DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 10/16/2025 regarding application 17/836,826. Claims 1, 3-4, 7, 9-11, 13-15 and 19-20 have been amended. Claim 6 has been canceled. Claims 1-5, 7-11, 13-15, 19-20 and 31-32 have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 10/16/2025 necessitated new grounds of rejection in this office action.
IDS Statements
3. The 1 Information Disclosure Statement (IDS) filed on 09/12/2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Response to Arguments
4. Applicant’s arguments, see page 8 of 18 filed on 10/16/2025, with respect to Claim Objections to Claims 1 and 11 have been fully considered and are found to be persuasive. Therefore, the Claim Objections to Claims 1 and 11 are withdrawn.
5. Due to Applicant’s proposed amendments to Independent Claims 1 and 11, Examiner adds 35 U.S.C. § 112 (a) rejection for Claims 1-5, 7-11, 13-15, 19-20 and 31-32. See the 35 U.S.C. § 112 (a) claim rejections shown below.
6. Applicant’s arguments, see page 16 of 18 filed on 10/16/2025, with respect to the 35 U.S.C. § 112 (b) rejection for Claims 11, 13-15 and 19-20 have been fully considered and are found to be persuasive. Therefore, the 35 U.S.C. § 112 (b) rejection for Claims 11, 13-15 and 19-20 have been withdrawn.
Response to 35 U.S.C. § 101 Arguments
7. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-5, 7-11, 13-15, 19-20 and 31-32 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 8-16, dated 10/16/2025). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are patent eligible due to the Ex parte Desjardins case (Appeal 2024-000567) regarding the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 10-13, dated 10/16/2025). Examiner respectfully disagrees.
Specifically, Applicant asserts that Examiner has evaluated claims at such a high level of generality and that the analysis in the Office action falls squarely into the high level of generality problem Director Squares warns against (see Applicant Remarks, Pages 8-9 of 18, dated 10/16/2025). Examiner respectfully disagrees.
Examiner notes that while the Ex parte Desjardins (Appeal 2024-000567) case signals a more permissive stance on AI eligibility, it does not provide a “blanket” eligibility for all machine learning claims. Claims 1-5, 7-11, 13-15, 19-20 and 31-32 recited are different from those in the Ex parte Desjardins case (Appeal 2024-000567) because they apply algorithms to a business problem rather than solving a technological problem in computer functionality. Reason #1: Technical Improvement vs. Business Application. These claims of the instant application different from Desjardins is due to the nature of the asserted improvement. Ex parte Desjardins were directed to a specific method of training machine learning models that reduced storage requirements, lowered system complexity, and solved the technical problem of “catastrophic forgetting” in continual learning systems. The Appeals Review Panel (ARP) held that these were improvements to the operation of the model itself. In contrast to Ex parte Desjardins, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 of the instant application describe an assortment simulation for retail inventory. While they use “k-means clustering”, they do not modify or improve the k-means algorithm or the underlying computer’s efficiency. Instead, they use a mathematical tool to achieve a business outcome (allocating objects to a shelf). Under the Desjardins framework, merely applying machine learning to a “new data environment” without improving the model’s architecture remains ineligible. Reason #2: Failure to Integrate into a practical application (Step 2a prong 2). In Desjardins, the ARP emphasized that eligibility is found when claims are “directed to an improvement in the functioning of a computer or other technology per the Enfish standard. In Desjardins, the claims integrated mathematical concepts into a practical application by enhancing the model’s ability to learn tasks sequentially while preserving prior knowledge. In contrast to Ex parte Desjardins, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 of the instant application integrate mathematical concepts into a merchandising process. Because “shelf allocation” and “assortment simulation” are methods of organizing human activity (business methods), they do not constitute a “technological improvement” in the sense required by Desjardins. PTAB decisions post-Desjardins have already affirmed rejections for AI methods that merely use generic computer components to process data without affecting the “form or function” of the model itself. Additionally, in Desjardins, the “additional elements” (specifically the training parameters) were essential to the technological breakthrough of reducing storage and complexity. In Claims 1-5, 7-11, 13-15, 19-20 and 31-32 of the instant application, the steps of “cause the … objects to be allocated to a shelf” and “execute an assortment simulation” are likely to be viewed as a limiting of a “field of use” (retail) rather than a technical solution (see MPEP 2106.05 § (h)). Unlike Desjardins, where the improvement was “logical structures and processes” that made the computer better, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 of the instant application describe a logical process that are asserted to make a store’s inventory better.
In conclusion, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 of the instant application are not analogous to the Ex parte Desjardins (Appeal 2024-000567) case and are maintained as patent ineligible under the 35 U.S.C. § 101 analysis.
Argument #2:
(B). Applicant argues that Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are patent eligible regarding the 35 U.S.C. § 101 analysis and analogous to the Enfish case (see Applicant Remarks, Pages 10-13, dated 10/16/2025). Examiner respectfully disagrees.
Specifically, Applicant asserts that the claimed subject matter is not simply directed to any manner of causing a new object to be allocated to a shelf, by a specific manner of transforming available data to a performance metric where the new product has not been sold within the channel and/or sales data from another retailer within the same channel is otherwise unavailable and thus are analogous to the Enfish case (see Applicant Remarks, page 9 of 18, dated 10/16/2025). Examiner respectfully disagrees.
