DETAILED ACTION
This Final Office Action is in response Applicant communication filed on
6/24/2025. In Applicant’s amendment, claims 45-58 were amended. Claims 59-64 have been cancelled.
Claims 45-58 are currently pending and have been rejected as follows.
Response to Amendments
Rejections under 35 USC 101 are maintained. Applicant’s amendments necessitated new grounds of rejection under 35 USC 103.
Response to Arguments
Applicant’s 35 USC 101 rebuttal arguments and amendments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 8-10 that claim 45 closely conforms to the eligible claim in Diehr and improves a technological process by improving computational efficiency by determining importance metrics in a more efficient manner than traditional techniques of computationally intensive regression, and by enabling an efficient, real-time technique to identify, measure, and track consumer attribute preference within and between product categories.Examiner respectfully disagrees. The present claim is not analogous to the claim in Diehr. In Diehr, the mathematical formula was applied within the rubber curing process that transformed rubber into a different state and recited the manufacturing process itself. Here, the claim recites result-based functional language to obtain data, “prevent” regression analysis when obtained data is sparse, determine specific features associated with the data, calculate two metrics, and cause the object stock to increase or decrease at a physical location based on the result of either calculation. There is no machine controlled to transform an article into a different state or thing. The claim merely recites a mathematical analysis to inform a business decision of increasing or decreasing inventory, performed by computing components. The increase and/or decrease of inventory at a physical location is insignificant post solution activity and not analogous to Diehr’s controlling of the curing press.
Applicant argues on p. 10-11 that claim 3 of Example 36 was found eligible because the combination of elements improved upon previous inventory management techniques, similar to present claim 45’s technological approach that avoids ”traditional techniques of computationally intensive regression.”Examiner respectfully disagrees. Claim 3 of Example 36 was found eligible at Step 2B because the combination of additional elements (a high‐resolution video camera array for acquiring at least one high resolution image sequence of each item; a memory for storing the acquired image sequences, classification and location data relating to the acquired image sequences, and a recognition model representing contour information and character information of each item; and a processor) improved upon the previous inventory management techniques using computer vision technology by reciting not well-understood, routine, conventional activity in that field. In contrast, claim 45 uses a generic processor and interface circuitry to perform mathematical operations and result-based functional steps without any technical detail.
Applicant's prior art arguments have been fully considered but they are moot in light of thew newly cited Chen reference.
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 45-58 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (apparatus, non-transitory machine-readable medium). Claims 45-58 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 45-51 are directed toward the statutory category of a machine (reciting an “apparatus”). Claims 52-58 are directed toward the statutory category of an article of manufacturer (reciting a “non-transitory machine-readable medium”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 45 and 52 are directed to an abstract idea by reciting obtain data corresponding to an identifier, the data including objects associated with characteristics; prevent regression analysis based on determining the obtained data is sparse; determine a first portion of the data associated with objects having two characteristics; determine a second portion of the data associated with objects having three or more characteristics; calculate first importance metrics associated with the first portion of the data based on a first technique, the first technique based on a binomial likelihood function (BLF); calculate second importance metrics associated with the second portion of the data based on a second technique, the second technique different than the first technique and based on a multinomial likelihood function (MLF); and cause the objects associated with the characteristics to at least one of increase or decrease object stock at physical a location based on the first importance metric and the second importance metric (Example Claim 45).
The claims are considered abstract because these steps recite mathematical concepts like mathematical calculations, mental processes like concepts performed in the human mind (including an observation, evaluation, judgment, opinion), and certain methods of organizing human activity like sales activities or behaviors; business relations. The claims recite steps to obtain data, determine specific features associated with the data, calculate two metrics, and increasing or decreasing product stock at a location based on the result of either calculation. Applicant’s disclosure does not recite a particular problem the claimed steps aim to solve, however, it is understood that the claimed steps aim to improve sales performance and identification of different levels of characteristics that improve consumer purchase behavior (Applicant’s Specification, [0011]-[0012]).
By this evidence, the claims recite a type of mathematical concepts like mathematical calculations, mental processes like concepts performed in the human mind (including an observation, evaluation, judgment, opinion), and certain methods of organizing human activity like sales activities or behaviors; business relations common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., an “apparatus” for determining product characteristics corresponding to purchase behavior).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as interface circuitry; machine-readable instructions; and at least one processor circuit to be programmed by the machine-readable instructions) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Dependent claims 46-51 and 53-58 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps to “obtain” purchase data, “prevent” regression analysis, “determine” a portion of data associated with objects having two characteristics, “determine” a portion of data associated with objects having three or more characteristics “calculate” importance metrics, and “cause” object stock to increase or decrease (Example Claim 45).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 45-58 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
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 45, 47-52, and 54-58 are rejected under 35 USC 103 as being unpatentable over the teachings of
Ouimet, US 20130325653 A1, cite no. 35 on IDS filed 6/28/2024, hereinafter Ouimet, in view of
Chen et al., US 20110035379 A1, hereinafter Chen. As per,
Claims 45, 52
Ouimet teaches
An apparatus comprising: interface circuitry; machine-readable instructions; and at least one processor circuit to be programmed by the machine-readable instructions to: /
At least one non-transitory machine-readable medium comprising machine- readable instructions to cause at least one processor circuit to at least: (Ouimet fig. 5; [0060])
obtain data corresponding to an identifier, the data including objects associated with characteristics; (Ouimet [0048] “For each sale transaction entered into between retailer 10 and consumer 14, information is stored in transaction log (T-LOG) data 16 … The time, date, store, and consumer information corresponding to that purchase are also recorded;” [0049] “T-LOG data 16 contains one or more line items for each retail transaction, such as those shown in Table 1. Each line item includes information or attributes relating to the transaction, such as store number, product number, time of transaction, transaction number, quantity, current price, profit, promotion number, and consumer identity or type number” note the consumer identity and purchase data for the retail transaction collected)
[…];
determine a first portion of the data associated with objects having two characteristics; (Ouimet figs. 10-11; [0076] “FIG. 10 shows individual products 152, 154, 156, and 158 organized into product family 150. In one example, product 152 is a yogurt product under brand A with package size of 170 grams (g), price of $1.00, and list of attributes or ingredients that include cherry flavoring, as shown in FIG. 11 … Consumer service provider 52 analyzes the product information of products 152-158 and determines that the products differ in the flavoring of the yogurt and otherwise have common product attributes. Consumer service provider 52 groups products 152-158 into product family 150 with common brand, size, price, or related product attribute. Product family 150 is stored in database 56 for each product 152-158.” Note the determined number of levels for an attribute such as brand, flavor, and size)
determine a second portion of the data associated with objects having three or more characteristics; (Ouimet fig. 19 noting the three or more characteristics of milk as brand, size, health, freshness, and price; [0113] “The product attributes of each dairy product … are compared to the consumer-defined weighted product attributes”)
[…];
[…];
cause the objects associated with the characteristics to at least one of increase or decrease object stock at physical a location based on the first importance metric and the second importance metric. (Ouimet [0046] “Retailer 10 operates under business plan 12 to set pricing, order inventory, formulate and run promotions, add and remove product lines;” [0118] “The net value NV to consumer 42 is greater than one (CV greater than FP) so the DP1 product family is a possible choice for the consumer. … The net value NV to consumer 42 is positive so the DP1 product family may be a good choice for the consumer. Consumer 42 is likely to buy the DP1 product family because the product attributes align or match reasonably well with the consumer weighted attributes, taking into account the discounted offer. A net value NV greater than one or positive indicates that retailer 46 may receive a positive purchasing decision from consumer 42 because the consumer value CV greater than the final price FP;” [0119] “The net value NV to consumer 42 is less than one so the DP2 product family will not be a good choice for the consumer. Using the second normalizing definition, NV=(2.00-2.90)/2.00=-0.45. The net value NV to consumer 42 is negative so the DP2 product family will not be a good choice for the consumer. Consumer 42 is likely not to buy the DP2 product family because the product attributes do not align or match well with the consumer weighted attributes, taking into account the discounted offer. A net value NV less than one or negative indicates that retailer 46 would likely not receive a positive purchasing decision from consumer 42.” Note the NV corresponding to a threshold and the retailer utilizing the invention to control inventory/add/remove products in response to customer/household preference metrics)
Ouimet does not explicitly teach, Chen however in the analogous art of purchase data analytics teaches
prevent regression analysis based on determining the obtained data is sparse; (Chen [0044] “an efficient method to evaluate the log likelihood l(x.sub.i|z.sub.k) may improve performance in certain example embodiments. For clarity, the following discussion focuses on an example method that only uses the Bernoulli terms, which are very numerous and sparse” note the recognition of sparse data and the use of likelihood scoring instead of regression analysis)
calculate first importance metrics associated with the first portion of the data based on a first technique, the first technique based on a binomial likelihood function (BLF); (Chen [0035] “The latent product model uses a binomial distribution Binom(p) to model each binary variable;” [0037] noting the BLF)
calculate second importance metrics associated with the second portion of the data based on a second technique, the second technique different than the first technique and based on a multinomial likelihood function (MLF); and (Chen [0035] “The latent product model uses … a multinomial distribution Mult(.theta.) for each categorical variable;” [0038] noting the MLF)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Ouimet’s purchase data analytics to include recognizing sparse data and calculating importance metrics using BLF and MLF in view of Chen in an effort to provide an improvement in merchandise recommendation modeling (see Chen ¶ [0021] & MPEP 2143G).
Claims 47, 54
Ouimet teaches
wherein the data corresponds to purchases of the objects, one or more of the at least one processor circuit to determine the number of characteristics by determining a number of brands associated with respective ones of the objects. (Ouimet [0049] “T-LOG data 16 contains one or more line items for each retail transaction, such as those shown in Table 1. Each line item includes information or attributes relating to the transaction, such as store number, product number, time of transaction, transaction number, quantity, current price, profit, promotion number, and consumer identity or type number” noting the purchase data of a product; [0092] “In pop-up window 240, the attributes for brand include brand A, brand B, and brand C. A brand option is provided for each type of dairy product or for the selected type of dairy product.” Note the number of brands available for a type of product)
Claims 48, 55
Ouimet teaches
wherein the identifier corresponds to a household, one or more of the at least one processor circuit to cause the at least one of the increase or decrease of the object stock to the household based on the first importance metric or the second importance metric. (Ouimet [0046] “Retailer 10 operates under business plan 12 to set pricing, order inventory, formulate and run promotions, add and remove product lines;” [0067] “The consumer weighted attribute values reflect the level of importance or preference that the consumer bestows on each product attribute;” [0089] “The shopping list can be aggregated for all items needed by the entire household.” Note the aggregation of household data and the retailer utilizing the invention to control inventory/add/remove products in response to customer/household preference metrics)
Claims 49, 56
Ouimet teaches
wherein one or more of the at least one processor circuit is to cause the decrease of the object stock based on the first importance metric or the second importance metric not satisfying a threshold. (Ouimet [0046] “Retailer 10 operates under business plan 12 to set pricing, order inventory, formulate and run promotions, add and remove product lines;” [0119] “The net value NV to consumer 42 is less than one so the DP2 product family will not be a good choice for the consumer. Using the second normalizing definition, NV=(2.00-2.90)/2.00=-0.45. The net value NV to consumer 42 is negative so the DP2 product family will not be a good choice for the consumer. Consumer 42 is likely not to buy the DP2 product family because the product attributes do not align or match well with the consumer weighted attributes, taking into account the discounted offer. A net value NV less than one or negative indicates that retailer 46 would likely not receive a positive purchasing decision from consumer 42.” Note the NV corresponding to a threshold and the retailer utilizing the invention to control inventory/add/remove products in response to customer/household preference metrics)
Claims 50, 57
Ouimet teaches
wherein one or more of the at least one processor circuit is to cause the increase of the object stock based on the first importance metric or the second importance metric satisfying a threshold. (Ouimet [0046] “Retailer 10 operates under business plan 12 to set pricing, order inventory, formulate and run promotions, add and remove product lines;” [0118] “The net value NV to consumer 42 is greater than one (CV greater than FP) so the DP1 product family is a possible choice for the consumer. … The net value NV to consumer 42 is positive so the DP1 product family may be a good choice for the consumer. Consumer 42 is likely to buy the DP1 product family because the product attributes align or match reasonably well with the consumer weighted attributes, taking into account the discounted offer. A net value NV greater than one or positive indicates that retailer 46 may receive a positive purchasing decision from consumer 42 because the consumer value CV greater than the final price FP.” Note the NV corresponding to a threshold and the retailer utilizing the invention to control inventory/add/remove products in response to customer/household preference metrics)
Claims 51, 58
Ouimet teaches
wherein the first importance metric or the second importance metric satisfies the threshold when at least one of the first or second importance metric is greater than zero. (Ouimet [0118] “The net value NV to consumer 42 is greater than one (CV greater than FP) so the DP1 product family is a possible choice for the consumer. … The net value NV to consumer 42 is positive so the DP1 product family may be a good choice for the consumer. Consumer 42 is likely to buy the DP1 product family because the product attributes align or match reasonably well with the consumer weighted attributes, taking into account the discounted offer. A net value NV greater than one or positive indicates that retailer 46 may receive a positive purchasing decision from consumer 42 because the consumer value CV greater than the final price FP.” Note the NV corresponding to a threshold and the threshold satisfied when it is positive)
Claims 46 and 53 are rejected under 35 USC 103 as being unpatentable over the teachings of
Ouimet in view of Chen in further view of
Zenor et al., US 20170161757 A1, cite no. 49 on IDS filed 6/28/2024, hereinafter Zenor. As per,
Claims 46, 53
Ouimet teaches
[…] and the objects having three or more characteristics are multinomial characteristics. (Ouimet [0092] “In pop-up window 240, the attributes for brand include brand A, brand B, and brand C. A brand option is provided for each type of dairy product or for the selected type of dairy product.” Noting the example with three brands corresponding to a multinomial characteristic including at least three levels)
Ouimet does not explicitly teach, Zenor however in the analogous art of purchase data analytics teaches
wherein the objects having two characteristics are binomial characteristics […]. (Zenor [0015] “As an example, assume that the product sub-type of breakfast bar has three products, in which product “1” is associated with brand “A” and has a strawberry flavor, product “2” is associated with brand “A” and has a chocolate flavor, and product “3” is associated with brand “B” and has a strawberry flavor. In this example, there are two characteristics per product, one for brand and one for flavor. Additionally, each identified characteristic may have two possible values; (1) brand “A” or “B” and (2) flavor chocolate or flavor strawberry.” Note the two levels)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Ouimet’s purchase data analytics and Chen’s BLF and MLF to include a consumer importance metric based on only two levels in view of Zenor in an effort to accurately reflect realistic consumer behavior (see Zenor ¶ [0008] & MPEP 2143G).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20080052205 A1; WO 2014056075 A1; Biswas et al., A Proposed Architecture for Big Data Driven Supply Chain Analytics, 2017.
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624