DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Status of the Claims
The pending claims in the present application are claims 1-6, 8-14, and 16-20, as presented in the Amendment filed 26 March 2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “system” of claims 1-6 and 8 constitutes a machine under 35 USC 101, the “method” of claims 9-14 and 16 constitutes a machine under the statute, and the “non-transitory computer readable medium” of claims 17-20 constitutes a manufacture under the statute. Accordingly, claims 1-6, 8-14, and 16-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“... obtain ... store layout data, product data and forecast data for a plurality of stores, the forecast data characterizing projected sales information for products described in the product data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtain ... demand transference data characterizing changes in demand for one or more products when a different product is unavailable; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtain ... product replenishment data characterizing a replenishment cost to re-stock products; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate a product sales function based on the forecast data, the demand transference data, and the product replenishment data, wherein the product sales function comprises a first parameter defining a product recommendation value, a second parameter defining a product benefit level, and a third parameter defining a demand transference value between products; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... determine a first function based on: upper and lower bounds of a number of copies to be displayed for each product, and a space for displaying a given number of copies of each product; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... apply a first limitation on the first function based on a maximum available display space in each store; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... determine a second function based on: the upper and lower bounds on the number of copies to be displayed for each product, and a replenishment cost for displaying a given number of copies of each product; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... apply a second limitation on the second function based on a maximum allowable replenishment cost; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... product assortment recommendation based at least by maximizing the product sales function subject to the first limitation on the first function and the second limitation on the second function; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... receive ... a request for product assortment recommendation comprising a timeframe and a product category; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate a recommended product assortment for the product category during the timeframe” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... the recommended product assortment for display” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: mathematical relationships, formulates, equations, and calculations, associated with various mathematical functions, which fall under the mathematical concepts grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: commercial interactions, including sales activities or behaviors associated with managing inventory, which falls under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “obtain” and “receive” steps), and evaluation, judgment, and/or opinion (e.g., the recited “generate,” “determine,” “apply,” “maximizing,” and “use” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
“A system for training a machine learning model, comprising: a processor; and a non-transitory memory storing instructions that, when executed, cause the processor to: ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “obtain ... store layout data” is “from a database and over a network” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “obtain ... demand transference data” is “from the database and over the network” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “obtain ... product replenishment data” is “from the database and over the network” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “product assortment recommendation” involves “train a machine learning model” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “receive ... a request” is “over the network” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generate” involves “use the trained machine learning model” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “recommended product assortment” undergoes “transmit, over the network” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, and mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to: receiving or transmitting data over a network, performing repetitive calculations, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-6 and 8, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “group the plurality of stores into two or more store clusters; receive the request for product assortment recommendation from a user ... ; determine the recommended product assortment for the product category during the timeframe for each store cluster; and display the recommended product assortment for each store cluster ... in response to the request” of claim 2, the “wherein the product sales function is described by:
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” of claim 3, the “obtain one or more assortment inputs, the one or more assortment inputs characterizing one or more strategic business considerations” of claim 4, the “wherein the one or more strategic business considerations comprises a weight of units sold versus revenue versus profit” of claim 5, the “allow a user to change a weight given to the product replenishment data when the recommended product assortment is determined” of claim 6, and the “wherein the two or more store clusters are determined based on one or more distribution limitations” of claim 8). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “system ... wherein the instructions, when executed, further cause the processor to: ... via an interactive electronic interface hosted by a server; use the trained machine learning model ...; and ... via the interactive electronic interface” of claim 2, the “system” of claim 3, the “system ... wherein the instructions, when executed, further cause the processor to display an input graphical user interface (GUI) that includes one or more input fields configured to” of claim 4, the “system” of claim 5, the “wherein the input graphical user interface (GUI) comprises a replenishment slider configured to” of claim 6, and the “system” of claim 8). Accordingly, claims 2-6 and 8 also are rejected as ineligible under 35 USC 101.