In response to Applicant’s 35 U.S.C. § 101 remarks here, Examiner points out that Claims 1-5, 7-11, 13-15, 19-20 and 31-32 of the instant application are distinguishable from Enfish because they improve a business process (retail assortment) rather than a computer capability (data structure functionality). Additionally, these claims of the instant application are different from the Enfish case due to the following reasons. Reason #1: “Improvement in Function” vs. “Use of a Tool”. The claims of the Enfish case were directed to a specific “self-referential” database model. The court found this eligible because it was a technological improvement to how computers store and retrieve data. It allowed for faster searching and more flexible data structures regardless of the type of data being stored. In contrast to Enfish, the claims of the instant application do not improve how the “processor circuit” or “interface circuitry” functions. The k-means clustering and ratio calculations are mathematical tools used to process retail data. The computer is acting as a generic tool to perform the calculations faster than a human, which Enfish specifically distinguished from a true technical improvement. Reason #2: Specificity of the Logical Architecture. The Enfish claims described a new way for a computer to “think” about information (the self-referential table), which was a departure from relational databases. In contrast to Enfish, the claims of the instant application describe a logical flow that mirrors decision making in merchandising such as look at what store A has the store B doesn’t, then group similar items and then estimate how a new item would sell based on the performance of similar items. Since this logic represents a method of organizing human activity (business logic) rather than a new way for a computer to handle data at a system level, it does not meet the Enfish standard. Reason #3: The “abstract idea” vs. “means of achieving a goal”. The court in Enfish ruled that the “self-referential table” was not an abstract idea because it was a specific implementation of a solution to a computer-science problem (database efficiency). In contrast to Enfish, the claims of the instant application are directed to the goal of “assortment simulation” and “shelf allocation”. The mathematical steps (clustering and ratios) are the abstract means of achieving that goal. Unlike Enfish, where the benefit was seen in the computer’s performance (speed/memory), the benefit here is seen in the retail entity’s performance (better shelf management).
Under 35 U.S.C. § 101, the claims of the instant application fail the Enfish test because they do not improve the “functioning of a computer”. Instead, they recite abstract mathematical concepts (clustering/ratios) to automate a method of organizing human activity (inventory management). Because the innovation is the result (the shelf allocation) rather than the technical means (the circuitry’s operation), Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are patent ineligible under 35 U.S.C. § 101 and not analogous to the claims of the Enfish case.
Argument #3:
(C). Applicant argues that Claims 1-5, 7-11, 13-15, 19-20 and 31-32 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 10 of 16, dated 10/16/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that these amended claim limitations of Independent Claims 1 and 11 are clearly not directed to an abstract idea under step 2a prong 1 where Applicant asserts that one can only arrive at the incorrect conclusion in the Office action through analysis done at an impermissibly high level of granularity (see Applicant Remarks, 1st ¶ on Page 10 of 16, dated 10/16/2025).
In response to Applicant’s 35 U.S.C. § 101 arguments here, Examiner notes that Independent Claims 1 and 11 explicitly recites a “specific manner” of data transformation that relies entirely on “Mathematical Concepts”. These concepts include k-means clustering which is a recognized mathematical algorithm used for data portioning and ratio determination for calculating a “performance metric ratio” and subsequently a “first value” based on that ratio is a mathematical calculation. A claim “recites” an abstract idea if it sets forth a mathematical formula, relationship or calculation. The fact that the math is used to predict performance for “unsold” products does not change the nature of the limitation; it remains a mathematical concept regardless of the “specific manner” or utility of the calculation. With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
The goal of transforming data into a metric to “cause… allocation to a shelf” is a Method of Organizing Human Activity. Commercial interactions: Merchandising, inventory management, and shelf allocation are fundamental economic practices. Comparison to Case Law: In SAP America v. Invest PIC, LLC, the Federal Circuit ruled that even “new and nonobvious” mathematical innovations in the realm of data analysis (such as calculating investment metrics) are directed to abstract ideas if they merely analyze and report information. Similarly, creating a performance metric for retail assortment is a business activity, not a technological one.
Examiner also points out that Transformation of Data is not a practical application (Step 2a prong 1). The argument that the “specific manner” of transformation avoids Step 2a Prong 1, conflates Prong 1 (recitation) with Prong 2 (integration). Recitation vs. Integration: In Prong 1, Examiner only checks if the claim sets forth an abstract idea. Because the claims recite k-means clustering and ratio calculations, it recites mathematical concepts. In Electric Power Group, LLC v. Alstom S.A., the court held that collecting, analyzing and displaying information – even through “specific” transformations – is an abstract idea. The “specific manner” described here (comparing two channels to find “new objects”) is simply a logical data-sorting exercise that could be performed as a mental process or with pencil and paper, albeit more slowly. Additionally, unlike the invention in Enfish (which improved database architecture) or Desjardins (which improved AI learning models), these claims of the instant application use the computer as a tool to execute business logic. The scarcity of sales data for a “new product” is a business problem, not a technological problem in the computer’s circuitry. Secondly, using a mathematical ration to fill a data gap is a statistical modeling technique, which the Federal Circuit has consistently categorized as an ineligible abstract idea.
Because Claims 1-5, 7-11, 13-15, 19-20 and 31-32 recite both mathematical concepts (clustering/ratios) and a method of organizing human activity (inventory allocation), it is directed to an abstract idea under step 2a prong 1 via the 35 U.S.C. 101 analysis. The specificity of the retail environment (missing sales data) serves only to limit the “field of use”, which is insufficient to overcome the abstract idea status.
