DETAILED ACTION
This final Office action is responsive to Applicant’s reply filed February 2, 2026. No claims have been amended. Claims 1, 3, 7-11, 62, 64, 68-72, 123, 125, and 129-132 are presented for examination.
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 .
Response to Arguments
Applicant's arguments filed February 2, 2026 have been fully considered but they are not persuasive.
On page 5 of the response, Applicant argues, “‘…Specifically, it is a technical improvement to execute this coordination in some embodiments because if each operational component were merely optimized independently, they would inevitably conflict with each other.’ US PG-Pub Specification ¶¶ 0045-46. ‘The independent knowledge generation objectives and optimization goals would collide, and optimization goals from different operational components would conflict.’, Id. at I 0046, the claims are patent eligible as were the claims held patent eligible in Enfish.” As stated in paragraph 18 of Applicant’s Specification, “[o]perational components discussed herein, by way of non-limiting example, relate to out-of-stock, assortment, in-store product location, planogram design, customer substitution behaviors, and purchase patterns.” Additionally, “a non-limiting example includes two operational components, out-of-stock and in-store product location.” (Spec: ¶ 19) The operational components are simply aspects of operations and the operations are not necessarily limited to technical operations, as seen in Applicant’s disclosure. A human user can optimize the operational components. Furthermore, Applicant does not point to any specific claim limitations that present a technical solution to a technical problem.
On page 5 of the response, Applicant further submits that the claims do not preempt any abstract ideas. Preemption is not a standalone test for patent eligibility. Preemption concerns have been addressed by the Examiner through the application of the Subject Matter Eligibility test. Applicant’s attempt to show that the recited abstract idea is a very narrow and specific one is not persuasive. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim.
On page 6 of the response, Applicant states, “It is a technical improvement to ‘continue to observe and improve the causational data sets, thus determining changes in causational relationships in near real-time..’ Specification ¶ 0064.” The Examiner respectfully disagrees. A human user can “continue to observe and improve the causational data sets, thus determining changes in causational relationships in near real-time..” and the data sets may be related to technical or non-technical data. The Examiner does not find details of a technical improvement in this cited portion of the Specification or in the claims themselves.
On pages 6-8 of the response, Applicant makes general assertions that the combination of elements amounts to significantly more than the abstract idea and requests supporting evidence for the additional elements that are well-understood, routine, and conventional. Applicant does not present any specific arguments as to how the combination of elements amounts to significantly more than the abstract idea. In the rejection, the Examiner has already stated that she considered the combination of additional elements. Additionally, the Examiner did not make an assertion that any of the additional elements were well-understood, routine, and conventional and the Examiner also did not rely on Official Notice in the rejection. Therefore, no related evidence is required.
Applicant argues that Ghosh’s queuing theory is not the same as the claimed queue optimization model (page 9 of Applicant’s response). First, the Examiner submits that queuing theory is a specific type of queue optimization model. Second, Ghosh discloses that queueing theory and queuing analytics and optimization techniques may be used to prioritize items in a production line, including in made-to-stock and made-to-order situations (Ghosh: abstract, ¶ 31). In other words, Ghosh uses both of the terms queuing theory and queueing optimization techniques (i.e., models).
On pages 9-10 of Applicant’s response, Applicant argues:
Moreover, claim 1 recites "applying the plurality of control signals to the plurality of operational components to determine an operational effect of the plurality control signals on the operational components." Mahalanobish " 0060, 0075, and 0140 are cited to allegedly disclose this. Mahalanobish essentially evaluates the application of control signals (possible assortment changes) then decide which to recommend for application, but there is no requirement that any recommended action is taken. By contrast, actions in the present application that are recommended are taken to study the dynamics of a complex system where available resources within and across stores may be applied to different operational components. Some recommended actions may be to do nothing, but there are many other options, which is not disclosed in Mahalanobish.