Regarding claims 9-14 and 16, while the claims are of different scope relative to claims 1-6 and 8, the claims recite limitations similar to the limitations of claims 1-6 and 8. As such, the rejection rationales applied to reject claims 1-6 and 8 also apply for purposes of rejecting claims 9-14 and 16. Claims 9-14 and 16 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding pending claims 17-20, while the claims are of different scope relative to claims 1-6 and to claims 9-14, the claims recite limitations similar to the limitations of claims 1-6 and 9-14. As such, the rejection rationales applied to reject claims 1-6 and 9-14 also apply for purposes of rejecting claims 17-20. Claims 17-20 are, therefore, also rejected as ineligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. 11,853,938 B1 to Muthusamy et al. (hereinafter referred to as “Muthusamy”), in view of U.S. Pat. No. 11,403,574 B1 to Rangarajan et al. (hereinafter referred to as “Rangarajan”), further in view of U.S. Pat. No. 10,445,810 B1 to Saad (hereinafter referred to as “Saad”), further in view of U.S. Pat. App. Pub. No. 2015/0199627 A1 to Gould et al. (hereinafter referred to as “Gould”), and further in view of U.S. Pat. App. Pub. No. 2007/0027745 A1 to Ouimet (hereinafter referred to as “Ouimet”).
Regarding claim 1, Muthusamy discloses the following limitations:
“A system for training a machine learning model, comprising: ...” - Muthusamy discloses, “A system” (Abstract), and “systems and methods of reinforced machine learning training and prediction” (col. 1, ll. 24 and 25).
“... a processor; and ...” - Muthusamy discloses, “One or more computers 180 may include one or more processors 186 and associated memory to execute instructions” (col. 8, ll. 55-57).
“... a non-transitory memory storing instructions that, when executed, cause the processor to: ...” - See the aspects of Muthusamy that have been cited above. The memory, instructions, and processors, of Muthusamy, read on the recited limitation.
“... obtain, from a database and over a network, store layout data, product data and forecast data for a plurality of stores, the forecast data characterizing projected sales information for products described in the product data; ...” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “network 190, and communication links 198a-198g” (col. 2, ll. 34 and 35), “any suitable number of servers or databases” (col. 3, ll. 16 and 17), “Stores 308a-308n of one or more retailers 150 may sell products according to rules, strategies, orders, and/or guidelines developed by one or more retail headquarters. For example, the retail headquarters may create product allocations, assign product allocations to stores 308a-308n or store clusters, and instruct one or more distribution centers 160 or other one or more supply chain entities 170 to supply products in the product allocation to stores 308a-308n or store clusters in an amount sufficient to meet an expected product need quantity, allocation quantity, or other determined quantity. One or more retailers 150 may comprise stores with shelving systems or other retail displays. Retail displays may comprise, for example, various racks, fixtures, brackets, notches, grooves, slots, or other attachment devices for fixing shelves or racks in various configurations. These configurations may comprise retail displays with adjustable lengths, heights, and other arrangements” (col. 6, ll. 40-57), “Product data 222 of database 114 may comprise one or more data structures comprising products identified by, for example, a product identifier, as disclosed above, and one or more attributes and attribute types associated with the product identifier, which may be stored as attribute data. Product data 222 may comprise any attributes of one or more products organized according to any suitable database structure, and sorted by, for example, attribute type, attribute, value, product identification, or any suitable categorization or dimension. Attributes of one or more items may be, for example, any categorical characteristic or quality of an item, and an attribute value may be a specific value or identity for the one or more items according to the categorical characteristic or quality” (col. 13, ll. 20-33), “Store data 224 of database 114 may comprise data describing stores 308a-308n of one or more retailers 150 and related store information. Store data 224 may comprise, for example, a store identifier, store description, store location details, store location climate, store type, store opening date, lifestyle, store area (expressed in, for example, square feet, square meters, or other suitable measurement), latitude, longitude, other geographic data, and the like” (col. 14, ll. 12-19), “market trends data 232 are calculated from sales data 220” (col. 15, ll. 29 and 30), and “market trends data 232 includes demand forecasts that may indicate future expected demand based on, for example, any data relating to past sales, past demand, purchase data, promotions, events, or the like of stores 308a-308n of one or more retailers 150” (col. 15, ll. 39-43). Obtaining, from databases over the network, store description data, including display configurations; product data; and trends data, including forecasts related to sales, for products, in Muthusamy, reads on the recited limitation. Additionally or alternatively, it should be noted that Rangarajan (see below) discloses, “The demand forecast data 214 includes information about projected sales for a particular item over a defined period of time” (col. 10, ll. 47-49), which explains characteristics of the demand forecasts of Muthusamy.