Argument #4:
(D). Applicant argues that Claims 1-5, 7-11, 13-15, 19-20 and 31-32 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, last ¶ of Page 10 thru Page 11 of 16, dated 10/16/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that these claims are patent eligible under 35 U.S.C. § 101 step 2a prong 2 because they “remove dependencies on hierarchies” and “avoid erroneous data” by using k-means clustering can be disputed because these points still describe business or logical efficiencies, not technological improvements to a computer system or a technical field. Examiner points out the following reasons why Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are ineligible under step 2a prong 2 of the 35 U.S.C. 101 analysis. First, the nature of the “improvements” business logic vs. technology. The argument identifies efficiencies (“removing dependencies”, “avoids erroneous data”) that relate to the quality of the business analysis, not the operation of the underlying technology. Removing Hierarchies/avoiding erroneous data: This describes a specific, perhaps better, way to organize data for analysis. This is a methodological improvement to a business process (merchandising), a form of organizing human activity which is categorized as an abstract idea. The Federal Circuit has repeatedly held that collecting, analyzing, and presenting information, even with novel or efficient analytical techniques (like avoiding bad data) is an abstract idea unless it is integrated into a concrete technological solution. The fact that the data transformation is efficient does not make it a technological practical application. Reason #2: K-Means clustering. The use of k-means clustering, while effective for the stated purpose is a well-known algorithm in data science. Applying a mathematical tool in a new business context does not satisfy the “significantly more” requirement of the Alice framework. The computer is merely performing its intended function: running known software to perform calculations. Lack of Integration: The algorithm itself isn’t integrated into a technological process in a non-conventional way (e.g., controlling a specific, non-generic machine beyond a processor circuit). It’s integrated into a merchandising strategy, which remains an abstract, organizational activity. Reason #3: Preemption and the “Field of Use” limitations. The assertion relies on the preemption argument from Alice, suggesting that because the claim is specific (avoids certain data/hierarchies), it doesn’t preempt the entire field of data analysis. Field of Use is Insufficient. This merely limits the field of use of the abstract idea to a specific context (retail assortment using this specific data-cleansing method). A field of use limitation does not make an otherwise abstract idea eligible. Focus on the Abstract Idea: The underlying abstract idea remains using statistics and mathematical modeling to predict business outcomes (sales performance). The limitations only define the parameters of the input data used in the abstract calculation. Reason #4: To succeed in Prong 2, there must be a technological improvement. The claims lack this. The “interface circuitry” is a generic data input, the “processor circuit” executes standard instructions and the “output” is a decision to allocate an object to a “shelf”, a physical business action.
In conclusion, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 fails to meet the Step 2a prong 2 standard. The cited efficiencies (“removing dependencies”, “avoids erroneous data”) are improvements to business analysis logic rather than the underlying technology. Using a k-means clustering algorithm on a computer to organize human activity (merchandising) does not provide the concrete, technological “something more” required for patent eligibility under 35 U.S.C. § 101.
Argument #5:
(E). Applicant argues that for the claims that no human activity is being organized and that the claims are merely “simplifying complexity” of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 12 of 16, dated 10/16/2025). Examiner respectfully disagrees.
Examiner responds by stating one must look to the substance of the claims rather than the Applicant’s characterization. First, the substance over form: Commercial Interactions. The applicant’s statement that “no commercial interactions” are claimed is contradicted by the explicit claim language. Fundamental economic practice: “Causing an object to be allocated to a shelf of an entity” is a commercial activity” – specifically inventory management and merchandising. Commercial Interaction: These claims involve “channels” and “entities” (retailers/businesses). The entire purpose of the “assortment simulation” is to manage the commercial interaction between a seller and a consumer by determining which products should be available for purchase. This falls squarely within the enumerated sub-grouping of commercial interactions. Simplify complexity is a mental process/business goal. The applicant argues that the claims simplify the “complexity of an assortment strategy”. However, the Federal Circuit has established that simplifying a complex business problem through data analysis is an abstract idea. Analogy to SAP America v. Invest Pic: In that case, the court held that even complex statistical analyses used to simplify investment decisions were abstract. Here, the “complexity” being simplified is a business strategy (assortment), not a technical bottleneck in a computer architecture. Logical steps such as identifying “new objects”, “common objects” and “iteratively clustering” them describes a logical process of categorization. Whether performed by a human or a computer, the logic of the simplification is a method of organizing business information. Independent Recitation of Mathematical Concepts: Even if the Applicant successfully argued that the claim is not a “method of organizing human activity”, in which Examiner does not agree with, these claims independently fail Prong 1 because it recites Mathematical Concepts. Mathematical Relationships: These claims explicitly require “executing a k-means clustering technique” and “determining a performance metric ratio”. Mathematical Calculations: The determination of a “first value… based on the first performance metric ratio” is a mathematical calculation. Under the USPTO Guidance, if a claim recites a mathematical concept (formula, relationship, or algorithm) it is directed to an abstract idea. The Applicant’s argument focus on “human activity” ignores the fact that these claims are built upon an ineligible mathematical framework. The Applicant argues that the groupings should not be expanded. However, the current claims do not require “expansion” because they fit directly into the existing categories: Managing Commercial Interactions: Managing a retail shelf and product assortment is a core business/commercial interaction and Mathematical Concepts: K-means and ratio-based extrapolation are textbook examples of mathematical concepts.
In conclusion, the Applicant’s characterization of Claims 1-5, 7-11, 13-15, 19-20 and 31-32 as a technical “simplification” of complexity is a “field of use” wrapper around an abstract core. Because these claims recite mathematical algorithms to achieve a commercial result (shelf allocation and assortment strategy), it is directed to an abstract idea under step 2a prong 1. The simplification of a business strategy through data processing is exactly the type of “method of organizing human activity” that the Alice framework was designed to exclude from patentability.
