DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 12/29/2025 regarding application 18/321,831. Claims 1, 9 and 17 have been amended. Claims 4, 12 and 20 have been canceled. Claims 24-26 have been added as new claims. Thus, Claims 1-2, 5-10, 13-18 and 21-26 are pending have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 12/29/2025 necessitated new grounds of rejection in this office action.
Response to Arguments
3. Applicant’s arguments, see pages 14-16 of 17, filed on 12/29/2025, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1, 6-9 and 14-17 have been fully considered and are found to be not persuasive. Applicant’s arguments with respect to Claims 1-2, 5-10, 13-18 and 21-26 have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Response to 35 U.S.C. § 101 Arguments
4. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-2, 5-10, 13-18 and 21-26 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 10-13, dated 12/29/2025). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-2, 5-10, 13-18 and 21-26 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 10-11 of 17 dated 12/29/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that Independent Claims 1, 9 and 17 reflect an improvement in the technical field of demand modeling and allocation optimization (see Applicant’s Remarks, Page 10, dated 12/29/2025). Examiner respectfully disagrees.
In response to Applicant’s Remark here, Examiner notes that Applicant asserts that the claims provide an improvement to a technical field or improvement to technology in “demand modeling and allocation optimization” and increased prediction accuracy. This argument is not persuasive due to the following reasons. First, the asserted improvement shown is to a business/economic field and not a technical field. The claimed “demand modeling” and “allocation optimization” relate to: retail planning and inventory management. These are commercial domains and not technological ones. The Federal Circuit has repeatedly held that improvements to business decision-making do not qualify as technological improvements. See SAP America v. InvestPic (Fed. Cir. 2018) & Electric Power Group v. Alstom (Fed. Cir. 2016) court cases. Also, the claimed techniques are mathematical and not technological. The alleged improvements arise from: better mathematical modeling and improved optimization accuracy. However, improving a mathematical model itself is still an abstract idea. SAP America v. InvestPic (Fed. Cir. 2018) noting that “improved statistical analysis” does not equal or does not equate to eligibility. Thirdly, there is no improvement to computer functionality shown. These claims do not improve processing efficiency, memory usage, data structures or distributed computing. Instead, the computer is used as a tool to perform calculations in inventory allocation and demand modeling (see MPEP § 2106.05 (f)). Moreover, these claims as a whole are limited to a particular field of use or technological environment for optimizing inventory assortment and allocation of a group of products, wherein the group of products are allocated from a plurality of different warehouses to a plurality of different retail stores using a computer in an inventory management environment (see MPEP § 2106.05(h)).
Additionally, certain/particular limitations shown in Independent Claims 1, 9 and 17 recite (1) mere data gathering such as (e.g., “receiving historical sales data for the group of substitutable products, wherein a first substitutable product and a second substitutable product in the group are substitutable products when they can be used in place of each other to satisfy a same need”) & (2) updating activity logs such as (e.g., “updating the plurality of decision variables based on a direction of the gradient” & “updating dual lambda variables of the Lagrangian relaxation”) wherein each of these steps reflects insignificant extra-solution activities (see MPEP § 2106.05 (g)). Examiner maintains that Claims 1-2, 5-10, 13-18 and 21-26 as currently recited do not contain additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. 101 analysis.
Argument #2:
(B). Applicant argues that Independent Claims 1, 9 and 17 cannot be practically be performed in the human mind under revised step 2a prong one of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 11-12 of 17 dated 12/29/2025). Examiner respectfully disagrees.
Specifically, Applicant argues in the claim limitations of Independent Claims 1, 9 and 17 that it is simply not reasonable to allege that a human mind can estimate “demand model parameters” and then solve an optimization problem that includes “determining a gradient” & “updating the plurality of decision variables based on a direction of the gradient” & “updating dual lambda variables of the Lagrangian relaxation” (see Applicant Remarks, Pages 11-12 of 17 dated 12/29/2025). Examiner respectfully disagrees.
Applicant argues that the claimed steps are too complex for human performance. This argument misapplies USPTO Guidance. First, complexity does not confer eligibility. The proper inquiry is not whether a human can practically perform the steps, but whether the claims recite concepts that are mathematical or mental in nature. The Federal Circuit has clarified that even highly complex calculations remain abstract. See SAP America v. InvestPic (Fed. Cir. 2018) & CyberSource v. Retail Decisions, Inc. (Fed. Cir. 2011). Moreover, mathematical algorithms remain abstract regardless of scale. These claims in Independent Claims 1, 9 and 17 explicitly recites gradients, iterative updates and Lagrangian relaxation, which are classical mathematical techniques. Their computational intensity does not change their abstract character. Next, August 2025 Memo does not override precedent. While the memo cautions against overextending “mental processes,” it does not state that complex math becomes non-abstract, but rather mathematical concepts remain a separate category of abstract ideas. The claims remain directed to abstract mathematical concepts regardless of computational complexity.
For step 2a prong 1, the claims in Independent Claims 1, 9 and 17 recite collecting and grouping sales data, estimating demand model parameters (seasonality, price sensitivity and cannibalization), solving an optimization problem using: objective functions, gradients and Lagrangian relaxation, iteratively updating variables and producing an optimal inventory allocation and assortment. These limitations fall squarely within the “Mathematical Concepts” Grouping that consists of demand modeling, gradient-based optimization, Lagrangian relaxation and iterative numerical techniques. These are mathematical relationships or mathematical calculations within the Mathematical Concepts Grouping. With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). Regarding the “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (A): “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.” Organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form.
Secondly, these claims in Independent Claims 1, 9 and 17 also recite “Certain Methods of Organizing Human Activities” Grouping that consists of inventory allocation, product assortment decisions and supply chain distributions. These are commercial interactions involve commercial interactions (including sales activities or behaviors) or fundamental economic principles or practices or managing personal behavior (including following rules or instructions or teachings). According to MPEP § 2106.04 (a) (2): “The sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may fall within the "certain methods of organizing human activity" grouping.” It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.
Additionally, or alternatively, these steps in Independent Claims 1, 9 and 17 also fall within the “Mental Processes” Grouping which are concepts that can be performed in the human mind (including evaluations or judgments or observations) or using pen to paper as a physical aid. For example; for the Mental Processes grouping, these steps involve evaluating product substitutability and analyzing demand relationships. Examiner also points out that according to MPEP § 2106.04 (a) (2) section III: Mental Processes part (b): A Claim that Encompasses a Human Performing the Step (s) Mentally with or Without a Physical Aid Recites a Mental Process -> “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. Furthermore, from MPEP § 2106.04 (a) (2) section III: Mental Processes part (c): A Claim that Requires a Computer May Still Recite a Mental Process -> Examiner has reviewed Applicant’s Specification and determined that the claim invention describes concepts performed in the human mind and applicant is merely claiming that concepts performed 1) on a generic computer (see at least Applicant’s Specification ¶ [0023]: “Processor 22 may be any type of general or specific purpose processor. System 10 further includes a memory 14 for storing information and instructions to be executed by processor 22.”), 2) in a computer environment (see at least Applicant’s Specification ¶ [0118]: “Architecture 900 permits effective integration between the systems in the operations technology portion and the systems in the information technology portion of the environment. Architecture 900 generally includes a gateway portion 902 having front-end data collection logic, and a server portion 920 to perform back-end processing of the collected data.”) or 3) is merely using a computer as a tool to perform these concepts. Thus, based on these factors, Examiner maintains that the claims still recite a mental process. Therefore, in conclusion, Examiner maintains that Claims 1-2, 5-10, 13-18 and 21-26 are directed to abstract ideas under “Certain Methods of Organizing Human Activities” or “Mental Processes” or “Mathematical Concepts” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #3:
(C). Applicant argues that Dependent Claims 8, 16 and 23 recite the practical application of automatically transporting a group of substitutable products determined by the optimized solution under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 12-13 of 17 dated 12/29/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that the “automatically transporting products based on the optimization solution” recited in Dependent Claims 8, 16 and 23 is analogous to USPTO Examples 45 and 46 (see Applicant Remarks, Pages 12-13 of 17 dated 12/29/2025). Examiner respectfully disagrees. This argument is not persuasive.
In response to Applicant’s remarks here, Examiner points out that “automatic transportation configured to automatically transport the group of substitutable products” occurs after the mathematical optimization is complete. This merely uses the result of the abstract idea and therefore is a post-solution activity, which does not integrate the juridical exception under 35 U.S.C. § 101 step 2a prong 2. Secondly, there is no specific technological implementation shown in Dependent Claims 8, 16 and 23. Unlike Examples 45 and 46, those examples control specific physical systems (molding machine and feed dispenser) with a particularized control logic. Here, for Dependent Claims 8, 16 and 23, the “automatic transportation” is claimed functionally and generically. These claim limitations provide no details regarding robotics, conveyor systems, routing algorithms or control mechanisms to aide or allow for the execution of the “automatic transportation” to occur. Next, Examiner points out that there is no improvement shown to transportation technology. These claims in Dependent Claims 8, 16 and 23 do not improve logistics systems, improve routing efficiency or improve automation hardware. These steps merely apply a computed business decision.
Moreover, the key distinctions from USPTO Examples 45 and 46 were that the control signals improve the operation of a machine. Here, for Dependent Claims 8, 16 and 23 of the instant application, the transportation is simply triggered by the result and there is no transformation in how the system operates. In conclusion, the “automatic transportation” limitation step in Dependent Claims 8, 16 and 23 result in insignificant post-solution activity and does not integrate the judicial exception into a practical application under 35 U.S.C. § 101 step 2a prong 2 of the 35 U.S.C. § 101 analysis.
Argument #4:
(D). Applicant argues that the additional elements recited in Dependent Claims 8, 16 and 23 of (e.g., IoT sensors & protocol conversion) are not conventional under step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 13 of 17 dated 12/29/2025). Examiner respectfully disagrees.
Examiner points out that the “automatic transportation” elements are claimed at a high level of generality and with no specific technical implementation. The “gateway sensor” & “IoT protocols” are not conventional under Berkheimer. For example; Examiner cites US PG Pub (US 2023/0325766 A1) – “Autonomous Exposure-Based Product Replacement System”, hereinafter Cella, et. al. See Cella at ¶ [0493]: “The IoT devices may identify collection for each warehouse and the warehouses may use the IoT devices to communicate with each other. The IoT devices may be configured to process data without using the cloud.” See Cella at ¶ [2555-2556]: “The gateway 14030 communicates with each of the sensor systems 14032, connected products 14034, and additional data sources 14036. The gateway 14030 then communicates directly with the digital product network service 14002. The gateway 14030 hosts the digital product network service 14002.” Moreover, Dependent Claims 8, 16 and 23 are claimed functionally without technical detail. For example; the claim recites receiving messages, communicating with IoT devices and converting protocols, but does not specify how the conversion is performed, any new protocol that is achieved or any improvement in communication efficiency. The generic IoT functionality is conventional. Protocol translation and device communication are standard features in IoT gateways and middleware systems. There is no evidence of non-conventionality in the specification with no indication pointing to novel architecture, improved protocol handling or technical advancement. Lastly, Examiner notes that the claims are merely appending known technology whereby the additional elements recited in Dependent Claims 8, 16 and 23 do not change the nature of the abstract idea and/or simply apply it in a generic environment. Examiner also points out that in the BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018), the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine. At step two we “search for an inventive concept” that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself. But this simply restates what we have already determined is an abstract idea. At Alice step two, it is irrelevant whether considering historical usage information while inputting data may have been non-routine or unconventional as a factual matter. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly more” to it. See SAP Am., Inc. V. InvestPic, LLC, No. 2017-2081, slip op. at 14 (Fed. Cir. Aug. 2, 2018). Therefore, Applicant’s suggestion that a specific limitation (or the claimed invention as a whole) must be shown to be well-understood, routine and conventional to support the conclusion of subject matter ineligibility is not persuasive. For step 2B, therefore the claims do not include additional elements that amount to significantly more than the recited judicial exceptions. These claims merely implement mathematical optimization using known technology in a business context.
Examiner refers Applicant to Examiner’s 35 U.S.C. 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below) shown for step 2B particularly for Independent Claims 1, 9 and 17. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception, (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer, and (3) generally linking the use of the judicial exception to a particular technological environment or field of use.
Independent Claims 1, 9 and 17: The “receiving” steps in Independent Claims 1, 9 and 17 pertain to activities at most amounts to “mere data gathering” and the “updating” steps in Independent Claims 1, 9 and 17 pertain to activities at most amounts to “updating activity logs” wherein each of these steps reflects insignificant extra-solution activities (see MPEP § 2106.05 (g)). Additionally, these activities have been expressly recognized as WURC under step 2B, and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii – Receiving or transmitting data over a network, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See also MPEP § 2106.05(d) ii – Electronic recordkeeping, Alice Corp., 134S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772F.3d at 716, 112 USPQ2d at 1755 (updating an activity log). The “repeating” steps in Independent Claims 1, 9 and 17 pertain to “performing repetitive calculations” and thus these activities have been expressly recognized as WURC under step 2B, and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii – Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425,1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”). Therefore, Claims 1-2, 5-10, 13-18 and 21-26 are maintained as patent ineligible over step 2B of the 35 U.S.C. § 101 analysis. Claims 1-2, 5-10, 13-18 and 21-26 are patent ineligible over 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-2, 5-10, 13-18 and 21-26 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-2, 5-10, 13-18 and 21-26 are each focused to a statutory category namely, a “method” or a “process” (Claims 1-2, 5-8 and 24), an “non-transitory computer readable medium” or an “article of manufacture” (Claims 9-10, 13-16 and 25) and an “apparatus” or a “system” (Claims 17-18, 21-23 and 26).