“... obtain, from the database and over the network, demand transference data characterizing changes in demand for one or more products when a different product is unavailable; ...” - See the aspects of Muthusamy that have been cited above. Obtaining, from the databases and over the network, market trends data including demand forecasts for products, in Muthusamy, reads on the recited limitation. Additionally or alternatively, Rangarajan (see below), discloses, “The demand transfer data 212 includes information relating to a customer’s willingness to substitute a desired item with an alternative item when the desired item is not available” (col. 10, ll. 35-38), which reads on the recited “demand transference data” and “when a different product is unavailable” limitations.
“... obtain, from the database and over the network, product replenishment data characterizing a replenishment cost to re-stock products; ...” - Muthusamy discloses, “These inventory policies may be based on target service level, demand, cost, fill rate, or the like” (col. 14, l. 66 to col. 15, l. 1). Obtaining, from the databases and over the network product data related to fill rates and costs for maintaining inventory levels, in Muthusamy, reads on the recited limitation.
“... generate a product sales function based on the forecast data, the demand transference data, and the product replenishment data, wherein the product sales function comprises ... a third parameter defining a demand transference value between products; ...” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “Prediction data 240 of database 114 comprise the constrained and/or unconstrained allocation quantities calculated by prediction module 206 based, at least in part, on independent variables 234, reward-penalty function 236, and what-if scenarios 238” (col. 16, ll. 29-33). Generating the reward-penalty function based on the demand forecasts, the other demand data, and product fill and cost data, including values associated therewith, in Muthusamy, reads on the recited limitation. Additionally or alternatively, Rangarajan (see below), discloses, “The demand transfer data 212 includes information relating to a customer’s willingness to substitute a desired item with an alternative item when the desired item is not available” (col. 10, ll. 35-38), which reads on the recited “demand transference data” and “third parameter defining a demand transference value between products” limitation.
“... receive, over the network, a request for product assortment recommendation comprising a timeframe and a product category; ...” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “FIG. 6 illustrates dashboard 600, in accordance with an embodiment. User interface module 208 of allocation planner 110 generates dashboard 600 comprising reinforcement learning execution button 602, filter 604, product list 606, clustering chart 608, and store list 612” (col. 26, ll. 31-35), “reinforcement learning execution button 602 generates constrained and/or unconstrained allocation quantities for a selected product at one or more allocation locations” (col. 26, ll. 36-39), “Product list 606 comprises identifier 612a (e.g. a product name), source 612b (e.g. a distribution center or other stocking location), brand 612c, supply quantity 612d, and expiration date 612e” (col. 26, ll. 43-47). Receiving, over the network, via the user interface, inputs for obtaining allocation quantities for products of products lists with expiration dates, in Muthusamy, reads on the recited limitation.
“... use the trained machine learning model to generate a recommended product assortment for the product category during the timeframe, and ...” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “handling product allocation, training a machine-learning model, predicting allocations using the trained machine-learning model” (col. 9, ll. 23-25). Using the trained machine learning model to generate product allocations, with consideration of their expiration dates, in Muthusamy, reads on the recited limitation.
“... transmit, over the network, the recommended product assortment for display.” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “FIG. 9 illustrates allocation map 700 of FIG. 7” and “In response to selection of an allocation location of one or more allocation locations 704, user interface module 208 updates map 700 to display analysis popup 900” (col. 28, ll. 22-26). The transmitting, over the network, of the data on product allocations, so they can be displayed on the allocation map interface, in Muthusamy, reads on the recited limitation.