Argument #6:
(F). Applicant argues that for the claims that no human activity is being organized and that the claims are merely “simplifying complexity” of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 12 of 16, dated 10/16/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that that “a person under the certain methods of organizing human activities category via managing personal behavior can update assortment and space optimization using pen to paper as a physical aid and that this high level of generality evaluation fails to consider over 50 million potential products available in the marketplace which inherently makes such efforts impractical via pen to paper as a physical aid (see Applicant Remarks, 2nd ¶ of Page 12, dated 10/16/2025). Examiner respectfully disagrees.
Examiner notes that inherent impracticality does not negate a mental process. The “mental process” grouping includes any process that can be performed in the human mind or with pen and paper as a physical aid. Speed and scale are irrelevant. The Federal Circuit has repeatedly ruled that a process does not stop being a mental process simplify because a computer can do it faster or on a larger scale. In SAP America, Inc. v. InvestPic, LLC, the court emphasized that even if the volume of data makes human calculation practically impossible, the underlying activity (statistical analysis and information selection) remains an abstract idea. If the steps of these claims (comparing, clustering, and calculating ratios) have a human analog – such as a retail analyst categorizing products and estimating performance – these claims are “directed to” a mental process regardless of a whether a human could realistically process 50 million items in a single setting. Grouping Under Certain Methods of Organizing Human Activities. The Applicant’s focus on the “impracticality” of pen and paper ignores that these claims also fall into the “Certain Methods of Organizing Human Activities” category, specifically Commercial Interactions: updating assortments and space optimization are fundamental economic business practices. Managing personal behavior: While the Applicant might argue this doesn’t “manage personal behavior”, the USPTO groups “managing commercial interactions” under this broader category of organizing human activities. Merchandising strategies and inventory allocation are classic example of managing commercial entities and their interactions within the marketplace. Also these claims of the instant application lack a technological solution to a technological problem. The “50 million products” argument highlights a business problem (scaling an analysis) not a technological problem (improving the computer’s own functionality). The Enfish Distinction for a claim to avoid being “directed to” an abstract idea, it must improve the computer’s operation itself.
In conclusion, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 do not improve how the processor circuit handles data at a system level; they use the processor as a tool to execute a business strategy. Claims 1-5, 7-11, 13-15, 19-20 and 31-32 merely describe a desired result (an optimized shelf) without reciting a specific, non-conventional technological implementation of how the computer is improved are ineligible.
Argument #7:
(G). Applicant argues that the delineated sub-groupings encompass both activity of a single person and activity that involves multiple people to allegedly support certain methods of organizing human activities and that this high level of generality has no bearing on methods of organizing human activities under the 35 U.S.C. § 101 analysis (see Applicant Remarks, 3rd ¶ of Page 12-13 of 16, dated 10/16/2025). Examiner respectfully disagrees.
To dispute that the Applicant’s argument that the “high level of generality” regarding single vs. multiple person activity has no bearing on the analysis, one must rely on the USPTO’s 101 Guidance and Federal Circuit precedent. The argument that the grouping “Certain Methods of Organizing Human Activities” is overly broad or irrelevant fails because the classification is based on the nature of the activity and not the number of participants. Generality does not negate the classification. The Applicant’s contention that a “high level of generality” makes the grouping inapplicable is legally incorrect. Under step 2a prong 1, an Examiner must determine if a claim falls into one of the enumerated categories. The test is whether the claims describe a “fundamental economic practice” or “commercial interaction”. “Allocating an object to a shelf” and “assortment simulation” are activities that, by their very nature, organize human commercial behavior – specifically inventory and supply chain management. Whether these are performed by one person (a single store manager) or multiple people (a corporate merchandising team) is irrelevant to the fact that the activity itself is a method of organizing human commercial interaction. High Level of Generality is the Hallmark of abstractness. In Alice Corp v. CLS Bank, the Supreme Court noted that the abstract ideas are often characterized by their “high level of generality” in describing a goal without a specific technical implementation. The Applicant’s admission that the evaluation is at a high level of generality actually supports the rejection. If a claim describes the “what” (organizing a shelf based on a ratio) rather than a specific “how” that improves computer technology, it is likely abstract. The Federal Circuit in Electric Power Group noted that the high degree of generality in the stated functions is a strong indicator that the claim is directed to an abstract idea, regardless of whether it involves one person or a network of people. Sub Groupings are Function-Based, not scale-based. The “Certain Methods of Organizing Human Activities” category is defined by the function being performed (e.g., managing commercial interactions). The Applicant argues that because the sub-groupings encompass both single and multiple-person activities, the category is too broad to be meaningful. However, the law does not require a specific headcount to trigger the “human activity” exception. Managing an “assortment strategy” is a commercial interaction between an entity (e.g. the retailer) and its marketplace. This interaction is a fundamental economic practice” regardless of whether the “human activity” is performed by a single analyst using the software or a distributed team. Mathematical Concepts as an Independent Ground. Even if the Applicant’s “generality” argument regarding human activity were to be accepted, the claims remain ineligible because it independently recites “Mathematical Concepts”. The claims explicitly recite “k-means clustering” and “performance metric ratios” via mathematical algorithms. The mathematical grouping under Step 2a prong 1, does not depend on human activity or generality. Since the claims set forth a mathematical relationship to achieve its result, the Applicant’s focus on the “human activity” grouping fails to address the entire scope of the abstract idea.
In conclusion, the Applicant’s argument attempts to dismiss the “Certain Methods of Organizing Human Activity” category by labeling it as a “high level of generality”. However, the USPTO Guidance specifically includes “commercial interactions” and “fundamental economic principles or practices” within this grouping. Because Claims 1-5, 7-11, 13-15, 19-20 and 31-32 end-goal – retail assortment and shelf allocation is a commercial activity, it fits in this category. The number of people involved (single v. multiple) does not change the subject matter of these claims, which is the organization of a business process through mathematical modeling.