As explained in the rejection, regarding “control signals”, Applicant’s Specification states, “Based on multi-objective optimization, control signals (e.g., fix or do-not-fix and the like) may be varied across stores or even within stores at different times to estimate confidence intervals on the value of fixing or not fixing an out-of-stock situation.” (Spec: ¶ 68) Paragraph 48 of Applicant’s Specification explains that “using an integrated, continuous, adaptive clinical trial, the control signals may be prescribed (e.g., replenish a specific SKU at a specific location within a specific store) within and across retail store locations, and there are several computational/algorithmic systems that could be used to execute such components (e.g., deep reinforcement learning, multi-arm bandits, standard adaptive clinical trial logic).” In other words, “control signals” can be any sort of recommendation or prescribed instruction. Mahalanobish recommends actions and enables implementation of such actions. For example, Mahalanobish states, “The assortment recommendation 132 may include a recommendation to accept or implement the proposed item assortment 122 or a recommendation to reject the proposed item assortment 122. In this manner, the assortment recommendation component 126 identifies improper assortment changes prior to the assortment changes being implemented in a store.” (Mahalanobish: ¶ 64) In other words, some recommendations may be actively implemented and some may not be. Additionally, the Examiner does not find “actions in the present application that are recommended are taken to study the dynamics of a complex system where available resources within and across stores may be applied to different operational components” presented in the claims.
The rejections are maintained.
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, 3, 7-11, 62, 64, 68-72, 123, 125, and 129-132 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1, 3, 7-11, 62, 64, 68-72, 123, 125, and 129-132 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to assigning an operational resource allocation to operation components without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1, 3, 7-11), Apparatus (claims 62, 64, 68-72), Article of Manufacture (claims 123, 125, and 129-132)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 62, 123] A method/operations comprising:
selecting a plurality of operational components to which operational resources are allocated;
selecting a plurality of control signals for the plurality of operational components;
applying the plurality of control signals to the plurality of operational components to determine an operational effect of the plurality control signals on the operational components;
applying a resource requirement for each of the plurality of operational components;
determining, using a queue optimization model, a first control signal target value, based on the operational effect of each of the plurality of control signals on each of the plurality of operational components and the resource requirement for each of the plurality of control signals for each of the plurality of operational components; and
assigning, based on the first control signal target value, an operational resource allocation to each of the plurality of operational components.
NOTE: Regarding “control signals”, Applicant’s Specification states, “Based on multi-objective optimization, control signals (e.g., fix or do-not-fix and the like) may be varied across stores or even within stores at different times to estimate confidence intervals on the value of fixing or not fixing an out-of-stock situation.” (Spec: ¶ 68) Paragraph 48 of Applicant’s Specification explains that “using an integrated, continuous, adaptive clinical trial, the control signals may be prescribed (e.g., replenish a specific SKU at a specific location within a specific store) within and across retail store locations, and there are several computational/algorithmic systems that could be used to execute such components (e.g., deep reinforcement learning, multi-arm bandits, standard adaptive clinical trial logic).” In other words, “control signals” can be any sort of recommendation or prescribed instruction.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user may perform the operations identified above (e.g., selecting, applying, determining, assigning, etc.). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to assigning an operational resource allocation to operational components, which (under its broadest reasonable interpretation) is an example of sales activities, especially given that the Specification and several of the dependent claims (including claims 7-11, 68-72, and 129-132) focus on inventory management (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Independent claims 1, 62, and 123 use a queue optimization model to make a determination and this is an example of a mathematical concept.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 does not include any additional elements and are, thus, directed to the abstract ideas per se.
Claim 62 recites at least one system comprising: at least one computing device comprising one or more processors; and at least one memory coupled to at least one of the one or more processors, wherein the at least one memory comprises instructions that configure the at least one computing device to generally perform the recited operations.
Claim 123 recites one or more non-transitory computer-readable storage media encoded with instructions that, when executed, configure processing circuitry of a computing device for generally performing the recited operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 219-223).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 3, 64, 125] ensuring that a subsequent operational component adheres to a limitation from another operational component wherein the limitation is a constraint that defines a parameter for control signals that are allowed or not allowed, wherein control signals assign operational resource allocations.
[Claims 7, 68, 129] wherein the plurality of operational components comprises two or more of the following: out-of-stock, assortment, product location, planogram, substitution, and multi-item purchase patterns, wherein one of the operational components is the out-of-stock operational component comprising simultaneously:
prioritizing fixing of out-of-stock identifier instances;
utilizing instances of fixing out-of-stock to use controlled experimentation to assess a substitution probability for a user to substitute a given identifier or collection of identifiers for other identifiers;
providing results of the controlled experimentation recursively into a continuously updated out-of-stock fixing prioritization process; and
providing the results to recommendation and operational systems for continuous improvement in product assortments at multiple retail locations.