Step 2A Prong One: Independent Claims 1, 9 and 17 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“ optimize an inventory assortment and allocation of a group of substitutable products, wherein the group of substitutable products are allocated from a plurality of different warehouses to a plurality of different retail stores, the optimizing comprising” (see Independent Claim 9);
“wherein the group of substitutable products are allocated from a plurality of different warehouses to a plurality of different retail stores, performs the optimizing comprising” (see Independent Claim 17);
“wherein the group of substitutable products are allocated from a plurality of different warehouses to a plurality of different retail stores, the method comprising” (see Independent Claim 1);
“receiving historical sales data for the group of substitutable products, wherein a first substitutable product and a second substitutable product in the group are substitutable products when they can be used in place of each other to satisfy a same need” (see Independent Claims 1, 9 and 17);
“estimating demand model parameters of a demand model that models a demand of the group of substitutable products, the estimating comprising grouping the historical sales data by merchandise and location hierarchies, wherein the demand model comprises a coefficient expressing change in demand due to product cannibalization caused by a change in the inventory assortment and the demand model parameters comprises at least seasonality parameters, price sensitivity parameters and assortment parameters” (see Independent Claims 1, 9 and 17);
“solving an optimization problem for the inventory assortment and allocation of the group of substitutable products from the different warehouses to the different retail stores where the inventory assortment is given and demand distributions are known, the optimization problem comprising a plurality of decision variables, an objective function, and a corresponding Lagrangian relaxation, the solving to generate an optimized solution comprising” (see Independent Claims 1, 9 and 17);
“determining a gradient of the objective function with respect to the plurality of decision variables, wherein the gradient comprises a direction and magnitude of a steepest ascent or descent of the objective function” (see Independent Claims 1, 9 and 17);
“updating the plurality of decision variables based on a direction of the gradient” (see Independent Claims 1, 9 and 17);
“updating dual lambda variables of the Lagrangian relaxation” (see Independent Claims 1, 9 and 17);
“when the optimized solution improves the objective function, repeating the determining the gradient of the objective function, the updating the plurality of decision variables, and the updating the dual lambda variables” (see Independent Claims 1, 9 and 17);
“when the optimized solution fails to improve the objective function or a predefined number of iterations have been reached, assigning the optimized solution as a final optimized solution” (see Independent Claims 1, 9 and 17);
“wherein the final optimized solution comprises an optimal inventory allocation of each of the group of substitutable products from each of the plurality of different warehouses to each of the plurality of different retail stores and an optimal assortment of products to be distributed from each of the different warehouses to each of the retail stores” (see Independent Claims 1, 9 and 17).
Here, the claim limitation steps for Independent Claims 1, 9 and 17 are directed to the abstract idea of mathematical optimization of inventory allocation and assortment decisions for substitutable products (a combination of mathematical concepts and economic practices).
These abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (3) commercial interactions (including sales activities or behaviors) or (4) managing personal behavior (including teachings or following rules or instructions) or (5) fundamental economic principles or practices.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mathematical Concepts” which pertains to which pertains to (6) mathematical relationships or (7) mathematical calculations.
That is, other than reciting the additional elements of (e.g., “one or more processors” & “the system” & “a storage device”), nothing in the claim elements precludes the steps from being performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) commercial interactions (including sales activities or behaviors) or (4) managing personal behavior (including teachings or following rules or instructions) or (5) fundamental economic principles or practices and additionally or alternatively as “Mathematical Concepts” which pertains to which pertains to (6) mathematical relationships or (7) mathematical calculations.
Therefore, at step 2a prong 1, Yes, Claims 1-2, 5-10, 13-18 and 21-26 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 1 does not recite any additional elements. Independent Claim 9 recites additional elements directed to: (e.g., “one or more processors” & “a storage device”). Independent Claim 17 recites additional elements directed to: (e.g., “one or more processors” & “the system” & “a storage device”). These additional elements have been considered both individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Therefore, at step 2a prong 2, Claims 1-2, 5-10, 13-18 and 21-26 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 1 does not recite any additional elements. Independent Claim 9 recites additional elements directed to: (e.g., “one or more processors” & “a storage device”). Independent Claim 17 recites additional elements directed to: (e.g., “one or more processors” & “the system” & “a storage device”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (f) and MPEP § 2106.05 (h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Spec ¶ [0023]: “Processor 22 may be any type of general or specific purpose processor. System 10 further includes a memory 14 for storing information and instructions to be executed by processor 22.”).
Independent Claims 1, 9 and 17: The “receiving” steps in Independent Claims 1, 9 and 17 pertain to activities at most amounts to “mere data gathering” and the “updating” steps in Independent Claims 1, 9 and 17 pertain to activities at most amounts to “updating activity logs” wherein each of these steps reflects insignificant extra-solution activities (see MPEP § 2106.05 (g)). Additionally, these activities have been expressly recognized as WURC under step 2B, and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii – Receiving or transmitting data over a network, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See also MPEP § 2106.05(d) ii – Electronic recordkeeping, Alice Corp., 134S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2, 5-8, 10, 13-16, 18 and 21-26 recite the same abstract ideas as Independent Claim 1, 9 and 17 along with further steps/details that could also be performed in the human mind as “Mental Processes” (including observations or evaluations or judgements) or using pen to paper as a “physical aid” and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to commercial interactions (including sales activities or behaviors) or managing personal behavior (including teachings or following rules or instructions) or fundamental economic principles or practices and additionally or alternatively as “Mathematical Concepts” which pertains to which pertains to mathematical relationships or mathematical calculations.
Dependent Claims 2, 5-6, 10, 13-14, 18, 21 and 24-26 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1, 9 and 17. Dependent Claims 7-8, 15-16 and 22-23: With respect to the additional elements of (e.g., “Internet of Things (IoT) sensors” & “a gateway sensor” & “a plurality of IoT devices” & “automatically transporting”) and when considered in view of the claim limitations both individually and as an ordered combination (as a whole) are insufficient to add a practical application under step 2a prong 2 and also are not significantly more under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment for optimizing inventory assortment and allocation of a group of products, wherein the group of products are allocated from a plurality of different warehouses to a plurality of different retail stores using a computer in an inventory management environment (see MPEP § 2106.05(h)).
The additional element of “Internet of Things (IoT) sensors” in Dependent Claims 7-8, 15-16 and 22-23 does not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as WURC in the art.
See Applicant’s Original Specification ¶ [0090]: “An loT device can be any device that has a sensor attached to it and can transmit data from one object to another or to people with the help of Internet. loT devices include wireless sensors, software, actuators, and computer devices.” Official Notice: Examiner notes that the IoT device is common in the art of supply chain management and wirelessly tracking and monitoring conditions of a group of products in different environments such as retail stores as well as transportation from one location to another in a supply chain network. This includes for example; monitoring the perishable of groups of products or goods from one location to another by attaching different types of sensors (e.g., humidity sensors & temperature sensors) onto products. Also see US PG Pub (US 2022/0012677 A1) hereinafter Rongley. See also Rongley at ¶ [0004]: “While the use of robots in warehouses and distribution centers is not uncommon, retail locations, such as grocery stores and department stores, have been reluctant to deploy robots in order to provide customers with a personalized shopping experience.” See also Rongley at ¶ [0090]: “The inventory item identifying module 122 may be at least one of logically, electronically, and communicatively coupled to a sensor (e.g., barcode reader, RFID scanner, QR code scanner, weight sensor, image recognition device) of each autonomous storage unit. In some cases, autonomous storage units may utilize one or more sensors to track their inventory and/or detect inventory item pick up events, to name two non-limiting examples, further described in relation to FIGS. 4 and 5.” See also US PG Pub (US 2023/0325766 A1) – “Autonomous Exposure-Based Product Replacement System”, hereinafter Cella, et. al. See Cella at ¶ [0493]: “The IoT devices may identify collection for each warehouse and the warehouses may use the IoT devices to communicate with each other. The IoT devices may be configured to process data without using the cloud.” See Cella at ¶ [2555-2556]: “The gateway 14030 communicates with each of the sensor systems 14032, connected products 14034, and additional data sources 14036. The gateway 14030 then communicates directly with the digital product network service 14002. The gateway 14030 hosts the digital product network service 14002.”
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-2, 5-10, 13-18 and 21-26 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-2, 5-10, 13-18 and 21-26 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 103
7. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
8. 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.
9. 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.
10. Claims 1-2, 9-10, 17-18 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent # (US 12,045,846 B1) hereinafter Jean-Marc Tilly, in view of US PG Pub (US 2023/0137578 A1) hereinafter Cella, et. al., in view of US Patent # (US 9,805,402 B1) hereinafter Maurer, et. al., and in further view of Foreign Patent Application (WO 03/075195 A2) hereinafter Hirth, et. al.
Regarding Independent Claim 1, Jean-Marc Tilly method of optimizing inventory assortment and allocation of a group of substitutable products teaches the following:
- optimizing inventory assortment and allocation of a group of substitutable products, wherein the group of substitutable products are allocated from a plurality of different warehouses to a plurality of different retail stores (see at least Jean-Marc Tilly: Fig. 1 & Col. 5, Lns. 10-18 & Col. 7, Lns. 51-67. Jean-Marc Tilly notes that one or more supply chain entities 150 that identifies items as the mobile scanner passes by one or more items, such as, for example, a mobile robotic scanner which scans items on store shelves or products in a warehouse. See also Jean-Marc Tilly at Col. 7, Lns. 51-67. Jean-Marc Tilly notes that distribution centers 156 may comprise automated warehousing systems 157 that automatically remove products from and place products into inventory based, at least in part, on the mappings of one or more items in the supply chain networks, one or more pricing strategies, modification of a product assortment, or demand or sales forecasts generated by demand planner. See also Jean-Marc Tilly at Col. 12, Lns. 19-45: Business rules and constraints 230 comprise any rules or constraints that may be reflected in an optimization problem to generate a pricing plan. Business rules and constraints 230 may be added to the optimization problem to generate prices that incorporate, for example, product interactions, direct effect factors, cross-effect factors, and pricing, margin, size, brand, and competitor constraints. See also Jean-Marc Tilly at ¶ [abstract].), the method comprising:
- receiving historical sales data for the group of substitutable products (see at least Jean-Marc Tilly: Col. 10, Lns. 54-67 & Col. 11, Lns. 22-46 & Fig. 5. Jean-Marc Tilly teaches that pricing data 222 may comprise historical prices, price changes, seasonality data, and the like. Pricing data 222 includes for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of one or more supply chain entities 150. See also Jean-Marc Tilly: Col. 10, Lns. 54-67: Jean-Marc Tilly teaches that categorization module 206 may define groups of items to be included in a particular category. Categories represent groupings of substitutable items. For example, a retailer may create categories comprising several substitutable retail products, where a change in the price of one product in the category will interact with the sales of other products in the same category.), wherein a first substitutable product and a second substitutable product in the group are substitutable products when they can be used in place of each other to satisfy a same need (see at least Jean Marc-Tilly: Col. 11, Lns. 54-67 & Col. 18, Lns. 9-22 & Fig. 5. Jean-Marc Tilly notes that any number of substitutable products may be grouped into any number of categories according to particular needs. Additionally, the groups of products assigned to particular categories may change over time by adding or removing products from categories based on, for example, changing product attributes, changing customer behavior, miscategorization of products, and the like. See also Jean-Marc Tilly at Col. 18, Lns. 4-18: Jean Marc Tilly notes that each of the three panels 502a-502c comprise boxes 504a-504c. Boxes 504a-504c illustrate substitutable products for the product under consideration. For example, as described in more detail below, first panel 502a illustrates the calculations when Product A 402 is the product under consideration. Accordingly, box 504a illustrates that Product B 404 and Product C 406 are the substitutable products when Product A 402 is the product under consideration. Similarly, box 504b in panel 502b illustrates that Product A 402 and Product C 406 are substitutable products when Product B 404 is the product under consideration, and box 504c in panel 502c illustrates that Product A 402 and Product B 404 are substitutable products when Product C 406 is the product under consideration. See also Jean-Marc Tilly at ¶ [abstract]: Jean Marc Tilly notes that sales forecast without using a cross elasticity by receiving a percentage pricing change for at least two substitutable products of an inventory in a supply chain network having one or more supply chain entities, and at least two substitutable products are grouped in the same product category and at least one of at least two substitutable products is grouped in a product assortment.);
- estimating demand model parameters of a demand model that models a demand of the group of substitutable products (see at least Jean-Marc Tilly: Fig. 2 & Fig. 5 & Col. 3, Lns. 14-24. Jean-Marc Tilly notes that the disclosed model comprises a demand model with product interaction due to base price changes which is more accurate than previous models based, at least in part, on the larger amount of data that is processed by the model when compared with models that include estimated cross elasticities. A demand planner executes a method that forecasts the sales for items in a category based on price changes of an item and one or more substitutable items, while eliminating the need to estimate cross elasticities for each pair of items. See also Jean-Marc Tilly at Col. 2, Lns. 36-61.), the estimating comprising grouping the historical sales data by merchandise and location hierarchies (see at least Jean-Marc Tilly: Fig. 2 & Col. 4, Lns. 11-26 & Col. 10, Lns. 51-67. Jean-Marc Tilly notes that demand planner 110 comprises one or more modules to, for example, define item groups and hierarchies, store and transmit product information, and calculate item and group elasticities, price changes, and direct and cross-effect factors. Server 122 of inventory system 120 is configured to receive and transmit inventory data, including item identifiers, pricing data, attribute data, inventory levels, and other like data about one or more items at one or more locations in the supply chain network 100. See also Jean-Marc Tilly at Col. 10, Lns. 61-67: Jean-Marc Tilly notes that categories are defined in a hierarchy comprising classes and sub-classes, which may be stored as hierarchy data 224 in database 114 or one or more databases associated with demand planner 110 or one or more supply chain entities 150. See also Jean-Marc Tilly at Fig. 2 noting “hierarchy data” 228.), wherein the demand model comprises a coefficient expressing change in demand due to product cannibalization caused by a change in the inventory assortment (see at least Jean-Marc Tilly: Fig. 5 & Col. 3, Lns. 14-24 & Col. 15, Lns. 33-53. Jean-Marc Tilly notes that the disclosed model comprises a demand model with product interaction due to base price changes which is more accurate than previous models based, at least in part, on the larger amount of data that is processed by the model when compared with models that include estimated cross elasticities. A demand planner executes a method that forecasts the sales for items in a category based on price changes of an item and one or more substitutable items, while eliminating the need to estimate cross elasticities for each pair of items. The disclosed model 40 considers cannibalization, or the effect of one item stealing sales from another item. Cannibalization may occur when a new product is introduced that reduces sales of another existing product or when a product reduces its base price and take sales from other substitutes perceived of lesser value. For example, when a name brand cola beverage company introduced a new zero-calorie cola beverage into the market, the new product stole more sales from the company's existing diet soda than from competitors in the marketplace. See also Jean-Marc Tilly at Col. 15, Lns. 63-67 & Col. 16, Lns. 1-4: The disclosed model may determine changes in demand for an item caused by a price change in another item, even when the items are in different categories. For example, if the price of milk greatly decreases, shoppers may additionally purchase almond milk, now that they can afford it. Accordingly, the price of almond milk would change. And even if the price went down a small amount, based on the larger budget, the shopper may still purchase more items. See also Jean-Marc Tilly noting at Fig. 5 for a coefficient expressing changes for demand of changes in inventory assortment.) and the demand model parameters comprises at least seasonality parameters (see at least Jean-Marc Tilly: Col. 4, Lns. 37-40 & Col. 11, Lns. 22-29. Jean-Marc Tilly notes that pricing data 222 may comprise historical prices, price changes, seasonality data, and the like. Pricing data 222 includes for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of one or more supply chain entities 150. See also Jean-Marc Tilly at Col. 4, Lns. 37-40: Jean-Marc Tilly notes that inventory database 124 may comprise explanatory variables that describe the data relating to specific past, current, or future indicators and the data of promotions, seasonality, special events (such as sporting events), weather, and the like.), price sensitivity parameters (see at least Jean-Marc Tilly: Col. 13, Lns. 20-40 & Col. 14, Lns. 39-46. Jean-Marc Tilly notes that it relates to price elasticity as the price elasticity of demand is the product of price sensitivity Bi and price Pi. See also Jean-Marc Tilly at Col. 14, Lns. 39-46: The elasticity may be estimated by the multiplication of a price sensitivity and the current price of the item. The direct effect factor indicates, for example, whether the sales of an item will increase or decrease based on the percentage price change of the item. The elasticity indicates how much sales change in response to changes in price.) and assortment parameters (see at least Jean-Marc Tilly: Fig. 3 & Fig. 5 & Col. 21, Lns. 18-21. Jean-Marc Tilly teaches that after demand planner 110 generates the calculations at action 320, demand planner 110 may calculate new prices for one or more retail items at action 322 and/or generate a new product assortment at action 324. See Jean-Marc Tilly at Fig. 3 step 324 “generate new product assortment.”).