Argument #8:
(H). Applicant argues that the claimed invention is described as concepts that are performed in the human mind and Applicant asserts that this is factually incorrect under step 2a prong 1 of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 13 of 16, dated 10/16/2025). Examiner respectfully disagrees.
The legal standard of “can be performed” not “is performed”. The Applicant’s argument often relies on the fact that a human is not currently siting and performing these calculations. However, the Federal Circuit and the USPTO 101 Guidance clarifies that a claim is directed to a mental process if the steps can be performed in the human mind or with the aid of pen and paper. The steps of “comparing” two lists (measurement data) and “grouping” similar items (clustering), and “calculating a ratio” to estimate a value are all fundamental logical operations. In CyberSource Corp v. Retail Decisions, Inc., the court held that a method for verifying a credit card transaction was a mental process because it could be performed by a human “mentally or with the aid of pencil and paper” even if it was actually performed by a computer. Automation does not change the nature of the process. The Applicant’s contention that it is “factually incorrect” to call this a mental process because it is performed by “processor circuits” and “machine readable instructions” fails under established law. The “speed fallacy”. Automating a mental process to run at a high speed or across large datasets (like 50 million products) does not change the underlying nature of the process. In SAP America Inc. v. InvestPic, LLC, the court rules that even though the calculations were too complex for a human to perform in real-time, the nature of the activity (statistical analysis) remained an ineligible mental process. A processor circuit is a generic tool. Using a tool to perform a mental process more efficiently is still an abstract idea unless the tool itself is improved (per Enfish). The Applicant’s focus on the “mental process” label ignores that the claim is independently ineligible under other sub-groupings of Step 2A Prong 1. Particularly, Mathematical Concepts: The claims recite “k-means clustering” and “performance metric ratios”. These are mathematical algorithms. Even if the Applicant could prove a human cannot do this in their mind, the claims still recite a Mathematical Concept which is an abstract idea. Organizing Human Activity whereby the goal of – allocating objects to a shelf is a fundamental economic practice. These claims describe the organization of business inventory, which is an abstract method of organizing human commercial interactions.
In conclusion, the Applicant’s argument misinterprets that the “mental process” test as a literal requirement for human feasibility. Because the claims core logic – comparing, grouping and extrapolating ratios – is a series of mental steps and mathematical relationships used to solve a business problem, it remains an abstract idea. The use of “circuitry” and “instructions” merely provides a generic computer environment for the abstract idea’s execution.
Argument #9:
(I). Applicant argues that the claimed invention is analogous to the Diamond v. Diehr case via the 35 U.S.C. § 101 analysis (see Applicant Remarks, 3rd ¶ of Page 13 thru Page 14 of 16, dated 10/16/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that like the Diamond v. Diehr claim, claim 1 improves a technological process (e.g., With over 50 million potential products available in the marketplace, a common technique to simplify the complexity of an assortment strategy and to predict new product impact is to run simulations and that running a simulation is clearly a technological process performed on a computer and that improving the efficiency of such simulations is clearly a solution to a technological process and improves the functioning of a machine (see Applicant Remarks, Pages 13-15 of 18, dated 10/16/2025). Examiner respectfully disagrees. The argument that these claims are analogous to the landmark Diamond v. Diehr case is legally flawed. There is a distinction between a “technological process” and “automated business logic” remains a critical hurdle under 35 U.S.C. 101 step 2a prong 2 due to the following reasons. First, physical transformation vs. information processing. The primary reason these claims fails the Diamond v. Diehr comparison is the absence of a physical transformation. Diamond v. Diehr: The mathematical formula (Arrhenius equation) was integrated into a larger physical process – the curing of synthetic rubber. The invention used a computer to constantly monitor temperature and physically open a press to transform raw rubber into a finished product. These claims transform data, not physical matter. “Allocating an object to an shelf” or “executing a simulation” are logistical and informational outcomes. Unlike Diehr, where the math was tied to a physical industrial machine, here the math is tied to a generic computer performing a business simulation. “Assortment simulation” is not a technological process. The Applicant argues that running a simulation is “clearly a technological process”. However, Federal Circuit precedent (e.g., SAP America v. InvestPic) establishes that simulating or modeling an abstract business or mathematical concept is not a technological improvement. Technological vs. Business Difficulty: Managing “50 million products” is a computational scale challenge, not a technological one. Using a computer to perform a simulation that is too complex for a human to do by hand is merely “automation of a mental process” which Alice prohibits. The function of the machine. A simulation that produces better business results (better shelf placement) does not “improve the functioning of the machine”. To improve the machine, the claim would need to describe how the computer’s efficiency, speed or memory management is enhanced, rather than just using the computer’s existing power to solve a non-technical problem. Failure to Improve a Technical Field. Field of Use: Retail merchandising and assortment strategy are categorized as certain methods of organizing human activities (commercial interactions). Improving the “efficiency” of a business simulation is an improvement in the field of economics/marketing not computer science or engineering. Lack of specificity: Diehr succeeded because it provided a specific technical solution to the problem of under-cured or over-cured rubber. These claims do not provide a specific technological “how-to” for computer science; it provides a “what-to-do” for retail store managers.
Because these claims do not integrate the abstract idea into a practical application because it lacks the physical-technological nexus found in Diamond v. Diehr. The “complexity” addressed is purely commercial, and the “simulation” is a computational tool used to automate a fundamental economic business practice. Therefore, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are patent ineligible under 35 U.S.C. § 101 analysis.