[Claims 8, 69, 130] determining a substitution probability by comparing the actual sales of other products when a subject product is unavailable to the expected sales of other products over the same time periods the subject product was available.
[Claims 9, 70, 131] determining an estimated substitution matrix;
determining, based on the substitution matrix, the relative value of offering and not offering each product; and
providing a recommendation for an assortment change based upon the determined relative value of offering and not offering a product.
[Claims 10, 71, 132] wherein one of the operational components is the assortment optimization operational component which determines the optimal mix of available products for each store by comparing a value of an item being in a store versus a value of the item not being in the store.
[Claims 11, 72] wherein determining the optimal mix of available products for each store further comprises a continuous, integrated, adaptive clinical trial that automatically adjusts to changes in real-time.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims above.
NOTE: Regarding “control signals”, Applicant’s Specification states, “Based on multi-objective optimization, control signals (e.g., fix or do-not-fix and the like) may be varied across stores or even within stores at different times to estimate confidence intervals on the value of fixing or not fixing an out-of-stock situation.” (Spec: ¶ 68) Paragraph 48 of Applicant’s Specification explains that “using an integrated, continuous, adaptive clinical trial, the control signals may be prescribed (e.g., replenish a specific SKU at a specific location within a specific store) within and across retail store locations, and there are several computational/algorithmic systems that could be used to execute such components (e.g., deep reinforcement learning, multi-arm bandits, standard adaptive clinical trial logic).” In other words, “control signals” can be any sort of recommendation or prescribed instruction.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user may perform the operations identified above (e.g., selecting, applying, determining, assigning, etc.). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to assigning an operational resource allocation to operational components, which (under its broadest reasonable interpretation) is an example of sales activities, especially given that the Specification and several of the dependent claims (including claims 7-11, 68-72, and 129-132) focus on inventory management (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Independent claims 1, 62, and 123 and their dependent claims use a queue optimization model to make a determination and this is an example of a mathematical concept.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claims 1, 3, and 7-11 do not include any additional elements and are, thus, directed to the abstract ideas per se.
Claims 62, 64, and 68-72 recite at least one system comprising: at least one computing device comprising one or more processors; and at least one memory coupled to at least one of the one or more processors, wherein the at least one memory comprises instructions that configure the at least one computing device to generally perform the recited operations.
Claims 123, 125, and 129-132 recite one or more non-transitory computer-readable storage media encoded with instructions that, when executed, configure processing circuitry of a computing device for generally performing the recited operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 219-223).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
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.
Claims 1, 3, 7-11, 62, 64, 68-72, 123, 125, and 129-132 are rejected under 35 U.S.C. 103 as being unpatentable over Mahalanobish et al. (US 2019/0180301) in view of Bergstrom et al. (US 2011/0276364) in view of Ghosh et al. (US 2011/0258087).
[Claim 1] Mahalanobish discloses a method comprising:
selecting a plurality of operational components to which operational resources are allocated (¶ 141 – “The results component 702 may optionally also receive a walk-off rate 708 identifying an amount or quantity of lost demand associated with one or more items in a proposed assortment due to proposed assortment changes. The lost demand 710 may quantify demand lost as the number of units of an item remaining on a shelf (lost sales), percentage of sales lost for an item, or any other quantity associated with an item to be removed from the current item assortment.” Demand transfer may be customized for an item assortment at a given store and/or region (¶¶ 58, 60, 189). Items, i.e., operational resources, are assigned to shelves, stores, regions, etc., i.e., operational components including at least a product location, based on an item assortment, i.e., operational components including an assortment, including analysis of scenarios in which one or more items are removed from the inventory, i.e., operational components including “out-of-stock” item instances of identified items (as seen in ¶ 44 of Mahalanobish).);
selecting a plurality of control signals for the plurality of operational components (NOTE: Regarding “control signals”, Applicant’s Specification states, “Based on multi-objective optimization, control signals (e.g., fix or do-not-fix and the like) may be varied across stores or even within stores at different times to estimate confidence intervals on the value of fixing or not fixing an out-of-stock situation.” (Spec: ¶ 68) Paragraph 48 of Applicant’s Specification explains that “using an integrated, continuous, adaptive clinical trial, the control signals may be prescribed (e.g., replenish a specific SKU at a specific location within a specific store) within and across retail store locations, and there are several computational/algorithmic systems that could be used to execute such components (e.g., deep reinforcement learning, multi-arm bandits, standard adaptive clinical trial logic).” In other words, “control signals” can be any sort of recommendation or prescribed instruction.; ¶¶ 64-66 – Item assortment recommendations are made.);