The combination of Muthusamy and Rangarajan (hereinafter referred to as “Muthusamy/Rangarajan”) teaches limitations below of claim 1 that do not appear to be disclosed in their entirety by Muthusamy:
The claimed “demand transference data” for “when a different product is unavailable” - See the references to Muthusamy and Rangarajan above (and, in particular, the additional or alternative rationales). Rangarajan discloses, “The demand transfer data 212 includes information relating to a customer’s willingness to substitute a desired item with an alternative item when the desired item is not available” (col. 10, ll. 35-38).
The claimed “demand transference data” with “a third parameter defining a demand transference value between products” - See the references to Muthusamy and Rangarajan above (and, in particular, the additional or alternative rationales). See also the aspects of Rangarajan that have been cited above. The demand transfer data, with its information about the degree of willingness to substitute, in Rangarajan, reads on the recited limitation.
Rangarajan discloses “optimizing item assortments” (Abstract), similar to the claimed invention and to Muthusamy. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the demand and sales data, of Muthusamy, to include consideration of the demand transfer data, of Rangarajan, as “Such demand transfer data can inform the extent to which similar items, from a demand perspective, are stocked concurrently,” per Rangarajan (col. 10, ll. 39-41).
The combination of Muthusamy, Rangarajan, and Saad (hereinafter referred to as “Muthusamy/Rangarajan/Saad”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Muthusamy/Rangarajan:
The claimed “product sales function” also includes “a first parameter defining a product recommendation value, a second parameter defining a product benefit level” - Saad discloses, “This invention applies “causal theory,” as opposed to traditional correlative techniques, to provide, product (or service) suggestions and/or recommendations, customized for the specific individual consumer. Causation requires decision expertise, such as feedback loops and filtering relationships, as per the efficiency and/or effectiveness of particular product parameters, that is, attributes and benefits with respect to the evaluation information” (col. 2, ll. 30-37), “In step S365, the remaining selected offered products are sorted by their final product recommendation value, such that the product or products with the highest final product recommendation value are ranked above the product or products with the lowest final product recommendation value” (col. 13, ll. 18-23), and “information regarding each product’s or service’s attributes and/or benefits” (col. 18, ll. 63-65). The elements of the functions, in Muthusamy, when incorporating product recommendation values, and information regarding benefits of products, of Saad, read(s) on the recited limitation.
Saad discloses a “product recommendation expert system” (Abstract), similar to the claimed invention and to Muthusamy/Rangarajan. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the function parameters, of Muthusamy/Rangarajan, to include the parameters and information, of Saad, as such additional information allows for better specific tailoring of recommendations, per Saad (col. 2, l. 61).
The combination of Muthusamy, Rangarajan, Saad, and Gould teaches (hereinafter referred to as “Muthusamy/Rangarajan/Saad/Gould”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Muthusamy/Rangarajan/Saad:
“... determine a first function based on: upper and lower bounds of a number of copies to be displayed for each product, and a space for displaying a given number of copies of each product; ...” - Muthusamy discloses, “one or more modules of server 112 model an allocation horizon and decision points as a semi-Markov Decision Process (SMDP) with a reward-penalty function 236 that determines the unconstrained quantity (need quantity) or constrained quantity (allocation quantity) of short-life cycle products for stores of one or more retailers 150 and/or one or more distribution centers 160” (col. 2, ll. 48-54), “retailers 150 may comprise stores with shelving systems or other retail displays. Retail displays may comprise, for example, various racks, fixtures, brackets, notches, grooves, slots, or other attachment devices for fixing shelves or brackets in various configurations. These configurations may comprise retail displays with adjustable lengths, heights, and other arrangements, which may be adjusted by an employee of retailers based on computer-generated instructions or automatically by machinery to place products in a desired location in one or more retailers 150. According to embodiments, one or more retailers 150 retain some quantity or one or more products for display by the retail displays in stores 308a-308n. This quantity of one or more products held for display by a retail store may be referred to as the minimum presentation quantity” (col. 6, ll. 51-65), and “After determining independent variables 234 that are responsible for sales of the short-life cycle products, training module 204 calculates reward-penalty function 236 from independent variables 234 for historical allocations” (col. 12, ll. 23-26). Calculating the function based on the minimum presentation quantity, and the dimensions of the retail display for displaying the quantities of products, in Muthusamy, reads on the recited “determine a first function based on: ... lower bounds of a number of copies to be displayed for each product, and a space for displaying a given number of copies of each product” limitation. Gould discloses, “product minimums to be displayed and product maximums” (para. [0027]). The additional consideration of product maximums, in Gould, read on the recited “upper ... bounds” limitation.
