faster simply by its application on a computer. It fails to integrate the abstract idea into a practical application. Claim 19 follows the same reasoning, and the dependent claims 2-15, 17-18 and 20 do not depend from eligible subject matter.
Regarding 102
Examiner is interpreting the claim limitation “calculating…a safety stock curve in a time-phased manner” to mean that the claimed invention performs a calculation, and the elements used are customer data and material data, and the output is information about safety stock for specific time phases. When Gopinath teaches in [0096] it describes that the stock may be based on days, or lead time, and both days and lead time is a specific timed phase of a restocking lifeline. Therefore, when Gopinath chooses a strategy (an algorithm to calculate the SS) in [0049], and is further specifically in a time phase in [0096]. Therefore Gopinath teaches the limitation of “calculating, based on the customer data and the material data, a safety stock curve in a time-phased manner to cover an uncertainty until a demand is fulfilled based on the customer data and the material data”. The claim limitation does not define what a safety stock curve in a time phased manner is not claimed beyond the elements that are used to create this curve, and therefore when Gopinath teaches the elements, it is able to showcase the necessary materials to perform the curve calculation. The additional references therefore are not needed to teach the claim limitations, and the dependence on claim 1 remain rejected for the previously mentioned reasons. Claim 19 remain rejected for the same reason as described above in regards to claim 1.
Regarding 103
The dependent claims do not depend on an allowable claim, and therefore remain rejected under 103.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2017/0364614 A1 Freeman et al. teaches forecasting demand (Abstract); US 2015/0254589 A1 Saxena et al. teaches stock optimization (Abstract); “Inventory Policies and Safety Stock Optimization for Supply chain Planning” Brunaud et al. teaches suggestions for new supply chain models (Abstract).
Conclusion
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/JESSICA E SULLIVAN/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627