applying the plurality of control signals to the plurality of operational components to determine an operational effect of the plurality control signals on the operational components (¶ 60 – “These models predict the extent of transference via simulations using predicted magnitudes of demand transference on a per-item basis for a given assortment of items within a specified store during a predetermined time-period. For example, if an item “i1” in the pre-delete scenario prior to item removal is selling one-hundred units per predetermined time-period, then the demand transference model determines how many units of the deleted item demand is transferred to each substitute item for the removed item, such as item “i2”, item “i3”, and item “i4”. This is calculated by the model at an individual store level as well as at a location, region, county, state, country, or other level.”; ¶ 75 – “A proposed item assortment includes one or more assortment changes. An assortment change is a change which adds one or more new items to the current item assortment 302 and/or removes one or more items from the current item assortment 302. In a given store and for a given set of substitute items, an assortment change may occur when one or more items are dropped from the current item assortment. Customers that intended to obtain any of the dropped items, may either choose to opt for another ‘substitutable’ item or walk away from the store without selecting a substitute item. When one or more items are introduced into the assortment, users that select one or more of the new items may choose the new item in addition to legacy items they typically select. However, if a user selects a new item instead of a legacy item they would have selected prior to addition of the new item, this is cannibalized demand taken from a legacy item and transferred to a new item. The cannibalized demand may be identified as negative or un-desirable demand transfer between items.”; ¶ 140 – “The per-item predicted demand change identifies each item in the set of substitute items for a given proposed item assortment and provides a magnitude of demand transfer to or from each item due to one or more proposed changes in assortment, such as the set of assortment changes 204 in FIG. 2.”).
Mahalanobish does not explicitly perform the steps of:
applying a resource requirement for each of the plurality of operational components; and
assigning, based on the first control signal target value, an operational resource allocation to each of the plurality of operational components.
Regarding the applying and the assigning steps, in addition to analyzing demand transference, Bergstrom further introduces spatial constraints of each shelf to optimize item assortment and facings on each shelf and generates an efficient planogram layout in accordance with specific trade-off levels, including trade-offs related to shoppability, profitability, and other factors (Bergstrom: ¶¶ 50, 59, 63, 99). Bergstrom also performs demand transfer analysis and conveys results in a demand transfer matrix format, including with the use of substitution percentage values that reflect a likelihood that a customer will purchase each available substitute item when another item is not available (Bergstrom: ¶¶ 100-102, 113-114). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Mahalanobish to perform the steps of:
applying a resource requirement for each of the plurality of operational components; and
assigning, based on the first control signal target value, an operational resource allocation to each of the plurality of operational components
in order to facilitate the optimization of a planogram layout based on acceptable trade-offs among profitability, shoppability, and other factors (as suggested in ¶ 63 of Bergstrom) while “improv[ing] the accuracy of profit estimation in those typical situations when the supply of items…is limited” (as suggested in ¶ 72 of Bergstrom).
Mahalanobish determines a first control signal target value based on the operational effect of each of the plurality of control signals on each of the plurality of operational components and the resource requirement for each of the plurality of control signals for each of the plurality of operational components (¶ 141 – “The results component 702 may optionally also receive a walk-off rate 708 identifying an amount or quantity of lost demand associated with one or more items in a proposed assortment due to proposed assortment changes. The lost demand 710 may quantify demand lost as the number of units of an item remaining on a shelf (lost sales), percentage of sales lost for an item, or any other quantity associated with an item to be removed from the current item assortment.” Demand transfer may be customized for an item assortment at a given store and/or region (¶¶ 60, 189). Items, i.e., operational resources, are assigned to shelves, stores, regions, etc., i.e., operational components including at least a product location, based on an item assortment, i.e., operational components including an assortment, including analysis of scenarios in which one or more items are removed from the inventory, i.e., operational components including “out-of-stock” item instances of identified items (as seen in ¶ 44 of Mahalanobish).