“... apply a first limitation on the first function based on a maximum available display space in each store; ...” - See the aspects of Muthusamy and Gould that have been cited above. Gould also discloses, “Aijmax is the maximum area to be allocated to a product” (para. [0038]). Using the maximum areas to be allocated to products, as in Gould, with the function, in Muthusamy, reads on the recited limitation.
Gould discloses “retail establishment technology” that is “for a optimizing” (para. [0002]), similar to the claimed invention and to Muthusamy/Rangarajan/Saad. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the consideration of product quantity bounds, of Muthusamy/Rangarajan/Saad, to have included consideration of both product minimums and maximums, as in Gould, for playing a role in making comprehensive optimization determinations, per Gould (paras. [0023] and [0027]).
The combination of Muthusamy, Rangarajan, Saad, Gould, and Ouimet (hereinafter referred to as “Muthusamy/Rangarajan/Saad/Gould/Ouimet”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Muthusamy/Rangarajan/Saad/Gould:
“... determine a second function based on: the upper and lower bounds on the number of copies to be displayed for each product, and a replenishment cost for displaying a given number of copies of each product; ...” - See the aspects of Muthusamy and Gould that have been cited above. Muthusamy also discloses, “Stock out Penalty” and “Inventory Handling Cost” (FIG. 6). Calculating the function based on the minimum presentation quantity, and the stockout penalties and the inventory handling costs associated with the displayed products, in Muthusamy, reads on the recited “determine a first function based on: ... lower bounds of a number of copies to be displayed for each product, and a ... cost for displaying a given number of copies of each product” limitation. The additional consideration of product maximums, in Gould, read on the recited “upper ... bounds” limitation. It is not explicitly stated in Muthusamy as to whether the stock out penalty and inventory handling cost are, or constitute, replenishment costs. They likely are, under broadest reasonable interpretation. For clarification, however, the examiner also cites Ouimet. Ouimet discloses, “The shelf replenishment costs are given in equations (5) and (6). Shelf capacity (SC) is the maximum units stored on a shelf. In equation (5), shelf capacity is a function of facings and facing capacity (FC). In equation (6), shelf replenishment frequency is a function of unit sales and shelf capacity” (para. [0036]), and “Shelf replenishment costs are generally linear with shelf replenishment frequency, although the slope of the function differs between night and day. Day costs are generally higher and will cause a greater slope for shelf replenishment costs” (para. [0037]). The consideration of inventory handling costs in the form of shelf replenishment costs (tied to, among other things, maximum units stored on shelves), in Ouimet, when applied in the context of consideration of inventory handling costs, in Muthusamy, reads on the recited limitation.
“... apply a second limitation on the second function based on a maximum allowable replenishment cost; ...” - See the aspects of Muthusamy and Ouimet that have been cited above. Ouimet also discloses, “the product decision modeling tool simultaneously optimizes each of the multiple product decision variables by iteratively resolving the objective function from equations (9)-(11) into values which optimize sales, revenue, and profit for retailer 12. Maximizing the objective function 8 as described above will optimize these parameters for the retail store.” (para. [0076]). Using the inventory handling costs (of Muthusamy) and the related shelf replenishment costs (of Ouimet) with the functions (of Muthusamy and Ouimet), to determine optimal costs (per Ouimet), reads on the recited limitation. When the parameter of replenishment cost is optimized, the value is the maximum allowable value (i.e., the value that is sought for optimality). Further, use of the term “allowable” introduces a level of subjectivity to the claim limitation that permits a multitude of broad interpretations.