Argument #10:
(J). Applicant argues that the claimed invention is patent eligible under 35 U.S.C. 101 due to the claims recite a technological process improvements are achieved by taking measurements, performing calculations based on these measurements and then controlling the operation of resources based on the calculations via the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 16, dated 10/16/2025). Examiner respectfully disagrees.
Examiner responds by stating that “operation of resources” mentioned allocating objects to a shelf is a merchandising and inventory task, which is a method of organizing human activities. Technological improvement: For a process to be eligible, it must improve the functioning of a machine (e.g., computer speed, memory, or security) or a technological field (e.g., chemical processing or circuit design). Simply automating a business decision (where to place a product) using a computer is considered an ineligible business application. The Federal Circuit recently confirmed in Recentive Analytics, Inc. v. Fox Corp (2025) that applying machine learning to optimize a field of use (like event schedules or network maps) is insufficient without a technical innovation in the underlying technology. Information Collection and Analysis as Abstract Ideas. The “taking of measurements” and “performing calculations” described in these claims are foundational components of information processing. Electric Power Grouping Standard: The Federal Circuit in Electric Power Group, LLC v. Alstom S.A. established that the collection, analysis and display of information – even in real-time is an abstract idea. Using k-means clustering to find a “performance metric ratio” is a math concept. Transforming data form one form to another is a logical process, not a technological one, unless it solves a problem in how the computer handles that data. “Controlling Resources” such as the final step of “causing… new objects to be allocated to a shelf” merely limits the mathematical abstract idea to the retail sector. It does not provide the “something more” required to integrate the idea into a practical application. Lack of a physical transformation. Unlike Dimaond v. Diehr, where calculations controlled a physical chemical reaction (rubber curing), this process controls logistics, which is an intangible business process. Therefore, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are ineligible under 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 112
8. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
9. Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The first paragraph of 35 U.S.C. 112 requires that the “specification shall contain a written description of the invention.” This requirement is separate and distinct from the enablement requirement. See, e.g., Vas-Cath, Inc. v. Mahurkar, 935 F.2d 1555, 1560, 19 USPQ2d 1111, 1114 (Fed. Cir. 1991). See also Univ. of Rochester v. G.D. Searle & Co., 358 F.3d 916, 920-23, 69 USPQ2d 1886, 1890-93 (Fed. Cir. 2004) (discussing history and purpose of the written description requirement). To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. However, a showing of possession alone does not cure the lack of a written description. Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 969-70, 63 USPQ2d 1609, 1617 (Fed. Cir. 2002).
(A). In this instance, Independent Claims 1 and 11 were amended on 10/16/2025 to include the limitations of: “causing the first one of the new objects to be allocated to a shelf of an entity associated with the first channel” and “causing the target object to be allocated to a shelf of an entity in the focus channel”. The language in the Spec doesn’t appear to be enough to satisfy 35 U.S.C. 112(a) in this instance because of the complete absence of the “how” to support the claimed result. For instance, there is no support shown in Applicant’s Specification to corroborate “how” the “causing” “the first one of the new objects to be allocated to a shelf of an entity associated with the first channel” and “causing the target object to be allocated to a shelf of an entity in the focus channel” is achieved either through manual or automated control.
Examiner notes that the only mentioning of “shelf allocation” is shown at paragraph ¶ [0037] which states that: “Thus, an opportunity to predict new product performance of a new product before deciding whether to add the new product to a retailer's assortment and/or deciding which other product(s) to modify (e.g., de-list, modify shelf allocation, etc.), thereby avoiding the overall diminished sales profits and/or revenue effects of a poor modification decision. Although identifying different product assortments to ensure aggregate sales, profits and/or revenue is possible for existing products based on, in part, analysis of historical sales data, new products do not have such historical sales data to facilitate forecasting efforts. Examples disclosed herein are able to search through datasets of thousands of products that include thousands of attributes to identify data of interest, and utilize the data of interest to generate clusters based on selected metrics to be used as proxies for predicting a performance metric of the new product”. The paragraph describes “deciding whether to add the new product” and “deciding which other products to modify” as an opportunity or benefit for the user at paragraph [0037].). Independent Claims 1 and 11 requires the system to “cause” the allocation a functional step of implementation. The specification merely describes a decision-making process that occurs after the system’s output (prediction) is generated). Describing a reason for a human to make a choice does not show possession of a technological system that automatically performs the specific act of “causing” allocation to a physical or virtual shelf. Also “modifying shelf allocation” is a broad concept that could encompass many different actions (e.g., “removing items, changing facings, or adjusting shelf lengths). Disclosure of a broad category (modifying) does not support a specific sub-category (allocating a specific new object) unless there are clear indicators directing a person of ordinary skill to that specific act. The paragraph groups modify shelf allocation with “de-list” as examples of decisions. It fails to provide the specific instructional logic required to bridge the gap between “forecasting efforts” and the actual “allocation” of a new object. Independent Claims 1 and 11 require a specific relationship: the new object must be allocated to a shelf associated with the first channel or focus channel.” Paragraph [0037] discusses “retailer assortment” generally but does not describe the logic for moving or placing objects across different identified channels. The text focuses on the intended result (predicting impact to avoid diminished profits) rather than the manipulative steps (causing allocation) recited in Independent Claims 1 and 11.
The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement. “Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002).
Thus, Independent Claims 1 and 11 fail to satisfy the written description requirement of §112(a) because there is no evidence of a complete specific application or embodiment to satisfy the requirement that the description is set forth “in such full, clear, concise, and exact terms” to show possession of the claimed invention. See Fields v. Conover, 443 F.2d 1386, 1392, 170 USPQ 276, 280 (CCPA 1971). Dependent Claims 2-5, 7-10, 13-15, 19-20 and 31-32 depend from Claims 1/11 and therefore inherit the 35 U.S.C. § 112 (a) deficiency of Claims 1/11 discussed above. Appropriate corrections are required.