Mahalanobish does not explicitly disclose that the determining step is performed using queueing theory. Ghosh discloses that queueing theory and queuing analytics and optimization techniques (i.e., models) may be used to prioritize items in a production line, including in made-to-stock and made-to-order situations (Ghosh: abstract, ¶ 31). Item profit margins may be taken into account for the priority queueing and a solution may be solved recursively (Ghosh: ¶ 33). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Mahalanobish to perform the step of determining, using a queue optimization model, a first control signal target value, based on the operational effect of each of the plurality of control signals on each of the plurality of operational components and the resource requirement for each of the plurality of control signals for each of the plurality of operational components in order to facilitate the provision of inventory to stock shelves as needed in an efficient manner by using an approach that “is optimized to take advantage of substitutability at each step in managing the inventory replenishment orders” (Ghosh: ¶ 26) while optimizing production costs and taking into account space for inventory (as suggested in ¶¶ 27, 29 of Ghosh).
[Claim 3] Mahalanobish does not explicitly disclose ensuring that a subsequent operational component adheres to a limitation from another operational component wherein the limitation is a constraint that defines a parameter for control signals that are allowed or not allowed, wherein control signals assign operational resource allocations. In addition to analyzing demand transfer, Bergstrom further introduces spatial constraints of each shelf to optimize item assortment and facings on each shelf and generates an efficient planogram layout in accordance with specific trade-off levels, including trade-offs related to shoppability, profitability, and other factors (Bergstrom: ¶¶ 50, 59, 63, 99). Bergstrom also performs demand transfer analysis and conveys results in a demand transfer matrix format, including with the use of substitution percentage values that reflect a likelihood that a customer will purchase each available substitute item when another item is not available (Bergstrom: ¶¶ 100-102, 113-114). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Mahalanobish to perform the step of ensuring that a subsequent operational component adheres to a limitation from another operational component wherein the limitation is a constraint that defines a parameter for control signals that are allowed or not allowed, wherein control signals assign operational resource allocations in order to facilitate the optimization of a planogram layout based on acceptable trade-offs among profitability, shoppability, and other factors (as suggested in ¶ 63 of Bergstrom) while “improv[ing] the accuracy of profit estimation in those typical situations when the supply of items…is limited” (as suggested in ¶ 72 of Bergstrom).
[Claim 7] Mahalanobish discloses wherein the plurality of operational components comprises two or more of the following: out-of-stock, assortment, product location, planogram, substitution, and multi-item purchase patterns, wherein one of the operational components is the out-of-stock operational component (¶ 141 – “The results component 702 may optionally also receive a walk-off rate 708 identifying an amount or quantity of lost demand associated with one or more items in a proposed assortment due to proposed assortment changes. The lost demand 710 may quantify demand lost as the number of units of an item remaining on a shelf (lost sales), percentage of sales lost for an item, or any other quantity associated with an item to be removed from the current item assortment.” Demand transfer may be customized for an item assortment at a given store and/or region (¶¶ 60, 189). Items, i.e., operational resources, are assigned to shelves, stores, regions, etc., i.e., operational components including at least a product location, based on an item assortment, i.e., operational components including an assortment, including analysis of scenarios in which one or more items are removed from the inventory, i.e., operational components including “out-of-stock” item instances of identified items (as seen in ¶ 44 of Mahalanobish).).
Mahalanobish does not explicitly disclose simultaneously:
prioritizing fixing of out-of-stock identifier instances;
utilizing instances of fixing out-of-stock to use controlled experimentation to assess a substitution probability for a user to substitute a given identifier or collection of identifiers for other identifiers;
providing results of the controlled experimentation recursively into a continuously updated out-of-stock fixing prioritization process; and
providing the results to recommendation and operational systems for continuous improvement in product assortments at multiple retail locations.