“... train a machine learning model for product assortment recommendation based at least by maximizing the product sales function subject to the first limitation on the first function and the second limitation on the second function.” - See the aspects of Muthusamy and Ouimet that have been cited above. Ouimet also discloses, “providing an objective function that utilizes the rules and constraints for the multiple product decision variables, and simultaneously modeling each of the multiple product decision variables by iteratively resolving the objective function into value which optimize sales” (para. [0010]). Training the machine learning to generate optimal product allocations based on parameters, constraints, and functions, as in Muthusamy, wherein the parameters and functions also includes those for optimizing sales, in Ouimet, reads on the recited limitation.
Ouimet discloses “modeling of product decisions in a retail store” (Abstract), similar to the claimed invention and to Muthusamy/Rangarajan/Saad/Gould. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the functions, parameters, and constraints, in Muthusamy/Rangarajan/Saad/Gould, to further include the functions, parameters, and constraints (including replenishment costs), of Ouimet, for optimization purposes, as taught by Ouimet (see Abstract).
Regarding claim 2, Muthusamy/Rangarajan/Saad/Gould/Ouimet teaches the following limitations:
“The system of claim 1, wherein the instructions, when executed, further cause the processor to: group the plurality of stores into two or more store clusters; ...” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “the retail headquarters may create product allocations, assign product allocations to stores 308a-308n or store clusters” (col. 6, ll. 43-35), and “stores 308a-308n grouped by store profiles into one or more store clusters” (col. 14, ll. 20 and 21). The instructions, when executed, causing the processor to group stores by their profiles into store clusters, in Muthusamy, reads on the recited limitation.
“... receive the request for product assortment recommendation from a user via an interactive electronic interface hosted by a server; ...” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “a server comprising a processor and memory” (Abstract), and “user interface module 208 displays a GUI comprising interactive graphical elements for generating and viewing mappings, charts, and calculated quantities of constrained and unconstrained allocation quantities, and the like. In addition, or as an alternative, the GUI user interface module 208 may comprise interactive elements for selecting or modifying one or more items, clusters, variables, model parameters, cost factors, constraints, casual factors, such as, for example, sales, previous allocations, market trends, customer behaviors, inventory positioning, and profit optimization, and, in response to the selection or modification calculating the need quantity or allocation quantity for supply chain network 100” (col. 12, ll. 42-54). Users utilizing the GUI to enter inputs for obtaining allocation quantities via the GUI and its server, in Muthusamy, reads on the recited limitation. See also, FIG. 6 of Muthusamy, for more GUI details.
“... use the trained machine learning model to determine the recommended product assortment for the product category during the timeframe for each store cluster; and ...” - See the aspects of Muthusamy that have been cited above. Using the trained machine-learning model to determine the optimal allocation of products for the store clusters, including items with expiration dates, in Muthusamy (see also, FIG. 6), reads on the recited limitation.
“... display the recommended product assortment for each store cluster via the interactive electronic interface in response to the request.” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “In response to selection of an allocation location of one or more allocation locations 704, user interface module 208 updates map 700 to display analysis popup 900” (col. 28, ll. 22-26). Displaying the calculated allocation quantities for multiple selected allocation locations via the GUI, in response to the inputs received via the other GUIs, in Muthusamy, reads on the recited limitation.
Regarding claim 3, Muthusamy/Rangarajan/Saad/Gould/Ouimet teaches the following limitations:
“The system of claim 2, wherein the product sales function is described by:
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28
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” - See the aspects of Muthusamy that have been cited above. The reward-penalty function, machine learning model, and regression model, used to generate outputs including values, in Muthusamy, reads on the recited limitation. The combination of functions and models is “described by” the equation above whenever output values of the functions and models overlaps with output values of the equation. It appears that those values can be as simple as one or more whole numbers.
Regarding claim 4, Muthusamy/Rangarajan/Saad/Gould/Ouimet teaches the following limitations:
“The system of claim 2, wherein the instructions, when executed, further cause the processor to display an input graphical user interface (GUI) that includes one or more input fields configured to obtain one or more assortment inputs, the one or more assortment inputs characterizing one or more strategic business considerations.” - See the aspects of Muthusamy that have been cited above. The system displaying the GUI, wherein the GUI includes interactive elements for selecting or modifying parameters for calculating allocations for optimizing metrics, in Muthusamy, reads on the recited limitation.