Claim Rejections - 35 USC § 101
10. 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.
11. Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are each focused to a statutory category namely a “system” or an “apparatus” (Claims 1-5, 7-10 and 31-32) and a “non-transitory computer readable storage medium” or an “article of manufacture” (Claims 11, 13-15 and 19-20).
Step 2A Prong One: Independent Claims 1 and 11 recite limitations that set forth the abstract idea(s), namely (see in bold except via strikethrough):
“to obtain, based on a request, first measurement data associated with a first channel and second measurement data associated with a second channel different than the first channel” (see Independent Claim 1);
“” (see Independent Claim 1);
“” (see Independent Claim 1);
“compare the first measurement data and the second measurement data to identify (a) new objects present in the second measurement data and not present in the first measurement data, and (b) common objects present in both the first measurement data and the second measurement data” (see Independent Claim 1);
“execute a technique to iteratively cluster the common objects into clusters based on at least one metric” (see Independent Claim 1);
“determine a first performance metric ratio for a first one of the clusters, the first performance metric ratio based on a first portion of the first measurement data associated with the first channel and a second portion of the second measurement data associated with the second channel, the first and second portions based on first ones of the common objects” (see Independent Claim 1);
“determine a first value of a performance metric for a first one of the new objects relative to the first channel based on the first performance metric ratio and the second measurement data” (see Independent Claim 1);
“cause the first one of the new objects to be allocated to a shelf of an entity associated with the first channel” (see Independent Claim 1);
“execute an assortment simulation based on the first one of the new objects” (see Independent Claim 1);
“” (see Independent Claim 11);
“compare a first dataset associated with a focus channel and a second dataset associated with a reference channel different than the focus channel to identify (a) a target object present in the second dataset and not present in the first dataset, and (b) proxy objects present in the first dataset and the second dataset” (see Independent Claim 11);
“execute a technique to iteratively cluster the proxy objects into clusters based on at least one metric” (see Independent Claim 11);
“determine a first performance metric ratio for a first one of the clusters, the first performance metric ratio based on a first portion of the first dataset associated with the focus channel and a second portion of the second data associated with the reference channel, the first and second portions based on first ones of the proxy objects” (see Independent Claim 11);
“determine a first value of a performance metric for the target object relative to the focus channel based on the first performance metric ratio and the second dataset” (see Independent Claim 11);
“cause the target object to be allocated to a shelf of an entity in the focus channel” (see Independent Claim 11);
“execute an assortment simulation based on the target object” (see Independent Claim 11).
Here, for step 2a prong 1, Independent Claims 1 and 11 recite the abstract idea of the mathematical modeling of retail performance data to automate inventory assortment decisions. Independent Claims 1 and 11 focuses on collecting data from two different “channels” (e.g., wo different stores or sales platforms), using a mathematical ratio to estimate how a product from one channel would perform in the other, and then simulating or executing its placement on a retail store shelf. These claims recite (1) “a k-means clustering technique” which is a mathematical algorithm used to partition data into groups” & (2) “determining a ratio”: The calculation of a “performance metric ratio” based on measured data portions is a mathematical calculation. (3) Calculating values: Determining the “first value of a performance metric” based on that ratio is a further mathematical operation. Each of these three steps or features fall under the category of “Mathematical Concepts” which pertains to (1) mathematical calculations or (2) mathematical relationships. Secondly, these claims additionally or alternatively recite (1) “assortment simulation” which is a commercial business process used to predict how product changes affect sales and (2) “inventory allocation” in which “causing an object to be allocated to a shelf of an entity” is a fundamental business practice (inventory management/merchandising). Each of these two steps or features fall under the category of “Certain Methods of Organizing Human Activities” which pertains to (3) commercial interactions (including sales activities or behaviors or business relations) or (4) fundamental economic principles or practices (inventory management/merchandising).
Therefore, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (1) commercial interactions (including marketing or sales activities or behaviors or business relations) or (2) fundamental economic principles or practices (inventory management/merchandising) and additionally or alternatively as “Mathematical Concepts” which pertains to (3) mathematical calculations or (4) mathematical relationships.
That is, other than reciting the additional elements of (e.g., “interface circuitry” & “machine readable instructions” & “at least one processor circuit” & “k-means clustering”), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) commercial interactions (including marketing or sales activities or behaviors or business relations) or (2) fundamental economic principles or practices (inventory management/merchandising) and additionally or alternatively as “Mathematical Concepts” (1) which pertains to (3) mathematical calculations or (4) mathematical relationships.
Moreover, the mere recitation of generic computer components such as (e.g., “interface circuitry” & “at least one processor circuit”) does not take the claims out of “Certain Methods of Organizing Human Activities” or “Mental Processes” groupings.
Therefore, at step 2a prong 1, Yes, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1 and 11 recites additional elements directed to: (e.g., “interface circuitry” & “machine readable instructions” & “at least one processor circuit”). These additional elements have been considered both individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h). The USPTO and courts (e.g., Alice) have established that simply using a computer to perform calculations faster than a human does not constitute a technical improvement. There is no indication that the processor or circuitry is specialized or performs in a way that improves the computer’s internal functioning (e.g., faster data retrieval or better memory management). Instead, the computer is used merely as a tool to execute the mathematical k-means clustering and ratio calculations.