In addition to analyzing demand transfer, Bergstrom further introduces spatial constraints of each shelf to optimize item assortment and facings on each shelf and generates an efficient planogram layout in accordance with specific trade-off levels, including trade-offs related to shoppability, profitability, and other factors (Bergstrom: ¶¶ 50, 59, 63, 99). Bergstrom also performs demand transfer analysis and conveys results in a demand transfer matrix format, including with the use of substitution percentage values that reflect a likelihood that a customer will purchase each available substitute item when another item is not available (Bergstrom: ¶¶ 100-102, 113-114). Bergstrom further explains, “The simulation technique may involve executing multiple iterations of a simulated arrival of a group of customers, attempted selection of a preferred item by each customer, and a selection of a replacement item if the primary selection is unavailable. The selection of the replacement item may be simulated using transfer probabilities and historical sales data.” (Bergstrom: ¶ 10) In other words, addressing out-of-stock item scenarios is a main priority. Bergstrom may perform the disclosed analysis continuously and using recursive algorithms (Bergstrom: ¶¶ 69, 94, 97, 108-109, 120-121, 125-126). While Bergstrom does not explicitly state that the product assortments are continuously improved at multiple retail locations, Mahalanobish explains that demand transfer may be customized for an item assortment at a given store and/or region (Mahalanobish: ¶¶ 58, 60, 189). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Mahalanobish to simultaneously perform the steps of:
prioritizing fixing of out-of-stock identifier instances;
utilizing instances of fixing out-of-stock to use controlled experimentation to assess a substitution probability for a user to substitute a given identifier or collection of identifiers for other identifiers;
providing results of the controlled experimentation recursively into a continuously updated out-of-stock fixing prioritization process; and
providing the results to recommendation and operational systems for continuous improvement in product assortments at multiple retail locations
in order to facilitate the optimization of a planogram layout based on acceptable trade-offs among profitability, shoppability, and other factors (as suggested in ¶ 63 of Bergstrom) while “improv[ing] the accuracy of profit estimation in those typical situations when the supply of items…is limited” (as suggested in ¶ 72 of Bergstrom).
[Claim 8] Mahalanobish discloses determining a substitution probability (¶ 79 – “FIG. 4 is an exemplary block diagram illustrating per-item demand change 400 due to an assortment change. A per-item demand change 400 is an identification of an amount of change in demand associated with each item in an assortment due to one or more changes in the assortment. Demand change may be quantified in terms of a percentage of units of an item, a percentage of cases of an item, the number of units of an item, the number of cases of an item, a monetary value, a score, and/or a ranking.” A demand change quantified as a percentage is an example of a substitution probability.) and determines the relative value of offering and not offering each product (Mahalanobish: ¶ 80 – “A demand change may include a predicted horizontal demand transference 402 from one item to another item and/or an incremental demand 404 associated with a single item. The predicted horizontal demand transference 402 includes an identification of demand transferred to a substitute item 406 and/or demand transferred from a substitute item 408. Demand transferred horizontally from one item to another item is demand that is maintained or preserved by the proposed item assortment. However, when demand is transferred from one legacy item to a new item being added to the item assortment, the demand transfer is not beneficial because the new item is cannibalizing demand from a legacy item.”) by comparing the actual sales of other products when a subject product is unavailable to the expected sales of other products over the same time periods the subject product was available (¶ 60 – “These models predict the extent of transference via simulations using predicted magnitudes of demand transference on a per-item basis for a given assortment of items within a specified store during a predetermined time-period. For example, if an item “i1” in the pre-delete scenario prior to item removal is selling one-hundred units per predetermined time-period, then the demand transference model determines how many units of the deleted item demand is transferred to each substitute item for the removed item, such as item “i2”, item “i3”, and item “i4”. This is calculated by the model at an individual store level as well as at a location, region, county, state, country, or other level.”; fig. 17, ¶¶ 103-141 – Sales are compared for the same products over multiple weekly periods of time.; ¶ 61 – “The demand prediction component 124 in some examples outputs a magnitude of sales for each item in a given assortment. For example, the demand transference model 128 may output predicted per-unit sales at a given store on a weekly basis (Store×Week×UPC level) if one or more proposed assortment changes are implemented. The demand prediction component 124 in other examples may output an estimated walk-off rate (demand lost) for the proposed assortment changes.”; ¶ 62 – “In still other examples, the demand prediction component 124 utilizes a multinomial logic model to estimate how sales of a given item are affected by price and presence of one or more item attributes. The demand prediction component 124 utilizes item attributes and historical transaction data to generate the predicted demand transference. In one example, the