Regarding claim 5, Muthusamy/Rangarajan/Saad/Gould/Ouimet teaches the following limitations:
“The system of claim 4, wherein the one or more strategic business considerations comprises a weight of units sold versus revenue versus profit.” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “Received product data 222 may include an image of the item, an identifier, as described above, and/or other data associated with the item (dimensions, texture, estimated weight, and any other like data)” (col. 11, ll. 11-14), “each of the clusters are classified based on sales, revenue, and the like” (col. 26, ll. 66 and 67), and “the calculated profit expected for various allocation quantities to the selected store of allocation location 704 including the optimal need quantity which achieve the maximum profit or reward” (col. 28, ll. 30-33). Considering estimated weights of products, revenues, and calculated profits, in Muthusamy, reads on the recited limitation.
Regarding claim 6, Muthusamy/Rangarajan/Saad/Gould/Ouimet teaches the following limitations:
“The system of claim 4, wherein the input graphical user interface (GUI) comprises a replenishment slider configured to allow a user to change a weight given to the product replenishment data when the recommended product assortment is determined.” - See the aspects of Muthusamy that have been cited above, including the GUI features (e.g., col. 12, ll. 42-54). Rangarajan discloses, “In the example user interface 600 of FIG. 6, a goal slider 622 is displayed that allows a user U to select a balance of sales and margins as the goal for the optimized item assortment” (col. 14, ll. 3-6). The goal slider for selecting the balance of parameters for determining the optimized item assortment, in Rangarajan, reads on the recited limitation. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the GUI features, of Muthusamy, to include the goal slider, of Rangarajan, to provide users with a means for selecting and balancing parameters, as taught by Rangarajan. The rationales for combining the teachings of the cited references, from the rejection of independent claim 1 above, also apply for purposes of rejecting claim 6.
Regarding claim 8, Muthusamy/Rangarajan/Saad/Gould/Ouimet teaches the following limitations:
“The system of claim 2, wherein the two or more store clusters are determined based on one or more distribution limitations.” - See the aspects of Muthusamy that have been cited above. Muthusamy also discloses, “location cluster (a cluster of stores 308a-308n of one or more retailers 150 that have similar customer behavior or characteristics, climate, market trends, store size, or other types of store clustering factors)” (col. 15, ll. 58-62). The store clustering being based on various store clustering factors, in Muthusamy, reads on the recited limitation.
Regarding claims 9-14 and 16, while the claims are of different scope relative to claims 1-6 and 8, the claims recite limitations similar to those recited by claims 1-6 and 8. As such, the rationales applied in the rejection of claims 1-6 and 8 also apply for purposes of rejecting claims 9-14 and 16. Claims 9-14 and 16 are, therefore, also rejected under 35 USC 103 as obvious in view of Muthusamy/Rangarajan/Saad/Gould/Ouimet.
Regarding claims 17-20, while the claims are of different scope relative to claims 1-6 and 9-14, the claims recite limitations similar to those recited by claims 1-6 and 9-14. As such, the rationales applied in the rejection of claims 1-6 and 9-14 also apply for purposes of rejecting claims 17-20. Claims 17-20 are, therefore, also rejected under 35 USC 103 as obvious in view of Muthusamy/Rangarajan/Saad/Gould/Ouimet.
Response to Arguments
On pp. 10-13 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 103. More specifically, the applicant contends that Muthusamy and the other cited references do not disclose, teach, or suggest that the independent variables, reward-penalty function, and what-if scenarios characterize projected sales information, changes in demand for products when different products are unavailable, and characterize replenishment cost. (Amendment, p. 11.) The examiner finds the contentions unpersuasive. The above-listed aspects of Muthusamy, along with others, teach the claim limitations. As explained in more detail above (with supporting citations), obtaining, from databases over the network, store description data, including display configurations; product data; and trends data, including forecasts related to sales, for products, in Muthusamy, reads on the recited projected sales information and demand for products. Further, obtaining, from the databases and over the network, market trends data including demand forecasts for products, in Muthusamy, reads on the recited demand. Rangarajan discloses demand transfer data about substituting desired items with alternative items in instances of unavailability, which reads on the changes in demand for products when different ones are unavailable. Obtaining, from the databases and over the network product data related to fill rates and costs for maintaining inventory levels, in Muthusamy, reads on replenishment costs.
The applicant also contends that Muthusamy and the other cited references do not disclose, teach, or suggest the claimed first, second, and third parameters. (Amendment, p. 11.) The examiner finds the contentions unpersuasive. See the 35 USC 103 section above regarding the disclosures and teachings of Saad. Aspects of Saad read on the first and second parameters. Values associated with demand transfer, in Muthusamy and Rangarajan, read on the third parameter.
The applicant also contends that the cited references do not disclose, teach, or suggest receiving the product assortment recommendation request, timeframe, and product category. (Amendment, p. 12.) The applicant also contends that the cited references do not disclose, teach, or suggest using the trained machine learning model to generate product assortment recommendation responses for product categories during the timeframe. (Amendment, p. 12.). The examiner finds the contentions unpersuasive. The combination of the trained machine learning model making allocation recommendations, based on the product category and expiration date information in FIG. 6, in Muthusamy, reads on the recited limitation.
The applicant also contends that Ouimet does not disclose, teach, or suggest the claimed training of a machine learning model for product assortment recommendation based on maximizing a product sales function subject to a first limitation on a first function and a second limitation on a second function. (Amendment, p. 12.) The examiner finds the contentions unpersuasive. The cited aspects of Ouimet, used in combination with, and in the context of, the cited aspects of the other cited references (and Muthusamy, in particular), read(s) on the recited limitation.
On pp. 13-17 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. The applicant contends that rationales from Ex Parte Desjardin call for eligibility. (Amendment, pp. 14 and 15.) The applicant also contends that the claims do not set forth mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. (Amendment, p. 15.) The applicant also contends that the claims provide technical improvements, including the training of a machine learning model and use thereof to address technical problems in prior art methodologies. (Amendment, p. 15.) The applicant also emphasizes rationales from Ex Parte Carmody. (Amendment, p. 16.) The applicant also contends that the steps performed via the network, trained machine learning model, and transmissions for display, are grounds for eligibility. (Amendment, p. 16.) The applicant also contends that the claims recite improvements to computers, technology, or a technical field (Amendment, p. 16.), the claims recite limitations that are not well-understood, routine, conventional activity (Amendment, p. 17), and the claimed subject matter is confined to a particular useful application (Amendment, p. 17).
The examiner finds the contentions above unpersuasive. Ex Part Desjardins involves a claim directed to a method of training a machine learning model that is an improvement to how the machine learning model operates. The applicant’s claims, on the other hand, recite a method of training a machine learning model without reciting or otherwise explaining how such training improves how the machine learning model operates. Training machine learning models is a step in virtually all generic, conventional machine learning. The applicant has not established that the claimed training is an improvement, and instead, just establishes that training takes place. The applicant’s claims read like training machine learning models to use them to recommend product assortments. In such scenarios, the eligibility rationales of Ex Parte Desjardins do not apply. Similarly, eligibility rationales from Ex Parte Carmody also do not apply to the applicant’s claims because the applicant’s claims do not recite how the ML system itself is technically improved, with support in the specification. The applicant’s claims and specification lack any discussion of improvements to ML training, architecture, modularity, or deployability. Features and operation of generic, conventional AI or ML are not eligibility-warranting improvements to AI or ML. Further, the examiner contends that the recited limitations about various functions, in the applicant’s claims, set forth mathematical relationships. And, as explained in the 35 USC 101 section above, some additional elements of the claims are well-understood, routine, conventional activity, while other additional elements do not warrant eligibility for other reasons. The well-understood, routine, conventional activity rationale is only being applied against the most back computer hardware and network aspects of the claims.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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