Independent Claims 1 and 11: With respect to the additional elements of (e.g., “k-means clustering technique”) when considered with the recited claim limitations both individually and as an ordered combination (as a whole), these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 according to the following: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment for providing performance predictions for new products by generating a cluster output having product clusters indicative of groups of alike ones of the common products in an sales or business enterprise environment (see MPEP 2106.05 § (h)). These claims are directed to a business problem (retail assortment optimization), not a technical one. Automating a process that could historically be performed by people (comparing product performance across channels) using k-means clustering does not qualify as a technical improvement. Steps such as “allocating to a shelf” or “executing an assortment simulation” are considered to be “field of use” limitations in a retail environment. “Allocating to a shelf” is a fundamental business practice in merchandising. Adding this step to a mathematical algorithm does not transform the algorithm into a patentable invention; it merely limits the use of the algorithm to the retail sector. Because these steps do not impose a meaningful limit on the judicial exception, they do not integrate it into a practical application.
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1 and 11 recites additional elements directed to: (e.g., “interface circuitry” & “machine readable instructions” & “at least one processor circuit”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (h) and See MPEP § 2106.05 (f). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0176]: “The processor circuitry may include and/or cooperate with one or more accelerators. In some examples, accelerators are implemented by logic circuitry to perform certain tasks more quickly and/or efficiently than can be done by a general-purpose processor.” See also Applicant’s Specification ¶ [0182]: “Other types of special purpose circuitry may be present. In some examples, the FPGA circuitry 1400 may also include example general purpose programmable circuitry 1418 such as an example CPU 1420 and/or an example DSP 1422. Other general purpose programmable circuitry 1418 may additionally or alternatively be present such as a GPU, an XPU, etc., that can be programmed to perform other operations.”).
Independent Claims 1 and 11: With respect to the additional elements of (e.g., “k-means clustering technique”) when considered with the recited claim limitations both individually and as an ordered combination (as a whole), these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment for providing performance predictions for new products by generating a cluster output having product clusters indicative of groups of alike ones of the common products in a sales or business enterprise environment (see MPEP § 2106.05 (h)). While k-means clustering is a specific mathematical algorithm, it a standard mathematical technique to group data does not constitute an “inventive” application of mathematics; it is a routine implementation of a mathematical concept. The additional elements of “assortment simulation and shelf allocation” represent the “field of use” (retail/merchandising). Controlling a shelf or running a simulation are business activities. Adding these steps to the end of a mathematical calculation does not transform the nature of these claims.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-5, 7-10, 13-15, 19-20 and 31-32 recite additional elements directed to: (e.g., “elbow method” (Dependent Claim 7) and “silhouette method” (Dependent Claim 7)), when considered individually and as an ordered combination (as a whole) with these claim limitations recite the same abstract idea(s) as shown in Independent Claims 1 and 11 along with further steps/details that are performed as “Certain Methods of Organizing Human Activities” which pertains to (1) commercial interactions (including marketing or sales activities or behaviors or business relations) or (2) fundamental economic principles or practices (inventory management/merchandising) and additionally or alternatively as “Mathematical Concepts” which pertains to (3) mathematical calculations or (4) mathematical relationships.
Dependent Claims 2-5, 8-10, 13-15, 19-20 and 31-32 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1 and 11. Dependent Claim 7: Moreover, with respect to the additional element of (e.g., “elbow method” & “silhouette method”) as recited in Dependent Claim 7, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that are significantly more than the recited judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment for providing performance predictions for new products by generating a cluster output having product clusters indicative of groups of alike ones of the common products in an sales or business enterprise environment (see MPEP § 2106.05 (h)).
The additional element of “k-means clustering” in Independent Claims 1 and 11 do not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
For example; see US PG Pub (US 2019/0205806 A1) – “System and Method for Determining and Implementing Sales Clusters for Stores”, hereinafter Karmakar.
See at least Karmakar at ¶ [0042]: “In examples, the algorithms may be any number of different algorithms or approaches. For example, a K-means algorithm, a K-medoids algorithm, a Wards's Clustering algorithm, and/or a Convex Clustering algorithm may be used. Other examples are possible.”
See at least Karmakar at ¶ [0084]: “Example algorithms which are used for clustering with the optimal numbers of clusters include K-means algorithms, K-medoids (PAM) with Euclidean distance algorithms, K-medoids (PAM) with Manhattan distance algorithms, Ward's Hierarchical Clustering with Euclidean distance algorithms, Ward's Hierarchical Clustering with Manhattan distance algorithms, and Convex Clustering algorithms.” In aspects, the algorithm that has maximum silhouette measure will be recommended for final clustering along with optimal number of clusters.”
For example; see US PG Pub (US 2008/0243815 A1) – “Cluster-Based Assessment of User Interests”, hereinafter Chan.
See at least Chan at ¶ [0038]: “The clusters may be generated using any appropriate type of clustering algorithm that uses item distances to cluster items. Examples include K-means, IsoData, nearest neighbor, and hierarchical type clustering algorithms. The clusters formed by such algorithms are mutually exclusive, meaning that each item is assigned to a single cluster. Specific examples of suitable clustering algorithms are described below in Sections VI-VIII.”
See at least Chan at ¶ [0129]: “By way of background, IsoData stands for “Iterative Self-Organizing Data Analysis Technique.” It is self-organizing in that it differs from standard clustering techniques such as K-Means, where the number of clusters must be pre-specified. Since the number of clusters is generally unknown, IsoData attempts to automatically determine this number by optimizing other criteria, such as the ratio of inter-to-intra cluster scatter.”
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-5, 7-11, 13-15, 19-20 and 31-32 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625