historical transaction data includes historical sales for a given item at a given store during a predetermined time-period. For example, the historical transaction may include data for an identified item at an identified store or location within a given item assortment on a weekly basis (item×store×week×assortment). This ensures statistically robust models are generated.”; ¶ 79 – “FIG. 4 is an exemplary block diagram illustrating per-item demand change 400 due to an assortment change. A per-item demand change 400 is an identification of an amount of change in demand associated with each item in an assortment due to one or more changes in the assortment. Demand change may be quantified in terms of a percentage of units of an item, a percentage of cases of an item, the number of units of an item, the number of cases of an item, a monetary value, a score, and/or a ranking.” A demand change quantified as a percentage is an example of a substitution probability.; ¶ 80 – “A demand change may include a predicted horizontal demand transference 402 from one item to another item and/or an incremental demand 404 associated with a single item. The predicted horizontal demand transference 402 includes an identification of demand transferred to a substitute item 406 and/or demand transferred from a substitute item 408. Demand transferred horizontally from one item to another item is demand that is maintained or preserved by the proposed item assortment. However, when demand is transferred from one legacy item to a new item being added to the item assortment, the demand transfer is not beneficial because the new item is cannibalizing demand from a legacy item.”; ¶ 93 – “The demand patterns 636 includes historical item-demand patterns associated with various item assortment combinations at the same retail environment and/or historical assortment data associated with one or more other retail environments.” Historical and predicted sales may be based on specific time periods, such as weekly time periods. Fig. 17 shows days available of each compared product by weekly time period.; ¶¶ 49-53 – Information evaluated for predetermined time periods may include item cannibalization for the different item assortment scenarios. In effect, this cannibalization analysis compares how much is expected to be sold for an item vs. how much is actually sold, responsive to a change in item assortment, including unavailability of an item.).
[Claim 9] Mahalanobish provides a recommendation for an assortment change based upon the determined relative value of offering and not offering a product (¶ 80 – “A demand change may include a predicted horizontal demand transference 402 from one item to another item and/or an incremental demand 404 associated with a single item. The predicted horizontal demand transference 402 includes an identification of demand transferred to a substitute item 406 and/or demand transferred from a substitute item 408. Demand transferred horizontally from one item to another item is demand that is maintained or preserved by the proposed item assortment. However, when demand is transferred from one legacy item to a new item being added to the item assortment, the demand transfer is not beneficial because the new item is cannibalizing demand from a legacy item.”).
Mahalanobish determines a substitution probability (Mahalanobish: ¶ 79 – “FIG. 4 is an exemplary block diagram illustrating per-item demand change 400 due to an assortment change. A per-item demand change 400 is an identification of an amount of change in demand associated with each item in an assortment due to one or more changes in the assortment. Demand change may be quantified in terms of a percentage of units of an item, a percentage of cases of an item, the number of units of an item, the number of cases of an item, a monetary value, a score, and/or a ranking.” A demand change quantified as a percentage is an example of a substitution probability.) and determines the relative value of offering and not offering each product (Mahalanobish: ¶ 80 – “A demand change may include a predicted horizontal demand transference 402 from one item to another item and/or an incremental demand 404 associated with a single item. The predicted horizontal demand transference 402 includes an identification of demand transferred to a substitute item 406 and/or demand transferred from a substitute item 408. Demand transferred horizontally from one item to another item is demand that is maintained or preserved by the proposed item assortment. However, when demand is transferred from one legacy item to a new item being added to the item assortment, the demand transfer is not beneficial because the new item is cannibalizing demand from a legacy item.”).
Mahalanobish does not explicitly perform the steps of:
determining an estimated substitution matrix; and
determining, based on the substitution matrix, the relative value of offering and not offering each product.
Bergstrom performs demand transfer analysis and conveys results in a demand transfer matrix format, including with the use of substitution percentage values that reflect a likelihood that a customer will purchase each available substitute item when another item is not available (Bergstrom: ¶¶ 100-102, 113-114). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Mahalanobish to perform the steps of:
determining an estimated substitution matrix; and
determining, based on the substitution matrix, the relative value of offering and not offering each product
in order to facilitate the optimization of a planogram layout based on acceptable trade-offs among profitability, shoppability, and other factors (as suggested in ¶ 63 of Bergstrom) while “improv[ing] the accuracy of profit estimation in those typical situations when the supply of items…is limited” (as suggested in ¶ 72 of Bergstrom).