Jean-Marc Tilly method of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Cella in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- solving an optimization problem for the inventory assortment (see at least Cella: ¶ [0338] & ¶ [0638-0639]. Cella notes that the set of facilities that provide automated recommendations for a set of value chain process tasks provide recommendations involving a wide range of types of activities, such as product assortment activities, product management activities, logistics activities, reverse logistics activities, artificial intelligence configuration activities, maintenance activities, product support activities, product recommendation activities. See also Cella at ¶ [0338]: For example, a supply chain or inventory management application in the value chain management platform 604, such as one for ordering replacement parts for a machine or item of equipment, may access the same data set about what parts have been replaced for a set of machines as a predictive maintenance application that is used to predict whether a component of a ship, or facility of a port is likely to require replacement parts. Similarly, prediction may be used with respect to the resupply of items. See also Cella at ¶ [0363]: The artificial intelligence systems 1160 may process the behavior data and conclude that there is a perceived need for greater consumer access to a second product in the category of goods 3010. This coordinated intelligence may be, optionally automatically, applied to the set of supply chain applications 812 so that, for example, production resources or other resources in the value chain for the category of goods are allocated to the second product. In examples, a distributor who handles stocking retailer shelves may receive a new stocking plan that allocates more retail shelf space for the second product, such as by taking away space from a lower margin product and the like. See also Cella at ¶ [1225] and ¶ [1245]: Solving an optimization problem is achieved through a deterministic policy gradient or the optimization method such as gradient descent to adjust weights and update the neural network characteristics.) and allocation of the group of substitutable products (see at least Cella: ¶ [0363] & ¶ [0747] & ¶ [1036] & ¶ [1616]. Cella notes that the demand of a product in the value chain network may be affected by factors like changes in consumer confidence, recessions, excessive inventory levels, substitute product pricing. See also Cella at ¶ [1038]: In a manufacturing enterprise, a CTO digital twin 8310 may depict where environment-compatible updates, upgrades, or substitutions may be available. See also Cella at ¶ [1616]: The artificial intelligence system 10212 may then predict and assess the impact of the predicted disruption to decide if a supply chain redesign may be required to minimize the disruption. Impact assessment and/or prediction may use a set of economic, financial or operating models, among many others, such as to assess primary, secondary, and other effects on an overall workflow or system. For example, assessment or prediction may include the impact on contract liability (such as liability for failure to deliver, including the obligation to pay for the cost of the buyer to cover in the marketplace by buying substitute items. See also Cella at ¶ [0363] noting “the value chain for the category of goods are allocated to the second product.”)), from the different warehouses to the different retail stores where the inventory assortment is given and demand distributions are known (see at least Cella: ¶ [0334] & ¶ [0439] & ¶ [0502] & ¶ [2604]. Cella teaches that the digital products 14512 may include products in a warehouse, packaging, environment sensors, or similar products. The products may generate data indicating a proximity of different products in the same warehouse or the presence of the same or different products in different warehouses. See also Cella at ¶ [0334]: The set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, a workforce management application 888 (such as for managing workers in various work forces, including work forces in, on or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise management locations and retail stores. See also Cella at ¶ [0439]: The set of demand management applications, supply chain applications, intelligent product applications and enterprise resource management applications may include, for example, ones involving supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation and demand customer profiling. See also Cella at ¶ [0502]: The set of supply chain applications and demand management applications includes, for example and without limitation one or more involving inventory management, demand prediction, product and service bundling, product assortment, upsell offer configuration, customer feedback engagement, customer survey, or others.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly method of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: solving an optimization problem for the inventory assortment and allocation of the group of substitutable products from the different warehouses to the different retail stores where the inventory assortment is given and demand distributions are known, and in view of Cella, whereby the additive manufacturing platform may address a “recall” situation by adding or revising a product in-warehouse, and may monitor for problems with vehicles, machines, tools, and other equipment being used and then replacing needed parts or materials “as needed,” creating tools on-demand as needed by workers or robots in warehouse/distribution network (see at least Cella: ¶ [1572]). An artificial intelligence system (e.g., a robotic process automation system trained on a training set of expert service visit data), to determine a recommended action, may involve replacement of a part and/or repair of a part, or some other activity. The platform may automatically determine that an element should be manufactured to facilitate repair, such as where a complementary component may be generated to replace a worn or absent element. Techniques utilized to achieve this include using AI to optimize product design, manufacturing process configuration (including packaging material generation process), job scheduling, prioritization and/or logistics (efficiency of warehouse processes for replacing parts, materials without disrupting other general processes involved in warehouse/distribution center) (see at least Cella: ¶ [1573].)
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cella, the results of the combination were predictable.
Jean-Marc Tilly / Cella method of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Maurer in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- the optimization problem comprising a plurality of decision variables (see at least Maurer: Figs. 8-9 & Col. 30, Lns. 55-67. Maurer notes that the optimization can follow the approach of solving constrained optimization problems, e.g., decoupling the optimization into multiple phases. See also Maurer at Col. 12, Lns. 61-67 noting the “decision variables that can be used”. See also Maurer at Col. 35, Lns. 28-32. Maurer teaches that the multi-period inventory optimization problem is solved with aggregate-level constraints on storage and receipt capacities. The described results are analyzed at an aggregate level and comparisons between items are drawn based on their attributes.), an objective function (see at least Maurer: Fig. 8 & Col. 13, Lns. 5-10. Maurer notes the objective function (1) subject to the constraints (2)-(9) shown at Col. 13, Lns. 5-10.), and a corresponding Lagrangian relaxation (see at least Maurer: Fig. 8 & Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.), the solving to generate an optimized solution comprising (see at least Maurer: Col. 14, Lns. 60-67 & Col. 15, Lns. 30-43 & Col. 24, Lns. 5-16.);
- determining a gradient of the objective function with respect to the plurality of decision variables (see at least Maurer: Fig. 8 & Col. 21, Lns. 49-67 & Col. 22, Lns. 1-18. Maurer notes the
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“Stochastic gradient of the objective function.” See also Maurer at Col. 21, Lns. 49-67. Maurer teaches that a stream of random variates for demand (di, ... , dr) and lead times (Ii, ... , Ir) may be generated. A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 12, Lns. 62-67.), wherein the gradient comprises a direction and magnitude of a steepest ascent or descent of the objective function (see at least Maurer: Fig. 8 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 7-18: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending step towards the gradient direction.)
- updating the plurality of decision variables based on a direction of the gradient (see at least Maurer: Figs. 8-9 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 7-18: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending
step towards the gradient direction.)
- updating dual lambda variables of the Lagrangian relaxation (see at least Maurer: Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to he
found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function
using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.).
- wherein the optimized solution improves the objective function (see at least Maurer: Col. 29, Lns. 60-67 & Col. 30, Lns. 1-5 & Fig. 8. Maurer notes that the simulation may use an objective function and can include an inner loop, where the ordering model tool 320 may simulate various inventory levels for an item of the item category and determine therefrom an optimization of the objective function. The inner loop may include an iterative simulation of the inventory level until a convergence criterion is satisfied, such as one that may optimize the objective function. See also Maurer: Col. 33, Lns. 53-57: Operations 812-816 may be iteratively repeated until a convergence criterion is met at operation 818. The convergence criterion may be based on the number of iterations and/or improvements to the objective function such as changes to the step sizes between iterations.), repeating the determining the gradient of the objective function (see at least Maurer: Fig. 8 & Col. 21, Lns. 49-67 & Col. 22, Lns. 1-18. Maurer notes the
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“Stochastic gradient of the objective function.” See also Maurer at Col. 21, Lns. 49-67. Maurer teaches that a stream of random variates for demand (di, ... , dr) and lead times (Ii, ... , Ir) may be generated. A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 12, Lns. 62-67.), the updating the plurality of decision variables (see at least Maurer: Fig. 8 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 8-11: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending step towards the gradient direction.), and the updating the dual lambda variables (see at least Maurer: Fig. 8 & Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.).
- when the optimized solution fails to improve the objective function or a predefined number of iterations have been reached, assigning the optimized solution as a final optimized solution (see at least Maurer: Col. 14, Lns. 25-39 & Fig. 6 & Figs. 8-9. Maurer teaches that the second problem can include the multi-item, multi-time period problem, where the results of the single-item problems can be used as inputs and optimize the objective function (1) subject to the constraints (2)-(9). The former one can be referred to as the inner loop and is further illustrated in FIG. 5. The latter can be referred to as the outer loop. Because inner and outer loops may depend on the output of each other, these loops should be iteratively solved convergence to an optimal solution. See also Maurer at Col. 19, Lns. 4-15: The ordering model tool 320 can search for a target inventory level that may optimize the objective function. This search can be performed across the different inventory levels to come up with the inventory level that may provide an optimum result (e.g., maximize profit). In other words, the inner loop may cause the search to be repeated across the various inventory levels until a convergence criterion may be met, such as whether the objective function may be optimized or improvements to the objective function. Once met, the ordering model tool 320 can compare the corresponding inventory level to a current level and generate the ordering decision 530. See also Maurer at Col. 31, Lns. 65-67 and Col. 32, Lns. 1-9: Maurer teaches that if the discrepancy fails a convergence criterion, the adaptive control may determine an adjustment to the opportunity cost that may mitigate the discrepancy. This determination can use, for example, a sub-gradient algorithm that may search for Lagrange multipliers that can optimize an objective function used in simulating the consumption. The opportunity cost can be adjusted based on the step size and direction from the search. To illustrate, if the consumption overuses the capacity, the adaptive capacity control tool may increase the cost. Otherwise, a decrease may be performed.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella method of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: the optimization problem comprising a plurality of decision variables, an objective function, and a corresponding Lagrangian relaxation, the solving to generate an optimized solution comprising determining a gradient of the objective function with respect to the plurality of decision variables, wherein the gradient comprises a direction and a magnitude of a steepest ascent or descent of the objective function & updating the plurality of decision variables based on a direction of the gradient & updating dual lambda variables of the Lagrangian relaxation and when the optimized solution improves the objective function, repeating the determining the gradient of the objective function, the updating the plurality of decision variables, and the updating the dual lambda variables & when the optimized solution fails to improve the objective function or a predefined number of iterations have been reached, assigning the optimized solution as a final optimized solution, and in further view of Maurer, whereby the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution can be found. This can allow the decoupling of the two loops. As a result, algorithms in each loop can be modified separately for improvements. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. Further, as the Lagrange multipliers for storage capacity could he interpreted as the value of having additional unit capacity, these values can be used in analysis of improving or designing new inventory facilities (see at least Maurer: (Col. 15, Lns. 30-43)).
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Maurer, the results of the combination were predictable.
Jean-Marc Tilly / Cella / Maurer method of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Hirth in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- wherein the final optimized solution comprises an optimal inventory allocation of each of the group of substitutable products (see at least Hirth: Page 21, Lns. 14-31 & Page 22, Lns. 1-5 & Page 38, Lns. 1-7. Hirth teaches that The ATP service is connected to one or more of the programs that provide: (1) scheduling of the processes (e.g., to determine the actual date of fulfillment), and (2) product selection or substitution, partner/location selection, capable to promise (CTP) service, production planning and scheduling, planning in general (e.g., forecasts, product allocations) and alert handling (e.g., if there is no confirmation for a request). See also Hirth at Page 21, Lns. 24-31: The product selection or substitution service selects the correct product for a logistics process according to batches, serial numbers, shelf-life expiration date, and stock determination (i.e., type of stock: on hand, blocked, inspection, etc.). The product selection or substitution program also may substitute products based on predefined parameter (e.g., a listing of acceptable product substitutes) and connect the bill of materials to handling the bill of materials. For example, a customer may need a product that is unavailable in the time frame specified in the order. The program then can determine an acceptable substitute product based on parameters that the customer has provided for the product. See also Hirth at Page 22, Lns. 1-5: If that brand is unavailable, the program may substitute a different brand of paper that otherwise meets the customer's criteria based on parameters provided to the program. Like the scheduling service, the product selection or substitution service is connected to the ATP service for the transfer of data. See also Hirth at Page 38, Lns. 1-7: Allocation of articles to the warehouses can be beneficially optimized to reduce inventory costs.) from each of the plurality of different warehouses to each of the plurality of different retail stores (see at least Hirth: Page 37, Lns. 1-5 & Page 38, Lns. 13-27. Hirth teaches that the engine is used when delivering goods from central warehouses (i.e., warehouses with a full range of products) and individual warehouses (i.e., warehouses with partial product ranges) to multiple stores (e.g., supermarkets and retailers). See also Hirth at Page 38, Lns. 13-27: Hirth notes that the engine can be used to manage the logistics where multiple warehouses and vendors deliver to a store, but the store desires a single daily delivery. The engine also can be used to manage the logistics of the cross-docking of single article vendor and retail warehouse pallets, pre-picked retailer and vendor warehouse pallets/containers, and flow-through of handling units from inbound pallets to outbound containers for the stores. The logistics is controlled by the engine by having the warehouse platform that receives the goods being empty at night, using inbound deliveries of goods from other warehouses in the morning, and outbound delivery of goods to the retailers in the afternoon. In this manner, the cross-docking warehouse is empty at the end of the day. In this scenario, the engine is used to optimize the routes from warehouses or vendors that supply the goods to the cross-docking platforms, as well as optimize the routes from the cross-docking platforms to the retailers' stores.) and an optimal assortment of products to be distributed from each of the different warehouses to each of the retail stores (see at least Hirth: Page 35, Lns. 10-14 & Page 35, Lns. 16-30 & Page 37, Lns. 18-31. Hirth notes that the quantity of stock in the warehouse may be set according to the range of coverage of the store, assortment of stock, the store's programs to optimize layout and stocks in stores, and the reorder point. The shipments can be optimized based on routes and using only full truckloads. See also Hirth at Page 35, Lns. 16-30: To optimize the logistics, the amount and type of stock in the store is based on a range of coverage, an assortment, and the store's own programs to optimize layout and stocks in stores. See also Hirth at Page 37, Lns. 18-31: The fulfillment coordination engine also can be used to coordinate the logistics of fast- and slow-moving items in a cross-docking warehouse scenario, such as a retail warehouse service in which the engine coordinates the movement of goods from the vendor to the retailer's store. This scenario is a variation of the pull warehouse scenario, described above, as applied to retail businesses. In particular, this scenario includes situations in which there is a large assortment of goods and it is not worth warehousing all the goods in every warehouse.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer method of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: wherein the final optimized solution comprises an optimal inventory allocation of each of the group of substitutable products from each of the plurality of different warehouses to each of the plurality of different retail stores and an optimal assortment of products to be distributed from each of the different warehouses to each of the retail stores, and in further view of Hirth, whereby the individual stores order all articles together from an organizing facility. A benefit of using the fulfillment coordination engine in this scenario is that there can be an optimization of routes from the slow-moving item warehouses to the fast-moving item warehouses, and from the fast-moving item warehouses to the stores. Another benefit is the optimization of the delivery to the store by using only one delivery for all the goods to each store. In addition, allocation of articles to the warehouses can be beneficially optimized to reduce inventory costs. The fulfillment coordination engine can be used for cross-docking delivery of goods for a warehouse service that manages retail goods by providing outbound delivery of the goods from the vendor to the retailer's store (see at least Hirth: (Page 38, Lns. 1-10)).
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hirth, the results of the combination were predictable.
Regarding Independent Claim 9, Jean-Marc Tilly non-transitory computer-readable medium of optimizing inventory assortment and allocation of a group of substitutable products teaches the following:
- having instruction stored thereon, when executed by one or more processors (see at least Jean-Marc Tilly: Fig. 1 & Col. 6, Lns. 23-31. Jean-Marc Tilly noting processor 132 in Fig. 1.), cause the one or more processors (see at least Jean-Marc Tilly: Fig. 1 & Col. 6, Lns. 23-31.) to optimize inventory assortment and allocation of a group of substitutable products, wherein the group of substitutable products are allocated from a plurality of different warehouses to a plurality of different retail stores (see at least Jean-Marc Tilly: Fig. 1 & Col. 5, Lns. 10-18 & Col. 7, Lns. 51-67. Jean-Marc Tilly notes that one or more supply chain entities 150 that identifies items as the mobile scanner passes by one or more items, such as, for example, a mobile robotic scanner which scans items on store shelves or products in a warehouse. See also Jean-Marc Tilly at Col. 7, Lns. 51-67. Jean-Marc Tilly notes that distribution centers 156 may comprise automated warehousing systems 157 that automatically remove products from and place products into inventory based, at least in part, on the mappings of one or more items in the supply chain networks, one or more pricing strategies, modification of a product assortment, or demand or sales forecasts generated by demand planner. See also Jean-Marc Tilly at Col. 12, Lns. 19-45: Business rules and constraints 230 comprise any rules or constraints that may be reflected in an optimization problem to generate a pricing plan. Business rules and constraints 230 may be added to the optimization problem to generate prices that incorporate, for example, product interactions, direct effect factors, cross-effect factors, and pricing, margin, size, brand, and competitor constraints. See also Jean-Marc Tilly at ¶ [abstract].), the optimizing comprising:
- receiving historical sales data for the group of substitutable products (see at least Jean-Marc Tilly: Col. 10, Lns. 54-67 & Col. 11, Lns. 22-46 & Fig. 5. Jean-Marc Tilly teaches that pricing data 222 may comprise historical prices, price changes, seasonality data, and the like. Pricing data 222 includes for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of one or more supply chain entities 150. See also Jean-Marc Tilly: Col. 10, Lns. 54-67: Jean-Marc Tilly teaches that categorization module 206 may define groups of items to be included in a particular category. Categories represent groupings of substitutable items. For example, a retailer may create categories comprising several substitutable retail products, where a change in the price of one product in the category will interact with the sales of other products in the same category.), wherein a first substitutable product and a second substitutable product in the group are substitutable products when they can be used in place of each other to satisfy a same need (see at least Jean Marc-Tilly: Col. 11, Lns. 54-67 & Col. 18, Lns. 9-22 & Fig. 5. Jean-Marc Tilly notes that any number of substitutable products may be grouped into any number of categories according to particular needs. Additionally, the groups of products assigned to particular categories may change over time by adding or removing products from categories based on, for example, changing product attributes, changing customer behavior, miscategorization of products, and the like. See also Jean-Marc Tilly at Col. 18, Lns. 4-18: Jean Marc Tilly notes that each of the three panels 502a-502c comprise boxes 504a-504c. Boxes 504a-504c illustrate substitutable products for the product under consideration. For example, as described in more detail below, first panel 502a illustrates the calculations when Product A 402 is the product under consideration. Accordingly, box 504a illustrates that Product B 404 and Product C 406 are the substitutable products when Product A 402 is the product under consideration. Similarly, box 504b in panel 502b illustrates that Product A 402 and Product C 406 are substitutable products when Product B 404 is the product under consideration, and box 504c in panel 502c illustrates that Product A 402 and Product B 404 are substitutable products when Product C 406 is the product under consideration. See also Jean-Marc Tilly at ¶ [abstract]: Jean Marc Tilly notes that sales forecast without using a cross elasticity by receiving a percentage pricing change for at least two substitutable products of an inventory in a supply chain network having one or more supply chain entities, and at least two substitutable products are grouped in the same product category and at least one of at least two substitutable products is grouped in a product assortment.);
- estimating demand model parameters of a demand model that models a demand of the group of substitutable products (see at least Jean-Marc Tilly: Fig. 2 & Fig. 5 & Col. 3, Lns. 14-24. Jean-Marc Tilly notes that the disclosed model comprises a demand model with product interaction due to base price changes which is more accurate than previous models based, at least in part, on the larger amount of data that is processed by the model when compared with models that include estimated cross elasticities. A demand planner executes a method that forecasts the sales for items in a category based on price changes of an item and one or more substitutable items, while eliminating the need to estimate cross elasticities for each pair of items. See also Jean-Marc Tilly at Col. 2, Lns. 36-61.), the estimating comprising grouping the historical sales data by merchandise and location hierarchies (see at least Jean-Marc Tilly: Fig. 2 & Col. 4, Lns. 11-26 & Col. 10, Lns. 51-67. Jean-Marc Tilly notes that demand planner 110 comprises one or more modules to, for example, define item groups and hierarchies, store and transmit product information, and calculate item and group elasticities, price changes, and direct and cross-effect factors. Server 122 of inventory system 120 is configured to receive and transmit inventory data, including item identifiers, pricing data, attribute data, inventory levels, and other like data about one or more items at one or more locations in the supply chain network 100. See also Jean-Marc Tilly at Col. 10, Lns. 61-67: Jean-Marc Tilly notes that categories are defined in a hierarchy comprising classes and sub-classes, which may be stored as hierarchy data 224 in database 114 or one or more databases associated with demand planner 110 or one or more supply chain entities 150. See also Jean-Marc Tilly at Fig. 2 noting “hierarchy data” 228.), wherein the demand model comprises a coefficient expressing change in demand due to product cannibalization caused by a change in the inventory assortment (see at least Jean-Marc Tilly: Fig. 5 & Col. 3, Lns. 14-24 & Col. 15, Lns. 33-53. Jean-Marc Tilly notes that the disclosed model comprises a demand model with product interaction due to base price changes which is more accurate than previous models based, at least in part, on the larger amount of data that is processed by the model when compared with models that include estimated cross elasticities. A demand planner executes a method that forecasts the sales for items in a category based on price changes of an item and one or more substitutable items, while eliminating the need to estimate cross elasticities for each pair of items. The disclosed model 40 considers cannibalization, or the effect of one item stealing sales from another item. Cannibalization may occur when a new product is introduced that reduces sales of another existing product or when a product reduces its base price and take sales from other substitutes perceived of lesser value. For example, when a name brand cola beverage company introduced a new zero-calorie cola beverage into the market, the new product stole more sales from the company's existing diet soda than from competitors in the marketplace. See also Jean-Marc Tilly at Col. 15, Lns. 63-67 & Col. 16, Lns. 1-4: The disclosed model may determine changes in demand for an item caused by a price change in another item, even when the items are in different categories. For example, if the price of milk greatly decreases, shoppers may additionally purchase almond milk, now that they can afford it. Accordingly, the price of almond milk would change. And even if the price went down a small amount, based on the larger budget, the shopper may still purchase more items. See also Jean-Marc Tilly noting at Fig. 5 for a coefficient expressing changes for demand of changes in inventory assortment.) and the demand model parameters comprises at least seasonality parameters (see at least Jean-Marc Tilly: Col. 4, Lns. 37-40 & Col. 11, Lns. 22-29. Jean-Marc Tilly notes that pricing data 222 may comprise historical prices, price changes, seasonality data, and the like. Pricing data 222 includes for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of one or more supply chain entities 150. See also Jean-Marc Tilly at Col. 4, Lns. 37-40: Jean-Marc Tilly notes that inventory database 124 may comprise explanatory variables that describe the data relating to specific past, current, or future indicators and the data of promotions, seasonality, special events (such as sporting events), weather, and the like.), price sensitivity parameters (see at least Jean-Marc Tilly: Col. 13, Lns. 20-40 & Col. 14, Lns. 39-46. Jean-Marc Tilly notes that it relates to price elasticity as the price elasticity of demand is the product of price sensitivity Bi and price Pi. See also Jean-Marc Tilly at Col. 14, Lns. 39-46: The elasticity may be estimated by the multiplication of a price sensitivity and the current price of the item. The direct effect factor indicates, for example, whether the sales of an item will increase or decrease based on the percentage price change of the item. The elasticity indicates how much sales change in response to changes in price.) and assortment parameters (see at least Jean-Marc Tilly: Fig. 3 & Fig. 5 & Col. 21, Lns. 18-21. Jean-Marc Tilly teaches that after demand planner 110 generates the calculations at action 320, demand planner 110 may calculate new prices for one or more retail items at action 322 and/or generate a new product assortment at action 324. See Jean-Marc Tilly at Fig. 3 step 324 “generate new product assortment.”)
Jean-Marc Tilly non-transitory computer-readable medium of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Cella in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- solving an optimization problem for the inventory assortment (see at least Cella: ¶ [0338] & ¶ [0638-0639]. Cella notes that the set of facilities that provide automated recommendations for a set of value chain process tasks provide recommendations involving a wide range of types of activities, such as product assortment activities, product management activities, logistics activities, reverse logistics activities, artificial intelligence configuration activities, maintenance activities, product support activities, product recommendation activities. See also Cella at ¶ [0338]: For example, a supply chain or inventory management application in the value chain management platform 604, such as one for ordering replacement parts for a machine or item of equipment, may access the same data set about what parts have been replaced for a set of machines as a predictive maintenance application that is used to predict whether a component of a ship, or facility of a port is likely to require replacement parts. Similarly, prediction may be used with respect to the resupply of items. See also Cella at ¶ [0363]: The artificial intelligence systems 1160 may process the behavior data and conclude that there is a perceived need for greater consumer access to a second product in the category of goods 3010. This coordinated intelligence may be, optionally automatically, applied to the set of supply chain applications 812 so that, for example, production resources or other resources in the value chain for the category of goods are allocated to the second product. In examples, a distributor who handles stocking retailer shelves may receive a new stocking plan that allocates more retail shelf space for the second product, such as by taking away space from a lower margin product and the like. See also Cella at ¶ [1225] and ¶ [1245]: Solving an optimization problem is achieved through a deterministic policy gradient or the optimization method such as gradient descent to adjust weights and update the neural network characteristics.) and allocation of the group of substitutable products (see at least Cella: ¶ [0363] & ¶ [0747] & ¶ [1036] & ¶ [1616]. Cella notes that the demand of a product in the value chain network may be affected by factors like changes in consumer confidence, recessions, excessive inventory levels, substitute product pricing. See also Cella at ¶ [1038]: In a manufacturing enterprise, a CTO digital twin 8310 may depict where environment-compatible updates, upgrades, or substitutions may be available. See also Cella at ¶ [1616]: The artificial intelligence system 10212 may then predict and assess the impact of the predicted disruption to decide if a supply chain redesign may be required to minimize the disruption. Impact assessment and/or prediction may use a set of economic, financial or operating models, among many others, such as to assess primary, secondary, and other effects on an overall workflow or system. For example, assessment or prediction may include the impact on contract liability (such as liability for failure to deliver, including the obligation to pay for the cost of the buyer to cover in the marketplace by buying substitute items. See also Cella at ¶ [0363] noting “the value chain for the category of goods are allocated to the second product.”)), from the different warehouses to the different retail stores where the inventory assortment is given and demand distributions are known (see at least Cella: ¶ [0334] & ¶ [0439] & ¶ [0502] & ¶ [2604]. Cella teaches that the digital products 14512 may include products in a warehouse, packaging, environment sensors, or similar products. The products may generate data indicating a proximity of different products in the same warehouse or the presence of the same or different products in different warehouses. See also Cella at ¶ [0334]: The set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, a workforce management application 888 (such as for managing workers in various work forces, including work forces in, on or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise management locations and retail stores. See also Cella at ¶ [0439]: The set of demand management applications, supply chain applications, intelligent product applications and enterprise resource management applications may include, for example, ones involving supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation and demand customer profiling. See also Cella at ¶ [0502]: The set of supply chain applications and demand management applications includes, for example and without limitation one or more involving inventory management, demand prediction, product and service bundling, product assortment, upsell offer configuration, customer feedback engagement, customer survey, or others.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly non-transitory computer-readable medium of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: solving an optimization problem for the inventory assortment and allocation of the group of substitutable products from the different warehouses to the different retail stores where the inventory assortment is given and demand distributions are known, and in view of Cella, whereby the additive manufacturing platform may address a “recall” situation by adding or revising a product in-warehouse, and may monitor for problems with vehicles, machines, tools, and other equipment being used and then replacing needed parts or materials “as needed,” creating tools on-demand as needed by workers or robots in warehouse/distribution network (see at least Cella: ¶ [1572]). An artificial intelligence system (e.g., a robotic process automation system trained on a training set of expert service visit data), to determine a recommended action, may involve replacement of a part and/or repair of a part, or some other activity. The platform may automatically determine that an element should be manufactured to facilitate repair, such as where a complementary component may be generated to replace a worn or absent element. Techniques utilized to achieve this include using AI to optimize product design, manufacturing process configuration (including packaging material generation process), job scheduling, prioritization and/or logistics (efficiency of warehouse processes for replacing parts, materials without disrupting other general processes involved in warehouse/distribution center) (see at least Cella: ¶ [1573].)
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cella, the results of the combination were predictable.
Jean-Marc Tilly / Cella non-transitory computer-readable medium of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Maurer in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- the optimization problem comprising a plurality of decision variables (see at least Maurer: Figs. 8-9 & Col. 30, Lns. 55-67. Maurer notes that the optimization can follow the approach of solving constrained optimization problems, e.g., decoupling the optimization into multiple phases. See also Maurer at Col. 12, Lns. 61-67 noting the “decision variables that can be used”. See also Maurer at Col. 35, Lns. 28-32. Maurer teaches that the multi-period inventory optimization problem is solved with aggregate-level constraints on storage and receipt capacities. The described results are analyzed at an aggregate level and comparisons between items are drawn based on their attributes.), an objective function (see at least Maurer: Fig. 8 & Col. 13, Lns. 5-10. Maurer notes the objective function (1) subject to the constraints (2)-(9) shown at Col. 13, Lns. 5-10.), and a corresponding Lagrangian relaxation (see at least Maurer: Fig. 8 & Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.), the solving to generate an optimized solution comprising (see at least Maurer: Col. 14, Lns. 60-67 & Col. 15, Lns. 30-43 & Col. 24, Lns. 5-16.);
- determining a gradient of the objective function with respect to the plurality of decision variables (see at least Maurer: Fig. 8 & Col. 21, Lns. 49-67 & Col. 22, Lns. 1-18. Maurer notes the
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“Stochastic gradient of the objective function.” See also Maurer at Col. 21, Lns. 49-67. Maurer teaches that a stream of random variates for demand (di, ... , dr) and lead times (Ii, ... , Ir) may be generated. A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 12, Lns. 62-67.), wherein the gradient comprises a direction and magnitude of a steepest ascent or descent of the objective function (see at least Maurer: Fig. 8 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 7-18: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending step towards the gradient direction.)
- updating the plurality of decision variables based on a direction of the gradient (see at least Maurer: Figs. 8-9 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 7-18: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending
step towards the gradient direction.)
- updating dual lambda variables of the Lagrangian relaxation (see at least Maurer: Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to he
found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function
using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.).
- wherein the optimized solution improves the objective function (see at least Maurer: Col. 29, Lns. 60-67 & Col. 30, Lns. 1-5 & Fig. 8. Maurer notes that the simulation may use an objective function and can include an inner loop, where the ordering model tool 320 may simulate various inventory levels for an item of the item category and determine therefrom an optimization of the objective function. The inner loop may include an iterative simulation of the inventory level until a convergence criterion is satisfied, such as one that may optimize the objective function. See also Maurer: Col. 33, Lns. 53-57: Operations 812-816 may be iteratively repeated until a convergence criterion is met at operation 818. The convergence criterion may be based on the number of iterations and/or improvements to the objective function such as changes to the step sizes between iterations.), repeating the determining the gradient of the objective function (see at least Maurer: Fig. 8 & Col. 21, Lns. 49-67 & Col. 22, Lns. 1-18. Maurer notes the
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“Stochastic gradient of the objective function.” See also Maurer at Col. 21, Lns. 49-67. Maurer teaches that a stream of random variates for demand (di, ... , dr) and lead times (Ii, ... , Ir) may be generated. A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 12, Lns. 62-67.), the updating the plurality of decision variables (see at least Maurer: Fig. 8 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 8-11: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending step towards the gradient direction.), and the updating the dual lambda variables (see at least Maurer: Fig. 8 & Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.).
- when the optimized solution fails to improve the objective function or a predefined number of iterations have been reached, assigning the optimized solution as a final optimized solution (see at least Maurer: Col. 14, Lns. 25-39 & Fig. 6 & Figs. 8-9. Maurer teaches that the second problem can include the multi-item, multi-time period problem, where the results of the single-item problems can be used as inputs and optimize the objective function (1) subject to the constraints (2)-(9). The former one can be referred to as the inner loop and is further illustrated in FIG. 5. The latter can be referred to as the outer loop. Because inner and outer loops may depend on the output of each other, these loops should be iteratively solved convergence to an optimal solution. See also Maurer at Col. 19, Lns. 4-15: The ordering model tool 320 can search for a target inventory level that may optimize the objective function. This search can be performed across the different inventory levels to come up with the inventory level that may provide an optimum result (e.g., maximize profit). In other words, the inner loop may cause the search to be repeated across the various inventory levels until a convergence criterion may be met, such as whether the objective function may be optimized or improvements to the objective function. Once met, the ordering model tool 320 can compare the corresponding inventory level to a current level and generate the ordering decision 530. See also Maurer at Col. 31, Lns. 65-67 and Col. 32, Lns. 1-9: Maurer teaches that if the discrepancy fails a convergence criterion, the adaptive control may determine an adjustment to the opportunity cost that may mitigate the discrepancy. This determination can use, for example, a sub-gradient algorithm that may search for Lagrange multipliers that can optimize an objective function used in simulating the consumption. The opportunity cost can be adjusted based on the step size and direction from the search. To illustrate, if the consumption overuses the capacity, the adaptive capacity control tool may increase the cost. Otherwise, a decrease may be performed.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella non-transitory computer-readable medium of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: the optimization problem comprising a plurality of decision variables, an objective function, and a corresponding Lagrangian relaxation, the solving to generate an optimized solution comprising determining a gradient of the objective function with respect to the plurality of decision variables, wherein the gradient comprises a direction and a magnitude of a steepest ascent or descent of the objective function & updating the plurality of decision variables based on a direction of the gradient & updating dual lambda variables of the Lagrangian relaxation and when the optimized solution improves the objective function, repeating the determining the gradient of the objective function, the updating the plurality of decision variables, and the updating the dual lambda variables & when the optimized solution fails to improve the objective function or a predefined number of iterations have been reached, assigning the optimized solution as a final optimized solution, and in further view of Maurer, whereby the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution can be found. This can allow the decoupling of the two loops. As a result, algorithms in each loop can be modified separately for improvements. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. Further, as the Lagrange multipliers for storage capacity could he interpreted as the value of having additional unit capacity, these values can be used in analysis of improving or designing new inventory facilities (see at least Maurer: (Col. 15, Lns. 30-43)).
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Maurer, the results of the combination were predictable.
Jean-Marc Tilly / Cella / Maurer non-transitory computer-readable medium of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Hirth in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- wherein the final optimized solution comprises an optimal inventory allocation of each of the group of substitutable products (see at least Hirth: Page 21, Lns. 14-31 & Page 22, Lns. 1-5 & Page 38, Lns. 1-7. Hirth teaches that The ATP service is connected to one or more of the programs that provide: (1) scheduling of the processes (e.g., to determine the actual date of fulfillment), and (2) product selection or substitution, partner/location selection, capable to promise (CTP) service, production planning and scheduling, planning in general (e.g., forecasts, product allocations) and alert handling (e.g., if there is no confirmation for a request). See also Hirth at Page 21, Lns. 24-31: The product selection or substitution service selects the correct product for a logistics process according to batches, serial numbers, shelf-life expiration date, and stock determination (i.e., type of stock: on hand, blocked, inspection, etc.). The product selection or substitution program also may substitute products based on predefined parameter (e.g., a listing of acceptable product substitutes) and connect the bill of materials to handling the bill of materials. For example, a customer may need a product that is unavailable in the time frame specified in the order. The program then can determine an acceptable substitute product based on parameters that the customer has provided for the product. See also Hirth at Page 22, Lns. 1-5: If that brand is unavailable, the program may substitute a different brand of paper that otherwise meets the customer's criteria based on parameters provided to the program. Like the scheduling service, the product selection or substitution service is connected to the ATP service for the transfer of data. See also Hirth at Page 38, Lns. 1-7: Allocation of articles to the warehouses can be beneficially optimized to reduce inventory costs.) from each of the plurality of different warehouses to each of the plurality of different retail stores (see at least Hirth: Page 37, Lns. 1-5 & Page 38, Lns. 13-27. Hirth teaches that the engine is used when delivering goods from central warehouses (i.e., warehouses with a full range of products) and individual warehouses (i.e., warehouses with partial product ranges) to multiple stores (e.g., supermarkets and retailers). See also Hirth at Page 38, Lns. 13-27: Hirth notes that the engine can be used to manage the logistics where multiple warehouses and vendors deliver to a store, but the store desires a single daily delivery. The engine also can be used to manage the logistics of the cross-docking of single article vendor and retail warehouse pallets, pre-picked retailer and vendor warehouse pallets/containers, and flow-through of handling units from inbound pallets to outbound containers for the stores. The logistics is controlled by the engine by having the warehouse platform that receives the goods being empty at night, using inbound deliveries of goods from other warehouses in the morning, and outbound delivery of goods to the retailers in the afternoon. In this manner, the cross-docking warehouse is empty at the end of the day. In this scenario, the engine is used to optimize the routes from warehouses or vendors that supply the goods to the cross-docking platforms, as well as optimize the routes from the cross-docking platforms to the retailers' stores.) and an optimal assortment of products to be distributed from each of the different warehouses to each of the retail stores (see at least Hirth: Page 35, Lns. 10-14 & Page 35, Lns. 16-30 & Page 37, Lns. 18-31. Hirth notes that the quantity of stock in the warehouse may be set according to the range of coverage of the store, assortment of stock, the store's programs to optimize layout and stocks in stores, and the reorder point. The shipments can be optimized based on routes and using only full truckloads. See also Hirth at Page 35, Lns. 16-30: To optimize the logistics, the amount and type of stock in the store is based on a range of coverage, an assortment, and the store's own programs to optimize layout and stocks in stores. See also Hirth at Page 37, Lns. 18-31: The fulfillment coordination engine also can be used to coordinate the logistics of fast- and slow-moving items in a cross-docking warehouse scenario, such as a retail warehouse service in which the engine coordinates the movement of goods from the vendor to the retailer's store. This scenario is a variation of the pull warehouse scenario, described above, as applied to retail businesses. In particular, this scenario includes situations in which there is a large assortment of goods and it is not worth warehousing all the goods in every warehouse.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer non-transitory computer-readable medium of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: wherein the final optimized solution comprises an optimal inventory allocation of each of the group of substitutable products from each of the plurality of different warehouses to each of the plurality of different retail stores and an optimal assortment of products to be distributed from each of the different warehouses to each of the retail stores, and in further view of Hirth, whereby the individual stores order all articles together from an organizing facility. A benefit of using the fulfillment coordination engine in this scenario is that there can be an optimization of routes from the slow-moving item warehouses to the fast-moving item warehouses, and from the fast-moving item warehouses to the stores. Another benefit is the optimization of the delivery to the store by using only one delivery for all the goods to each store. In addition, allocation of articles to the warehouses can be beneficially optimized to reduce inventory costs. The fulfillment coordination engine can be used for cross-docking delivery of goods for a warehouse service that manages retail goods by providing outbound delivery of the goods from the vendor to the retailer's store (see at least Hirth: (Page 38, Lns. 1-10)).
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hirth, the results of the combination were predictable.
Regarding Independent Claim 17, Jean-Marc Tilly system of optimizing inventory assortment and allocation of a group of substitutable products teaches the following:
- optimizing inventory assortment and allocation of a group of substitutable products, wherein the group of substitutable products are allocated from a plurality of different warehouses to a plurality of different retail stores (see at least Jean-Marc Tilly: Fig. 1 & Col. 5, Lns. 10-18 & Col. 7, Lns. 51-67. Jean-Marc Tilly notes that one or more supply chain entities 150 that identifies items as the mobile scanner passes by one or more items, such as, for example, a mobile robotic scanner which scans items on store shelves or products in a warehouse. See also Jean-Marc Tilly at Col. 7, Lns. 51-67. Jean-Marc Tilly notes that distribution centers 156 may comprise automated warehousing systems 157 that automatically remove products from and place products into inventory based, at least in part, on the mappings of one or more items in the supply chain networks, one or more pricing strategies, modification of a product assortment, or demand or sales forecasts generated by demand planner. See also Jean-Marc Tilly at Col. 12, Lns. 19-45: Business rules and constraints 230 comprise any rules or constraints that may be reflected in an optimization problem to generate a pricing plan. Business rules and constraints 230 may be added to the optimization problem to generate prices that incorporate, for example, product interactions, direct effect factors, cross-effect factors, and pricing, margin, size, brand, and competitor constraints. See also Jean-Marc Tilly at ¶ [abstract].), the system comprising one or more processors (see at least Jean-Marc Tilly: Fig. 1 & Col. 6, Lns. 23-31. Jean-Marc Tilly noting processor 132 in Fig. 1.) and a storage device (see at least Jean-Marc Tilly: Fig. 1 & Col. 6, Lns. 23-31 & Col. 6, Lns. 47-49. Jean-Marc Tilly notes storage device (e.g., memory) 134 shown in Fig. 1. Supply chain network 100 may comprise a cloud-based computing system having processing and storage devices at one or more locations.) that stores instructions that when executed by the one or more processors (see at least Jean-Marc Tilly: Fig. 1 & Col. 6, Lns. 23-31.) performs the optimizing comprising:
- receiving historical sales data for the group of substitutable products (see at least Jean-Marc Tilly: Col. 10, Lns. 54-67 & Col. 11, Lns. 22-46 & Fig. 5. Jean-Marc Tilly teaches that pricing data 222 may comprise historical prices, price changes, seasonality data, and the like. Pricing data 222 includes for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of one or more supply chain entities 150. See also Jean-Marc Tilly: Col. 10, Lns. 54-67: Jean-Marc Tilly teaches that categorization module 206 may define groups of items to be included in a particular category. Categories represent groupings of substitutable items. For example, a retailer may create categories comprising several substitutable retail products, where a change in the price of one product in the category will interact with the sales of other products in the same category.), wherein a first substitutable product and a second substitutable product in the group are substitutable products when they can be used in place of each other to satisfy a same need (see at least Jean Marc-Tilly: Col. 11, Lns. 54-67 & Col. 18, Lns. 9-22 & Fig. 5. Jean-Marc Tilly notes that any number of substitutable products may be grouped into any number of categories according to particular needs. Additionally, the groups of products assigned to particular categories may change over time by adding or removing products from categories based on, for example, changing product attributes, changing customer behavior, miscategorization of products, and the like. See also Jean-Marc Tilly at Col. 18, Lns. 4-18: Jean Marc Tilly notes that each of the three panels 502a-502c comprise boxes 504a-504c. Boxes 504a-504c illustrate substitutable products for the product under consideration. For example, as described in more detail below, first panel 502a illustrates the calculations when Product A 402 is the product under consideration. Accordingly, box 504a illustrates that Product B 404 and Product C 406 are the substitutable products when Product A 402 is the product under consideration. Similarly, box 504b in panel 502b illustrates that Product A 402 and Product C 406 are substitutable products when Product B 404 is the product under consideration, and box 504c in panel 502c illustrates that Product A 402 and Product B 404 are substitutable products when Product C 406 is the product under consideration. See also Jean-Marc Tilly at ¶ [abstract]: Jean Marc Tilly notes that sales forecast without using a cross elasticity by receiving a percentage pricing change for at least two substitutable products of an inventory in a supply chain network having one or more supply chain entities, and at least two substitutable products are grouped in the same product category and at least one of at least two substitutable products is grouped in a product assortment.);
- estimating demand model parameters of a demand model that models a demand of the group of substitutable products (see at least Jean-Marc Tilly: Fig. 2 & Fig. 5 & Col. 3, Lns. 14-24. Jean-Marc Tilly notes that the disclosed model comprises a demand model with product interaction due to base price changes which is more accurate than previous models based, at least in part, on the larger amount of data that is processed by the model when compared with models that include estimated cross elasticities. A demand planner executes a method that forecasts the sales for items in a category based on price changes of an item and one or more substitutable items, while eliminating the need to estimate cross elasticities for each pair of items. See also Jean-Marc Tilly at Col. 2, Lns. 36-61.), the estimating comprising grouping the historical sales data by merchandise and location hierarchies (see at least Jean-Marc Tilly: Fig. 2 & Col. 4, Lns. 11-26 & Col. 10, Lns. 51-67. Jean-Marc Tilly notes that demand planner 110 comprises one or more modules to, for example, define item groups and hierarchies, store and transmit product information, and calculate item and group elasticities, price changes, and direct and cross-effect factors. Server 122 of inventory system 120 is configured to receive and transmit inventory data, including item identifiers, pricing data, attribute data, inventory levels, and other like data about one or more items at one or more locations in the supply chain network 100. See also Jean-Marc Tilly at Col. 10, Lns. 61-67: Jean-Marc Tilly notes that categories are defined in a hierarchy comprising classes and sub-classes, which may be stored as hierarchy data 224 in database 114 or one or more databases associated with demand planner 110 or one or more supply chain entities 150. See also Jean-Marc Tilly at Fig. 2 noting “hierarchy data” 228.), wherein the demand model comprises a coefficient expressing change in demand due to product cannibalization caused by a change in the inventory assortment (see at least Jean-Marc Tilly: Fig. 5 & Col. 3, Lns. 14-24 & Col. 15, Lns. 33-53. Jean-Marc Tilly notes that the disclosed model comprises a demand model with product interaction due to base price changes which is more accurate than previous models based, at least in part, on the larger amount of data that is processed by the model when compared with models that include estimated cross elasticities. A demand planner executes a method that forecasts the sales for items in a category based on price changes of an item and one or more substitutable items, while eliminating the need to estimate cross elasticities for each pair of items. The disclosed model 40 considers cannibalization, or the effect of one item stealing sales from another item. Cannibalization may occur when a new product is introduced that reduces sales of another existing product or when a product reduces its base price and take sales from other substitutes perceived of lesser value. For example, when a name brand cola beverage company introduced a new zero-calorie cola beverage into the market, the new product stole more sales from the company's existing diet soda than from competitors in the marketplace. See also Jean-Marc Tilly at Col. 15, Lns. 63-67 & Col. 16, Lns. 1-4: The disclosed model may determine changes in demand for an item caused by a price change in another item, even when the items are in different categories. For example, if the price of milk greatly decreases, shoppers may additionally purchase almond milk, now that they can afford it. Accordingly, the price of almond milk would change. And even if the price went down a small amount, based on the larger budget, the shopper may still purchase more items. See also Jean-Marc Tilly noting at Fig. 5 for a coefficient expressing changes for demand of changes in inventory assortment.) and the demand model parameters comprises at least seasonality parameters (see at least Jean-Marc Tilly: Col. 4, Lns. 37-40 & Col. 11, Lns. 22-29. Jean-Marc Tilly notes that pricing data 222 may comprise historical prices, price changes, seasonality data, and the like. Pricing data 222 includes for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of one or more supply chain entities 150. See also Jean-Marc Tilly at Col. 4, Lns. 37-40: Jean-Marc Tilly notes that inventory database 124 may comprise explanatory variables that describe the data relating to specific past, current, or future indicators and the data of promotions, seasonality, special events (such as sporting events), weather, and the like.), price sensitivity parameters (see at least Jean-Marc Tilly: Col. 13, Lns. 20-40 & Col. 14, Lns. 39-46. Jean-Marc Tilly notes that it relates to price elasticity as the price elasticity of demand is the product of price sensitivity Bi and price Pi. See also Jean-Marc Tilly at Col. 14, Lns. 39-46: The elasticity may be estimated by the multiplication of a price sensitivity and the current price of the item. The direct effect factor indicates, for example, whether the sales of an item will increase or decrease based on the percentage price change of the item. The elasticity indicates how much sales change in response to changes in price.) and assortment parameters (see at least Jean-Marc Tilly: Fig. 3 & Fig. 5 & Col. 21, Lns. 18-21. Jean-Marc Tilly teaches that after demand planner 110 generates the calculations at action 320, demand planner 110 may calculate new prices for one or more retail items at action 322 and/or generate a new product assortment at action 324. See Jean-Marc Tilly at Fig. 3 step 324 “generate new product assortment.”)
Jean-Marc Tilly system of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Cella in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- solving an optimization problem for the inventory assortment (see at least Cella: ¶ [0338] & ¶ [0638-0639]. Cella notes that the set of facilities that provide automated recommendations for a set of value chain process tasks provide recommendations involving a wide range of types of activities, such as product assortment activities, product management activities, logistics activities, reverse logistics activities, artificial intelligence configuration activities, maintenance activities, product support activities, product recommendation activities. See also Cella at ¶ [0338]: For example, a supply chain or inventory management application in the value chain management platform 604, such as one for ordering replacement parts for a machine or item of equipment, may access the same data set about what parts have been replaced for a set of machines as a predictive maintenance application that is used to predict whether a component of a ship, or facility of a port is likely to require replacement parts. Similarly, prediction may be used with respect to the resupply of items. See also Cella at ¶ [0363]: The artificial intelligence systems 1160 may process the behavior data and conclude that there is a perceived need for greater consumer access to a second product in the category of goods 3010. This coordinated intelligence may be, optionally automatically, applied to the set of supply chain applications 812 so that, for example, production resources or other resources in the value chain for the category of goods are allocated to the second product. In examples, a distributor who handles stocking retailer shelves may receive a new stocking plan that allocates more retail shelf space for the second product, such as by taking away space from a lower margin product and the like. See also Cella at ¶ [1225] and ¶ [1245]: Solving an optimization problem is achieved through a deterministic policy gradient or the optimization method such as gradient descent to adjust weights and update the neural network characteristics.) and allocation of the group of substitutable products (see at least Cella: ¶ [0363] & ¶ [0747] & ¶ [1036] & ¶ [1616]. Cella notes that the demand of a product in the value chain network may be affected by factors like changes in consumer confidence, recessions, excessive inventory levels, substitute product pricing. See also Cella at ¶ [1038]: In a manufacturing enterprise, a CTO digital twin 8310 may depict where environment-compatible updates, upgrades, or substitutions may be available. See also Cella at ¶ [1616]: The artificial intelligence system 10212 may then predict and assess the impact of the predicted disruption to decide if a supply chain redesign may be required to minimize the disruption. Impact assessment and/or prediction may use a set of economic, financial or operating models, among many others, such as to assess primary, secondary, and other effects on an overall workflow or system. For example, assessment or prediction may include the impact on contract liability (such as liability for failure to deliver, including the obligation to pay for the cost of the buyer to cover in the marketplace by buying substitute items. See also Cella at ¶ [0363] noting “the value chain for the category of goods are allocated to the second product.”)), from the different warehouses to the different retail stores where the inventory assortment is given and demand distributions are known (see at least Cella: ¶ [0334] & ¶ [0439] & ¶ [0502] & ¶ [2604]. Cella teaches that the digital products 14512 may include products in a warehouse, packaging, environment sensors, or similar products. The products may generate data indicating a proximity of different products in the same warehouse or the presence of the same or different products in different warehouses. See also Cella at ¶ [0334]: The set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, a workforce management application 888 (such as for managing workers in various work forces, including work forces in, on or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise management locations and retail stores. See also Cella at ¶ [0439]: The set of demand management applications, supply chain applications, intelligent product applications and enterprise resource management applications may include, for example, ones involving supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation and demand customer profiling. See also Cella at ¶ [0502]: The set of supply chain applications and demand management applications includes, for example and without limitation one or more involving inventory management, demand prediction, product and service bundling, product assortment, upsell offer configuration, customer feedback engagement, customer survey, or others.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly system of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: solving an optimization problem for the inventory assortment and allocation of the group of substitutable products from the different warehouses to the different retail stores where the inventory assortment is given and demand distributions are known, and in view of Cella, whereby the additive manufacturing platform may address a “recall” situation by adding or revising a product in-warehouse, and may monitor for problems with vehicles, machines, tools, and other equipment being used and then replacing needed parts or materials “as needed,” creating tools on-demand as needed by workers or robots in warehouse/distribution network (see at least Cella: ¶ [1572]). An artificial intelligence system (e.g., a robotic process automation system trained on a training set of expert service visit data), to determine a recommended action, may involve replacement of a part and/or repair of a part, or some other activity. The platform may automatically determine that an element should be manufactured to facilitate repair, such as where a complementary component may be generated to replace a worn or absent element. Techniques utilized to achieve this include using AI to optimize product design, manufacturing process configuration (including packaging material generation process), job scheduling, prioritization and/or logistics (efficiency of warehouse processes for replacing parts, materials without disrupting other general processes involved in warehouse/distribution center) (see at least Cella: ¶ [1573].)
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cella, the results of the combination were predictable.
Jean-Marc Tilly / Cella system of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Maurer in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- the optimization problem comprising a plurality of decision variables (see at least Maurer: Figs. 8-9 & Col. 30, Lns. 55-67. Maurer notes that the optimization can follow the approach of solving constrained optimization problems, e.g., decoupling the optimization into multiple phases. See also Maurer at Col. 12, Lns. 61-67 noting the “decision variables that can be used”. See also Maurer at Col. 35, Lns. 28-32. Maurer teaches that the multi-period inventory optimization problem is solved with aggregate-level constraints on storage and receipt capacities. The described results are analyzed at an aggregate level and comparisons between items are drawn based on their attributes.), an objective function (see at least Maurer: Fig. 8 & Col. 13, Lns. 5-10. Maurer notes the objective function (1) subject to the constraints (2)-(9) shown at Col. 13, Lns. 5-10.), and a corresponding Lagrangian relaxation (see at least Maurer: Fig. 8 & Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.), the solving to generate an optimized solution comprising (see at least Maurer: Col. 14, Lns. 60-67 & Col. 15, Lns. 30-43 & Col. 24, Lns. 5-16.);
- determining a gradient of the objective function with respect to the plurality of decision variables (see at least Maurer: Fig. 8 & Col. 21, Lns. 49-67 & Col. 22, Lns. 1-18. Maurer notes the
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“Stochastic gradient of the objective function.” See also Maurer at Col. 21, Lns. 49-67. Maurer teaches that a stream of random variates for demand (di, ... , dr) and lead times (Ii, ... , Ir) may be generated. A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 12, Lns. 62-67.), wherein the gradient comprises a direction and magnitude of a steepest ascent or descent of the objective function (see at least Maurer: Fig. 8 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 7-18: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending step towards the gradient direction.)
- updating the plurality of decision variables based on a direction of the gradient (see at least Maurer: Figs. 8-9 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 7-18: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending
step towards the gradient direction.)
- updating dual lambda variables of the Lagrangian relaxation (see at least Maurer: Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to he
found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function
using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.).
- wherein the optimized solution improves the objective function (see at least Maurer: Col. 29, Lns. 60-67 & Col. 30, Lns. 1-5 & Fig. 8. Maurer notes that the simulation may use an objective function and can include an inner loop, where the ordering model tool 320 may simulate various inventory levels for an item of the item category and determine therefrom an optimization of the objective function. The inner loop may include an iterative simulation of the inventory level until a convergence criterion is satisfied, such as one that may optimize the objective function. See also Maurer: Col. 33, Lns. 53-57: Operations 812-816 may be iteratively repeated until a convergence criterion is met at operation 818. The convergence criterion may be based on the number of iterations and/or improvements to the objective function such as changes to the step sizes between iterations.), repeating the determining the gradient of the objective function (see at least Maurer: Fig. 8 & Col. 21, Lns. 49-67 & Col. 22, Lns. 1-18. Maurer notes the
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“Stochastic gradient of the objective function.” See also Maurer at Col. 21, Lns. 49-67. Maurer teaches that a stream of random variates for demand (di, ... , dr) and lead times (Ii, ... , Ir) may be generated. A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 12, Lns. 62-67.), the updating the plurality of decision variables (see at least Maurer: Fig. 8 & Col. 33, Lns. 46-49. Maurer teaches that at operation 816, the inventory level may be updated based on the gradient. For instance, a step size may be determined in the direction of the gradient and used to update the inventory level. See also Maurer at Col. 12, Lns. 62-67: “The following Decision variables can be used such as Target Inventory position of item I, quantity of item I and on-hand inventory units of item I at the end of time period t.”. See also Maurer at Col. 21, Lns. 51-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge. See also Maurer at Col. 22, Lns. 8-11: The crux of the method can include evaluating a stochastic gradient at each iteration using the realizations of the random variables at that iteration and taking an ascending step towards the gradient direction.), and the updating the dual lambda variables (see at least Maurer: Fig. 8 & Col. 14, Lns. 20-31 & Col. 15, Lns. 30-43. Maurer notes that for a given set of Lagrange multipliers, this problem can be decomposed into sub-problems for each item, resulting in multi-time period single item problems, Since the optimal values of the Lagrange multipliers, which can be used to relax and decompose the multi-item problem, may not be known, there can be two problems to solve. See also Maurer at Col. 15, Lns. 30-43: Maurer notes that the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution (e.g., a solution that may satisfy a convergence criterion) can be found. This can allow the decoupling of the two loops. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. See also Maurer at Col. 21, Lns. 50-55: A stochastic gradient of the objective function using the generated random variates can be calculated. A step in the gradient direction can be taken and the estimate for y can be updated. This process can be repeated until the estimates converge.).
- when the optimized solution fails to improve the objective function or a predefined number of iterations have been reached, assigning the optimized solution as a final optimized solution (see at least Maurer: Col. 14, Lns. 25-39 & Fig. 6 & Figs. 8-9. Maurer teaches that the second problem can include the multi-item, multi-time period problem, where the results of the single-item problems can be used as inputs and optimize the objective function (1) subject to the constraints (2)-(9). The former one can be referred to as the inner loop and is further illustrated in FIG. 5. The latter can be referred to as the outer loop. Because inner and outer loops may depend on the output of each other, these loops should be iteratively solved convergence to an optimal solution. See also Maurer at Col. 19, Lns. 4-15: The ordering model tool 320 can search for a target inventory level that may optimize the objective function. This search can be performed across the different inventory levels to come up with the inventory level that may provide an optimum result (e.g., maximize profit). In other words, the inner loop may cause the search to be repeated across the various inventory levels until a convergence criterion may be met, such as whether the objective function may be optimized or improvements to the objective function. Once met, the ordering model tool 320 can compare the corresponding inventory level to a current level and generate the ordering decision 530. See also Maurer at Col. 31, Lns. 65-67 and Col. 32, Lns. 1-9: Maurer teaches that if the discrepancy fails a convergence criterion, the adaptive control may determine an adjustment to the opportunity cost that may mitigate the discrepancy. This determination can use, for example, a sub-gradient algorithm that may search for Lagrange multipliers that can optimize an objective function used in simulating the consumption. The opportunity cost can be adjusted based on the step size and direction from the search. To illustrate, if the consumption overuses the capacity, the adaptive capacity control tool may increase the cost. Otherwise, a decrease may be performed.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella system of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: the optimization problem comprising a plurality of decision variables, an objective function, and a corresponding Lagrangian relaxation, the solving to generate an optimized solution comprising determining a gradient of the objective function with respect to the plurality of decision variables, wherein the gradient comprises a direction and a magnitude of a steepest ascent or descent of the objective function & updating the plurality of decision variables based on a direction of the gradient & updating dual lambda variables of the Lagrangian relaxation and when the optimized solution improves the objective function, repeating the determining the gradient of the objective function, the updating the plurality of decision variables, and the updating the dual lambda variables & when the optimized solution fails to improve the objective function or a predefined number of iterations have been reached, assigning the optimized solution as a final optimized solution, and in further view of Maurer, whereby the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution can be found. This can allow the decoupling of the two loops. As a result, algorithms in each loop can be modified separately for improvements. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. Further, as the Lagrange multipliers for storage capacity could he interpreted as the value of having additional unit capacity, these values can be used in analysis of improving or designing new inventory facilities (see at least Maurer: (Col. 15, Lns. 30-43)).
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Maurer, the results of the combination were predictable.
Jean-Marc Tilly / Cella / Maurer system of optimizing inventory assortment and allocation of a group of substitutable products does not explicitly disclose, but Hirth in the analogous art of optimizing inventory assortment and allocation of a group of substitutable products teaches the following limitations:
- wherein the final optimized solution comprises an optimal inventory allocation of each of the group of substitutable products (see at least Hirth: Page 21, Lns. 14-31 & Page 22, Lns. 1-5 & Page 38, Lns. 1-7. Hirth teaches that The ATP service is connected to one or more of the programs that provide: (1) scheduling of the processes (e.g., to determine the actual date of fulfillment), and (2) product selection or substitution, partner/location selection, capable to promise (CTP) service, production planning and scheduling, planning in general (e.g., forecasts, product allocations) and alert handling (e.g., if there is no confirmation for a request). See also Hirth at Page 21, Lns. 24-31: The product selection or substitution service selects the correct product for a logistics process according to batches, serial numbers, shelf-life expiration date, and stock determination (i.e., type of stock: on hand, blocked, inspection, etc.). The product selection or substitution program also may substitute products based on predefined parameter (e.g., a listing of acceptable product substitutes) and connect the bill of materials to handling the bill of materials. For example, a customer may need a product that is unavailable in the time frame specified in the order. The program then can determine an acceptable substitute product based on parameters that the customer has provided for the product. See also Hirth at Page 22, Lns. 1-5: If that brand is unavailable, the program may substitute a different brand of paper that otherwise meets the customer's criteria based on parameters provided to the program. Like the scheduling service, the product selection or substitution service is connected to the ATP service for the transfer of data. See also Hirth at Page 38, Lns. 1-7: Allocation of articles to the warehouses can be beneficially optimized to reduce inventory costs.) from each of the plurality of different warehouses to each of the plurality of different retail stores (see at least Hirth: Page 37, Lns. 1-5 & Page 38, Lns. 13-27. Hirth teaches that the engine is used when delivering goods from central warehouses (i.e., warehouses with a full range of products) and individual warehouses (i.e., warehouses with partial product ranges) to multiple stores (e.g., supermarkets and retailers). See also Hirth at Page 38, Lns. 13-27: Hirth notes that the engine can be used to manage the logistics where multiple warehouses and vendors deliver to a store, but the store desires a single daily delivery. The engine also can be used to manage the logistics of the cross-docking of single article vendor and retail warehouse pallets, pre-picked retailer and vendor warehouse pallets/containers, and flow-through of handling units from inbound pallets to outbound containers for the stores. The logistics is controlled by the engine by having the warehouse platform that receives the goods being empty at night, using inbound deliveries of goods from other warehouses in the morning, and outbound delivery of goods to the retailers in the afternoon. In this manner, the cross-docking warehouse is empty at the end of the day. In this scenario, the engine is used to optimize the routes from warehouses or vendors that supply the goods to the cross-docking platforms, as well as optimize the routes from the cross-docking platforms to the retailers' stores.) and an optimal assortment of products to be distributed from each of the different warehouses to each of the retail stores (see at least Hirth: Page 35, Lns. 10-14 & Page 35, Lns. 16-30 & Page 37, Lns. 18-31. Hirth notes that the quantity of stock in the warehouse may be set according to the range of coverage of the store, assortment of stock, the store's programs to optimize layout and stocks in stores, and the reorder point. The shipments can be optimized based on routes and using only full truckloads. See also Hirth at Page 35, Lns. 16-30: To optimize the logistics, the amount and type of stock in the store is based on a range of coverage, an assortment, and the store's own programs to optimize layout and stocks in stores. See also Hirth at Page 37, Lns. 18-31: The fulfillment coordination engine also can be used to coordinate the logistics of fast- and slow-moving items in a cross-docking warehouse scenario, such as a retail warehouse service in which the engine coordinates the movement of goods from the vendor to the retailer's store. This scenario is a variation of the pull warehouse scenario, described above, as applied to retail businesses. In particular, this scenario includes situations in which there is a large assortment of goods and it is not worth warehousing all the goods in every warehouse.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer system of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: wherein the final optimized solution comprises an optimal inventory allocation of each of the group of substitutable products from each of the plurality of different warehouses to each of the plurality of different retail stores and an optimal assortment of products to be distributed from each of the different warehouses to each of the retail stores, and in further view of Hirth, whereby the individual stores order all articles together from an organizing facility. A benefit of using the fulfillment coordination engine in this scenario is that there can be an optimization of routes from the slow-moving item warehouses to the fast-moving item warehouses, and from the fast-moving item warehouses to the stores. Another benefit is the optimization of the delivery to the store by using only one delivery for all the goods to each store. In addition, allocation of articles to the warehouses can be beneficially optimized to reduce inventory costs. The fulfillment coordination engine can be used for cross-docking delivery of goods for a warehouse service that manages retail goods by providing outbound delivery of the goods from the vendor to the retailer's store (see at least Hirth: (Page 38, Lns. 1-10)).
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hirth, the results of the combination were predictable.
Regarding Dependent Claims 2, 10 and 18, Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer-readable medium / system for optimizing inventory assortment and allocation of group of substitutable products teaches the limitations of Independent Claims 1, 9 and 17 above, and Maurer further teaches the method / non-transitory computer-readable medium / system for optimizing inventory assortment and allocation of group of substitutable products comprising:
- the solving further comprising: generating a randomized optimized solution before the determining the gradient (see at least Maurer: Col. 15, Lns. 5-10 & Col. 21, Lns. 45-67. Maurer notes that to solve the problem, stochastic approximation may be used. Briefly initial estimates for order-up-to levels (y 1, ... , yr) may be used. A stream of random variates for demand (di, ... , dr) and lead times (Ii, ... , Ir) may be generated. A stochastic gradient of the objective function using the generated random variates can be calculated. See also Maurer at Col. 15, Lns. 5-10: Given starting point
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. See also Maurer at Col. 21, Lns. 55-67: Further, let index k denote an index that keeps track of the iterations and n the index for scenarios. For the functions, let symbol "A " denote the value evaluated for particular realizations of the random variables (e.g., a path of demand and lead time). For instance, f, can be the objective function for a given set of random variates, vnf, can be the stochastic gradient obtained using scenario n.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer-readable medium / system of optimizing inventory assortment and allocation of a group of substitutable products with the aforementioned teachings of: the solving further comprising: generating a randomized optimized solution before the determining the gradient, and in further view of Maurer, whereby the algorithms implemented in the inner and outer loops can be run iteratively, while updating multipliers until an optimal solution can be found. This can allow the decoupling of the two loops. As a result, algorithms in each loop can be modified separately for improvements. The iterative search may also allow Lagrange multipliers for the inner loop to be found and updated on a timely basis in an automated manner, improving performance of the inner loop. Further, as the Lagrange multipliers for storage capacity could he interpreted as the value of having additional unit capacity, these values can be used in analysis of improving or designing new inventory facilities (see at least Maurer: (Col. 15, Lns. 30-43)).
Further, the claimed invention is merely a combination of old elements in a similar field of a method for optimizing inventory assortment and allocation of a group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Maurer, the results of the combination were predictable.
Regarding Dependent Claim 22, Jean-Marc Tilly / Cella / Maurer / Hirth system for optimizing inventory assortment and allocation of group of substitutable products teaches the limitations of Independent Claim 17 above, and Cella further teaches the system for optimizing inventory assortment and allocation of group of substitutable products comprising:
- wherein the inventory levels (see at least Cella: ¶ [0747] & ¶ [0933] & ¶ [1518] & ¶ [1543]. Cella notes that the demand of a product in the value chain network may be affected by factors like changes in consumer confidence, recessions, excessive inventory levels, substitute product pricing, overall market indices, currency exchange changes. See also Cella at ¶ [0933]: If a user is configuring an enterprise digital twin of a supply chain process, the user may identify an inventory management system to obtain inventory levels, various supplier systems to obtain pricing data of particular items, sensor systems to obtain sensor data from various points within the enterprise’s supply chain (e.g., manufacturing facilities, warehouse facilities, and the like). See also Cella at ¶ [1518]: Forecasting inventory levels based on a set of demand factors and/or supply factors of various types described herein and configuring schedules for additive manufacturing units 10102 to produce items for locations where shortages are anticipated. See also Cella at ¶ [1543]: A dashboard may provide visualization of demand factors, including predicted demand, inventory levels and the like.) are determined by attaching Internet of Things (IoT) sensors to inventory and tracking messages generated by the IoT sensors (see at least Cella: ¶ [0425] & ¶ [0489] & ¶ [0864]. Cella notes that robotic process automation 1442 is configured to train a set of physical robots that have hardware elements that facilitate undertaking tasks that are conventionally performed by humans. These may include robots that move about a facility, attach to items, lift items, carry items, remove and replace items. See also Cella at ¶ [0864]: The value chain monitoring systems layer 614 and its data collection systems 640 may include a wide range of systems for the collection of data from the maritime facilities 622 and the floating assets 620. This layer may include, without limitation, real time monitoring systems 1520 (such as onboard monitoring systems like event and status reporting systems on ships and other floating assets, on delivery vehicles, on trucks and other hauling assets; sensors and cameras 1950 and other IoT data collection systems 1172 (including onboard sensors, sensors or other data collectors (including click tracking sensors) in or about a value chain environment (such as, without limitation, a point of origin, a loading or unloading dock, a vehicle or floating asset used to convey goods, a container, a port, a distribution center, a storage facility, a warehouse, a delivery vehicle, and a point of destination). See also Cella at Fig. 33 also under ¶ [0489] noting “unified IoT monitoring systems” for monitoring inventory and tracking messages. See also Cella at ¶ [1404] & ¶ [1560].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer / Hirth system of optimizing inventory assortment and allocation of group of substitutable products with the aforementioned teachings of: wherein the inventory levels are determined by attaching Internet of Things (IoT) sensors to inventory and tracking messages generated by the IoT sensors, and in further view of Cella, whereby one goal of the unified set of Internet of Things systems may be coordination across a city or town involving citywide deployments where collectively a set of IOT devices may be connected by wide area network protocols (e.g., longer range protocols). In another example, the unified set of Internet of Things systems may involve connecting a mesh of devices across several different distribution facilities. The IoT devices may identify collection for each warehouse and the warehouses may use the IoT devices to communicate with each other. The IoT devices may be configured to process data without using the cloud (see at least Cella: ¶ [0491].)
Further, the claimed invention is merely a combination of old elements in a similar field optimizing inventory assortment and allocation of group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cella, the results of the combination were predictable.
11. Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent # (US 12,045,846 B1) to Jean-Marc Tilly, in view of US PG Pub (US 2023/0137578 A1) hereinafter Cella, et. al., and in view of US Patent # (US 9,805,402 B1) to Maurer, and in view of Foreign Patent Application (WO 03/075195 A2) hereinafter Hirth, et. al., and in further view of NPL Document: "A hierarchical Bayesian regression model that reduces uncertainty in material demand predictions." (2023), hereinafter Bhuwalka, Karan, et al.
Regarding Dependent Claims 5 and 13, Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer readable storage medium of optimizing inventory assortment and allocation of group of substitutable products does not explicitly disclose, but Bhuwalka, Karan, et al. in the analogous art of optimizing inventory assortment and allocation of group of substitutable products teaches the following limitations:
- wherein the estimating demand model parameters of the demand model comprises using Hierarchical Hamiltonian Monte-Carlo (see at least Bhuwalka, Karan, et al: (Pages 2-3 of NPL) & (Page 5 of NPL). Bhuwalka et. al. teaches that to estimate the parameters, we apply the No-U-Turn (NUTs) algorithm with PyMC3, a gradient-based extension to Hamiltonian Monte Carlo Sampling algorithm that improves efficiency of Markov chain Monte Carlo (MCMC) methods.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer readable storage medium of optimizing inventory assortment and allocation of group of substitutable products with the aforementioned teachings of: wherein the estimating demand model parameters of the demand model comprises using Hierarchical Hamiltonian Monte-Carlo, and in further view of Bhuwalka, Karan, et al, wherein priors are probability distributions that reflect existing knowledge or belief about uncertain value of a parameter. Priors add value in that it allows for a Bayesian model to estimate the credible interval for a parameter faster without having to search over the entire real number space. Priors inform the “first guess” of the parameter values, essentially improving parameter estimation when data are limited (see at least Bhuwalka, Karan, et al: (Page 4 of NPL Document)).
Further, the claimed invention is merely a combination of old elements in a similar field optimizing inventory assortment and allocation of group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Bhuwalka, Karan, et al, the results of the combination were predictable.
12. Claims 6-7, 14-15 and 21 are rejected under 35 U.S.C. 103 as being unpatentable US Patent # (US 12,045,846 B1) to Jean-Marc Tilly, in view of US PG Pub (US 2023/0137578 A1) hereinafter Cella, et. al., and in view of US Patent # (US 9,805,402 B1) to Maurer, and in view of Foreign Patent Application (WO 03/075195 A2) hereinafter Hirth, et. al., and in further view of US PG Pub (US 2009/0063251 A1) hereinafter Rangarajan, et. al.
Regarding Dependent Claims 6, 14 and 21, Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer readable storage medium / system of optimizing inventory assortment and allocation of group of substitutable products does not explicitly disclose, but Rangarajan, et. al in the analogous art of optimizing inventory assortment and allocation of group of substitutable products teaches the following limitations:
- wherein the plurality of decision variables (see at least Rangarajan: ¶ [0038] & ¶ [0147] & ¶ [0171]. Rangarajan notes that the demand model may be extended to derive optimal values and timings of several marketing instruments Mi by including the marketing instruments as additional decision variables. The Optimization solver uses the Mixed Integer Programming approach, selecting the optimal prices from among the price points determined in Price Point Generation. The decision variables are the indicator (0/1) variables corresponding to the allowed price points.) comprises inventory levels of the group of substitutable products (see at least Rangarajan: ¶ [0009] & ¶ [0060-0061] & ¶ [0072].), and the objective function comprises revenue (see at least Rangarajan: ¶ [0012] & ¶ [0131] & ¶ [0146]. Rangarajan notes that at step 170, the optimization objective function is determined. The optimization objective is formulated as a maximization of profit, or maximization of (revenue minus total cost). See also Rangarajan at ¶ [0012]: Other outputs are provided by a solver, based on the objective function, such as optimal profits over a plan horizon for the item, optimal revenue for the item. See also Rangarajan at ¶ [0146]: The objective function is modeled subject to both of the supply and demand-side constraints. The constraints may be generated based on metrics, which are converted into mathematical form. Constraints may also be based on data provided by the supply and demand business rules repository 238. Business rules in general are used to implement business policies. The repository 238 may include demand-side business rules such as margin, revenue, and cross item business rules.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer readable storage medium / system of optimizing inventory assortment and allocation of group of substitutable products with the aforementioned teachings of: wherein the plurality of decision variables comprises inventory levels of the group of substitutable products, and the objective function comprises revenue, and in further view of Rangarajan, et al, in addition with substitute components may be modeled in the supply-chain similarly to that of alternate resources. Components listed within a primary BOM are associated with a set of possible substitutes (see at least Rangarajan, et. al: ¶ [0072].) Additionally, optimization constraints are then generated for both supply-side constraints as well as demand-side constraints. The objective function is then solved subject to both of these supply and demand constraints (see at least Rangarajan, et. al: ¶ [0077].)
Further, the claimed invention is merely a combination of old elements in a similar field optimizing inventory assortment and allocation of group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Rangarajan, et al, the results of the combination were predictable.
Regarding Dependent Claims 7 and 15, Jean-Marc Tilly / Cella / Maurer / Hirth / Rangarajan method / non-transitory computer-readable medium for optimizing inventory assortment and allocation of group of substitutable products teaches the limitations of Claims 1, 6, 9 and 14 above, and Cella further teaches the method / non-transitory computer-readable medium for optimizing inventory assortment and allocation of group of substitutable products comprising:
- wherein the inventory levels (see at least Cella: ¶ [0747] & ¶ [0933] & ¶ [1518] & ¶ [1543]. Cella notes that the demand of a product in the value chain network may be affected by factors like changes in consumer confidence, recessions, excessive inventory levels, substitute product pricing, overall market indices, currency exchange changes. See also Cella at ¶ [0933]: If a user is configuring an enterprise digital twin of a supply chain process, the user may identify an inventory management system to obtain inventory levels, various supplier systems to obtain pricing data of particular items, sensor systems to obtain sensor data from various points within the enterprise’s supply chain (e.g., manufacturing facilities, warehouse facilities, and the like). See also Cella at ¶ [1518]: Forecasting inventory levels based on a set of demand factors and/or supply factors of various types described herein and configuring schedules for additive manufacturing units 10102 to produce items for locations where shortages are anticipated. See also Cella at ¶ [1543]: A dashboard may provide visualization of demand factors, including predicted demand, inventory levels and the like.) are determined by attaching Internet of Things (IoT) sensors to inventory and tracking messages generated by the IoT sensors (see at least Cella: ¶ [0425] & ¶ [0489] & ¶ [0864]. Cella notes that robotic process automation 1442 is configured to train a set of physical robots that have hardware elements that facilitate undertaking tasks that are conventionally performed by humans. These may include robots that move about a facility, attach to items, lift items, carry items, remove and replace items. See also Cella at ¶ [0864]: The value chain monitoring systems layer 614 and its data collection systems 640 may include a wide range of systems for the collection of data from the maritime facilities 622 and the floating assets 620. This layer may include, without limitation, real time monitoring systems 1520 (such as onboard monitoring systems like event and status reporting systems on ships and other floating assets, on delivery vehicles, on trucks and other hauling assets; sensors and cameras 1950 and other IoT data collection systems 1172 (including onboard sensors, sensors or other data collectors (including click tracking sensors) in or about a value chain environment (such as, without limitation, a point of origin, a loading or unloading dock, a vehicle or floating asset used to convey goods, a container, a port, a distribution center, a storage facility, a warehouse, a delivery vehicle, and a point of destination). See also Cella at Fig. 33 also under ¶ [0489] noting “unified IoT monitoring systems” for monitoring inventory and tracking messages. See also Cella at ¶ [1404] & ¶ [1560].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer / Hirth / Rangarajan method / non-transitory computer-readable medium of optimizing inventory assortment and allocation of group of substitutable products with the aforementioned teachings of: wherein the inventory levels are determined by attaching Internet of Things (IoT) sensors to inventory and tracking messages generated by the IoT sensors, and in further view of Cella, whereby one goal of the unified set of Internet of Things systems may be coordination across a city or town involving citywide deployments where collectively a set of IOT devices may be connected by wide area network protocols (e.g., longer range protocols). In another example, the unified set of Internet of Things systems may involve connecting a mesh of devices across several different distribution facilities. The IoT devices may identify collection for each warehouse and the warehouses may use the IoT devices to communicate with each other. The IoT devices may be configured to process data without using the cloud (see at least Cella: ¶ [0491].)
Further, the claimed invention is merely a combination of old elements in a similar field optimizing inventory assortment and allocation of group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cella, the results of the combination were predictable.
13. Claims 24-26 are rejected under 35 U.S.C. 103 as being unpatentable US Patent # (US 12,045,846 B1) to Jean-Marc Tilly, in view of US PG Pub (US 2023/0137578 A1) hereinafter Cella, et. al., and in view of US Patent # (US 9,805,402 B1) to Maurer, and in view of Foreign Patent Application (WO 03/075195 A2) hereinafter Hirth, et. al., and in further view of US PG Pub (US 2023/0081051 A1) hereinafter Brooks, et. al.
Regarding Dependent Claims 24-26, Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer readable storage medium / system of optimizing inventory assortment and allocation of group of substitutable products does not explicitly disclose, but Brooks, et. al in the analogous art of optimizing inventory assortment and allocation of group of substitutable products teaches the following limitations:
- wherein solving the optimization problem comprises assuming a zero-inflated Poisson distribution where the probability of drawing zero is parameterized as Ɵsl and the probability of drawing from Poisson (λsl) is 1- Ɵsl, which are specific for each item and location (see at least Brooks: Figs. 5-6 & ¶ [0040] & ¶ [0050] & ¶ [0084-0085]. Brooks teaches that the coefficients c′ are calculated based on the covariance in unit sales over time between products i and j when both are predicted to be available, and the variances σ2 are calculated based on the sales deltas relative to the uncertainty around the expected sales if all products were available. The expected sales of products i and j can be predicted using a regression model, such as a bagged zero inflated Poisson or random forest regression model, that uses sales and attribute data of other substitutable items in the set {z} as predictors. See also Brooks at ¶ [0040]: For each day, these conditional probabilities and uncertainties can be combined and weighted by the value K: I(t)−K*δI(t)=0. See also Brooks at ¶ [0059]: Dividing the difference between actual and predicted sales of {Y} when other products {Z} are unavailable by the average daily sales of {Z} yields approximate substitution ratios. These ratios can be converted into substitution probabilities by generating a normalized gaussian distribution with mean zero and variance equal to the daily unit sales variance of each product in {Z} by its average daily unit sales. See also Brooks at ¶ [0084-0085]: A multi-objective goals and weighting component 644 allows changing goals that may be location specific or may change with different corporate priorities, e.g., increase profit, increase traffic, establish long-term customer loyalty, and the like. The outputs of any of these functions can be parameters that are input to the synthetic world simulations 646, which provides numerous text and graphical presentation of simulation results. See also Brooks at Fig. 5 noting assortment optimization modeling and multi-objective optimization algorithms and Fig. 6 item #632 noting “synthetic world parameters that allow an end user to set up parameters for PoPs and products of interest”.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Jean-Marc Tilly / Cella / Maurer / Hirth method / non-transitory computer-readable medium of optimizing inventory assortment and allocation of group of substitutable products with the aforementioned teachings of: wherein solving the optimization problem comprises assuming a zero-inflated Poisson distribution where the probability of drawing zero is parameterized as Ɵsl and the probability of drawing from Poisson (λsl) is 1- Ɵsl, which are specific for each item and location, and in further view of Brooks, whereby aggregated substitution probabilities, a type of product unavailability effect, some embodiments of the assortment optimization framework estimate the change in total number of customers (lost when a product is removed, gained when a product is added) and the change in the retailer's other business goals when a new assortment replaces the existing assortment. The assortment optimization models of this disclosure provide the added technical advantage of scalability in their ability to leverage potentially rich, voluminous data to generate probability information that targets individual scenarios involving specific products that are unavailable, specific consumers' behaviors in response to these product unavailability instances (see at least Brooks: ¶ [0031].)
Further, the claimed invention is merely a combination of old elements in a similar field optimizing inventory assortment and allocation of group of substitutable products, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Brooks, the results of the combination were predictable.
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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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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