[Claim 10] Mahalanobish discloses wherein one of the operational components is the assortment optimization operational component which determines the optimal mix of available products for each store by comparing a value of an item being in a store versus a value of the item not being in the store (¶ 60 – “These models predict the extent of transference via simulations using predicted magnitudes of demand transference on a per-item basis for a given assortment of items within a specified store during a predetermined time-period. For example, if an item “i1” in the pre-delete scenario prior to item removal is selling one-hundred units per predetermined time-period, then the demand transference model determines how many units of the deleted item demand is transferred to each substitute item for the removed item, such as item “i2”, item “i3”, and item “i4”. This is calculated by the model at an individual store level as well as at a location, region, county, state, country, or other level.”; ¶ 80 – “A demand change may include a predicted horizontal demand transference 402 from one item to another item and/or an incremental demand 404 associated with a single item. The predicted horizontal demand transference 402 includes an identification of demand transferred to a substitute item 406 and/or demand transferred from a substitute item 408. Demand transferred horizontally from one item to another item is demand that is maintained or preserved by the proposed item assortment. However, when demand is transferred from one legacy item to a new item being added to the item assortment, the demand transfer is not beneficial because the new item is cannibalizing demand from a legacy item.”).
[Claim 11] Mahalanobish does not explicitly disclose wherein determining the optimal mix of available products for each store further comprises a continuous, integrated, adaptive clinical trial that automatically adjusts to changes in real-time. Bergstrom explains, “The simulation technique may involve executing multiple iterations of a simulated arrival of a group of customers, attempted selection of a preferred item by each customer, and a selection of a replacement item if the primary selection is unavailable. The selection of the replacement item may be simulated using transfer probabilities and historical sales data.” (Bergstrom: ¶ 10) In other words, addressing out-of-stock item scenarios is a main priority. Bergstrom may perform the disclosed analysis continuously and using recursive algorithms (Bergstrom: ¶¶ 69, 94, 97, 108-109, 120-121, 125-126). While Bergstrom does not explicitly state that the optimal mix automatically adjusts to changes in real-time, Mahalanobish states, “Assortment is frequently an important element in differentiating one store from another regarding competition and actual sales. The assortment recommendation, along with the demand transference result, and/or score output by the demand prediction component and assortment recommendation component assist a user in selecting an optimal assortment that maximizes category profitability, without sacrificing customer satisfaction.” (Mahalanobish: ¶ 150) Furthermore, Mahalanobish explains that “assortments cannot remain stagnant over time. As items experience a decline in demand over time, it is frequently beneficial to remove these lower performance items to make space for additional new items which may attract greater interest and demand.” (Mahalanobish: ¶ 1) Therefore, Mahalanobish expresses a desire to and a benefit in continuously using actual sales information to adjust item assortments over time. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Mahalanobish wherein determining the optimal mix of available products for each store further comprises a continuous, integrated, adaptive clinical trial that automatically adjusts to changes in real-time in order to ensure that item assortments are made as optimal as possible in light of the latest market trends and conditions.
[Claims 62, 64, 68-72] Claims 62, 64, and 68-72 recite limitations already addressed by the rejections of claims 1, 3, and 7-11 above; therefore, the same rejections apply.
Furthermore, Mahalanobish, Bergstrom, and Ghosh each disclose at least one system comprising: at least one computing device comprising one or more processors; and at least one memory coupled to at least one of the one or more processors, wherein the at least one memory comprises instructions that configure the at least one computing device to perform the respectively disclosed operations (Mahalanobish: ¶ 36; Bergstrom: ¶¶ 52, 64; Ghosh: ¶¶ 64, 67).
[Claims 123, 125, 129-132] Claims 123, 125, and 129-132 recite limitations already addressed by the rejections of claims 1, 3, and 7-10 above; therefore, the same rejections apply.
Furthermore, Mahalanobish, Bergstrom, and Ghosh each disclose one or more non-transitory computer-readable storage media encoded with instructions that, when executed, configure processing circuitry of a computing device for performing the respectively disclosed operations (Mahalanobish: ¶ 36; Bergstrom: ¶¶ 52, 64; Ghosh: ¶¶ 64, 67).
Conclusion
THIS ACTION IS MADE FINAL. 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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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights