DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is a FINAL office action in response to the Applicant’s response filed 30 June 2025.
Claims 1, 2, 9-12, 14, and 16-25 have been amended.
The 112b rejections of claims 1-13, and 16 have been overcome by amendments.
Claims 1-25 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed 25 June 2025 with respect to the double patenting rejection have been fully considered but they are not persuasive.
With respect to the claims, the Applicant argued on page 11 of their response, “Applicants note that the instant claims have been amended to recite a purpose of maximizing profit that is not claimed in the co-pending Application No. 18/204,975 nor in U.S. Patent No. 11,341,520, and the feature wherein two or more goods are used in a portfolio of goods is not claimed in co-pending Application No. 18/204,975 or U.S. Patent No. 11,341,520 nor is it disclosed in the cited references Rendahl or Ichimura. Accordingly, it is believed that the double patenting rejections are obviated. Withdrawal of the rejections is warranted and respectfully requested.” The Examiner respectfully disagrees with the Applicant’s interpretation of requirements under Double Patenting and the bounds of their claimed invention. In this case, while the Applicant has amended the claims to address the previous rejections, it is noted that the double patenting rejection has been adjusted below to account for the newly amended claims, and thus will be presented in a newly amended fashion. Therefore, the Examiner maintains that this rejection is proper.
Applicant's arguments filed 25 June 2025 with respect to the 101 rejection have been fully considered but they are not persuasive.
With respect to the claims, the Applicant argues on pages 13 and 14 of their response, “Rather than only being directed towards commercial activity or mental concepts as the Examiner infers, the focus of the claims relates to the ability to optimize pricing generating maximal profits of a portfolio of goods (i.e., two or more goods) while reducing waste in a retail establishment by minimizing spoilage. Being able to perform these functions relies on information that the algorithm uses to both maximize profit while minimizing waste. The information includes one or more of the marginal costs of the at least two goods, cost prices, historical point of sale data and pricing plan, which helps estimate a demand curve, price sensitivity of customers, category spillovers, substitute products, long run-stockout costs in conjunction with inventory policy, elements of long-run strategy, store positioning/image, competitors inventory policy/stockout costs brand strength, or brand prioritization as recited in instant claim 1. By feeding the algorithm with this very concrete information (or having the algorithm calculate this information), the algorithm has the concrete ability to perform simulations that give the retailer the ability to maximize profit in a portfolio of goods, while simultaneously reducing waste. This information that is input to generate the desired output is well beyond simply commercial activity or mental steps. Arguendo, if one were to assert that these are only mental steps, the fact that humans are notoriously illy equipped to perform simulations to ascertain maximal profits, negates that assertion. Rather, a human’s ability to maximize one variable while minimizing another is generally beyond a human’s ability, particularly when forced to consider other information and/or price calculation factors. In contrast, the algorithm of the present invention is able to perform complex calculations that are beyond the abilities of a human to perform as simulations must be done in order to ascertain how maximal profit can be generated while simultaneously minimizing waste. The fact that a human cannot perform these steps show that the present invention is more than simply mental steps or commercial activity. The fact that one is generating a portfolio of goods wherein two or more goods are in the portfolio as is instantly claimed, in addition to the maximizing profit and minimizing waste functionalities adds a level of complexity that simply cannot be performed a human.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that the claims relate to optimizing pricing generating maximal profits of a portfolio of goods (i.e., two or more goods) while reducing waste in a retail establishment by minimizing spoilage, the Examiner notes that this is purely a commercial problem, which can be performed with mental steps, and would encompass the management of commercial activities. Notably, optimizing pricing of goods to maximize profits and reduce waste is purely a commercial activity of pricing, sales activities, and business relations; and thus, the Applicant’s assertions that the claims are not directed to this is not persuasive. Second, with respect to the Applicant’s argument that using information (e.g. one or more of the marginal costs of the at least two goods, cost prices, historical point of sale data and pricing plan, which helps estimate a demand curve, price sensitivity of customers, category spillovers, substitute products, long run-stockout costs in conjunction with inventory policy, elements of long-run strategy, store positioning/image, competitors inventory policy/stockout costs brand strength, or brand prioritization) with an algorithm to perform simulations is not persuasive. In particular, at no point tin claims 1 (or claims 14 and 21) has the Applicant claimed any simulation being done, and thus, such an element is beyond the scope of the claims. Notably, claim 1 has been amended to state, “the dynamic pricing engine being configured to: query, dynamically and in real-time, the database to identify, from the readable identification tags, the at least two goods and the information associated with the at least two goods; and wherein the information comprises one or more of marginal costs of the at least two goods, cost prices, historical point of sale data and pricing plan, which helps estimate a demand curve, price sensitivity of customers, category spillovers, substitute products, long run-stockout costs in conjunction with inventory policy, elements of long-run strategy, store positioning/image, competitors inventory policy/stockout costs brand strength, or brand prioritization; apply the one or more algorithms to the identified at least two goods to: calculate a price of the at least two goods; and modify the calculated price of the at least two goods during the time period to optimize a target function, wherein the optimization depends on the one or more price- calculation factors associated with the shelf-state of the at least two goods and the one or more price-calculation factors associated with the future stock of the at least two goods” (emphasis added). As shown here, the Applicant has claimed accessing a database to retrieve information about items, which can simply be cost prices or a historical point of sale data and pricing plan, and using “algorithms” to calculate a price and modify the price of the goods to optimize a target function. This does not equate to performing simulations, but instead, is purely directed to price calculations, which is commercial activity and can be done mentally. Third, with respect to the Applicant’s argument that the claimed steps cannot be performed by a human mind, the Examiner is not persuaded. Particularly, the Applicant has stated humans are notoriously illy equipped to perform simulations to ascertain maximal profits, however no evidence of this assertion has been made, and as noted above, no such element exists in the claim. Additionally, contrary to this assertion, it is noted that humans can, mentally or with pen and paper, determine pricing to maximize profits, as this has long been practiced by merchants and service providers to price goods and services, even before the existence of computers; thus, merely asserting it can’t be done is not persuasive. Fourth, with respect to the Applicant’s argument that, “the algorithm of the present invention is able to perform complex calculations that are beyond the abilities of a human to perform as simulations must be done in order to ascertain how maximal profit can be generated while simultaneously minimizing waste,” the Examiner is not persuaded. In particular, as noted above, the claims do not encompass performing any simulations, and thus this would beyond the scope of the claims. Additionally, as noted above, the calculations that the claimed “algorithm” performs encompasses calculating a price of products and modifying the calculated price to optimize a target function, which are easily performed in the human mind and are commercial actions. Additionally, it is noted that said calculations are not complex, as argued, as and are merely recitations of solutions/outcomes, without any details about how specifically and particularly the algorithm calculates a price, and modifies the price. As such, the argued complexity has not been shown by the Applicant and thus, this argument is not persuasive. Fifth, it is additionally noted that the requirements under the abstract idea groupings for “Certain Methods of Organizing Human Activity” and “Mental Processes” are not the same, and that “Certain Methods of Organizing Human Activity” does not require that a human be able to perform the elements in their own mind. As the Applicant has failed to rebut the showing that the claims recite the management of commercial activity, and that the particular claim elements identified in the rejection could be performed in the human mind, the Examiner is not persuaded of error. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 14 of their response, “Similar to claim 1, independent claim 21 relies on price calculation factors that include a first expiration date of the at least two goods, a second expiration date of the at least two goods, the quantity the at least two goods in the store, a quantity of similar items from the same or a different seller, a seller's inventory, a sale strength of a brand associated with the at least two goods, a catalog price listed for the at least two goods, a predetermined minimum price allowed for the at least two goods, a demographic area in which the at least two goods are sold, a day of the week in which the at least two goods are sold, any particularly significant days of sale for the at least two goods, an hour of the day in which the at least two goods are sold, a stock protection of the at least two goods, a demand curve for the at least two goods, a system model for each of the at least two goods, sale elasticity factors for the at least two goods, whether certain goods are otherwise already discounted, whether the at least two goods are packaged, type of packaging, a packaged quantity of the at least two goods, whether the at least two goods has any competitors, a last price that was charged for the at least two goods, any rounding rules applicable for the at least two goods, whether there is an active advertising campaign for the at least two goods, a shelf state for the at least two goods, or a price history of the at least two goods. Again, the input of this concrete information into the algorithm allows one to concretely derive a maximal profit while simultaneously minimizing waste. This is more than merely mental thoughts or commercial activity as a human attempting to perform the same functions as the algorithm would surely result in an outcome that is less than ideal and certainly below the profit margin that is attained by using the algorithm.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. With respect to the Applicant’s argument that “independent claim 21 relies on price calculation factors,” and that the input of “this concrete information” into the algorithm allows one to concretely derive a maximal profit while simultaneously minimizing waste,” which is “more than merely mental thoughts or commercial activity as a human attempting to perform the same functions as the algorithm would surely result in an outcome that is less than ideal and certainly below the profit margin that is attained by using the algorithm,” is not persuasive. First, it is noted that the various price calculation factors listed by the Applicant in their argument and in the claim, are listed in the alternative, and thus, only a single factor need be considered. As the Applicant’s factors include basic pricing concepts (i.e. quantity, minimum price allowed, a catalog price, a system model for each of the at least two goods, sale elasticity factors, whether certain goods are otherwise already discounted, a last price that was charged for the at least two goods, or a price history of the at least two goods), the Examiner notes that these factors are not complex and incapable of being considered by a human mind, and instead are merely basic principles of price setting. Second, with regards to the Applicant’s argument that this inputted information allows one to concretely derived maximal profit, it is noted that at no point in the Applicant’s claims has the Applicant defined specifically any algorithm used to calculate the price that would maximize profit, and instead, the Applicant has claimed, “wherein the at least one pricing server comprises a dynamic pricing engine, and wherein the dynamic pricing engine comprises one or more algorithms; applying, by the dynamic pricing engine, the one or more algorithms to the identified at least two goods to: calculate a price of the at least two goods; and modify the calculated price of the at least two goods during the time period to optimize a target function, wherein the optimization depends on the one or more price-calculation factors associated with the shelf-state of the at least two goods and the one or more price- calculation factors associated with the future stock of the at least two goods.” (Emphasis added). As shown and emphasized here, the Applicant’s claim refers to applying the pricing algorithm to calculate a price, and modify the price based on the price calculation factors; however notably, no specific steps or details of the “algorithm” are claimed which disclose how the specific price is calculated, how the price is to be modified, and how the price-calculation factors of the claim and the Applicant’s argument are used to modify said price. As such, the Applicant has merely claimed the solution of calculating a price, and not some concrete method as argued. Third, with respect to the Applicant’s argument that, “this is more than merely mental thoughts or commercial activity as a human attempting to perform the same functions as the algorithm would surely result in an outcome that is less than ideal and certainly below the profit margin that is attained by using the algorithm,” the Examiner is not persuaded. As noted above, commercial activity and mental processes do not have the same requirements when analyzing whether a claim recites an abstract idea in the groupings, and thus, the Applicant’s attempt to combine these different rejections together, is deemed not persuasive. Additionally, as noted above, the claimed elements are fully capable of being performed by a human mind, and the Applicant has failed to provide any evidence or explanation as to why a human mind, alone or with assistance of pen and paper, could not calculate a price for items, or modify the price based on basic pricing factors. Additionally, as noted above, the Applicant has failed to show how calculating a price for items and modifying the price is not commercial activity, as this is simply price setting, managing sales activities, and managing business relations. As such, the Applicant’s arguments are not persuasive, and the examiner maintains that this rejection is proper.
The Applicant continues on page 14 of their response, “Even if the claims as currently amended were construed to be directed to an abstract idea, the claimed invention integrates the exception into a practical application. The practical application is that one is able to maximize profit using the information and price-calculation factors to simultaneously maximize profit while reducing waste. This is a practical application that will lead to financial rewards for the retailer well beyond the amount or level that could be generated by a human in the absence of the algorithm.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. With respect to the Applicant’s argument that the claims recite elements that integrate the abstract idea into a practical application, and particularly the practical application is that one is able to maximize profit using the information and price-calculation factors to simultaneously maximize profit while reducing waste, the Examiner is not persuaded. Notably, maximizing profit while reducing wastes, is not a reason in accordance with MPEP 2106.04(d) and 2106.05 as to why an abstract idea can be integrated into a practical application. Notably, MPEP 2106.04(d)(I) states, “The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h).” (Emphasis added). In addition, it is noted that MPEP 2106.05(I) states, “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"). Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).” (Emphasis added). As shown and emphasized here, the determination of whether a recited abstract idea is integrated into a practical application is whether the claim recites additional elements beyond the abstract idea, that when taken individually and in combination with the abstract idea, show eligibility under MPEP 2106.05(a)-(c) and (e). In this case, the Applicant’s argument has failed to identify any additional elements in the claim, nor have they identified any specific elements of the claim in general, and instead merely asserted that the practical application is that one is able to maximize profit using the information and price-calculation factors to simultaneously maximize profit while reducing waste; which is not a reasoning in any section as stated above. Additionally, the Applicant has asserted that this, “will lead to financial rewards for the retailer well beyond the amount or level that could be generated by a human in the absence of the algorithm,” however this appears to be no more than an improvement in the abstract idea of optimizing pricing. MPEP 2106.05(a)(II) states, “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” (Emphasis added). In this case, maximizing profit and providing financial rewards would be an improvement in the business process of pricing and managing sales activities, and not an improvement in computer functionality, another technology or technical field. As such, the Applicant has failed to show that they have integrated the abstract idea into a practical application, and instead, merely showed that they have conducted an abstract idea. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 15 of their response, “Accordingly, the claim includes significantly more than the abstract idea under the view that ‘software can make non-abstract improvements to computer technology just as hardware improvements can’. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016). As in Enfish, the improvement claimed here is ‘bolstered by the specification’s teachings that the claimed invention achieves’ a reduction in both food waste while maximizing profit for sellers.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. With respect to the Applicant’s argument that the Applicant’s claims are similar to Enfish the Examiner is not persuaded. Notably, MPEP 2106.05(I) states, “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691.” (Emphasis added). As shown here, the Court identified that the claims in Enfish, as supported by the specification, improved the functioning of a computer. Unlike this, the Applicant’s claims do not improve computer functionality, the Applicant’s specification does not set forth any such improvement, nor does the Applicant’s argument set forth such a case. Instead, the Applicant’s argument has asserted the claimed improvement in the abstract idea of price setting sand sales activities, and thus, insufficient to show improvement in computer functionality or another technology. Therefore, the Examiner maintains that this rejection is proper.
Applicant's arguments filed 25 June 2025 with regards to maximizing profit, while reducing waste, while simultaneously using it on a portfolio of goods have been fully considered but they are not persuasive.
With respect to the claims, the Applicant argues on pages 15 and 16 of their response, “None of the cited references disclose or suggest maximizing profit while reducing waste, while simultaneously using it on a portfolio of goods (i.e., two or more different types of goods) as is instantly claimed in independent claims 1, 14, and 21. The only reference that discusses multiple goods is Rendahl, however, Rendahl is limited to “bundling” the same good and therefore Rendahl does not generate a “portfolio” of goods (i.e., at least two separate goods) as is instantly claimed. None of the references make up for the deficiencies of Rendahl. Wohlert, Murakami, Ichimura, or Averbuch also fail to disclose the ability to generate a portfolio of goods as is instantly claimed. Accordingly, no combination of the references discloses or suggests the elements as disclosed in instant claims 1, 14, and 21 meaning that a proper prima facie case of obviousness has not been presented. Because all other dependent claims are properly dependent from these claims, they too cannot be rendered prima facie obvious by the cited references. Withdrawal of the rejection is warranted and respectfully requested.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art of record and the bounds of the claimed invention. First, the Examiner notes Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. In this case, the Applicant has failed to identify any claim language in the claims which reflects the Applicant’s arguments, and instead merely made a general assertion that the cited prior art does not disclose high level concepts disclosed in the claims. Additionally, with respect to the cited prior art, the Applicant has failed to specifically explain why the cited references do not disclose the argued “limitations,” and merely asserts without evidence that the references do not disclose maximizing profit while reducing waste, while simultaneously using it on a portfolio of goods. Second, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a portfolio of goods being two or more different types of goods) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In this case, claim 1 states, “A system for optimizing pricing of a portfolio of goods to maximize profit while reducing waste in a retail setting, the system comprising: at least two goods; readable identification tags affixed to said at least two goods; at least one server comprising: a dynamic pricing engine comprising one or more algorithms; a memory; a processor; and a database configured to store information associated with the at least two goods, wherein the information comprises one or more price-calculation factors associated with a shelf-state of the at least two goods and one or more price-calculation factors associated with a future stock of the at least two goods; and a barcode scanner connected to a point-of-sale (POS) terminal in a store, the POS terminal being connected to the at least one server, wherein the barcode scanner is configured to scan the readable identification tags affixed to the at least two goods during a time period; the dynamic pricing engine being configured to: query, dynamically and in real-time, the database to identify, from the readable identification tags, the at least two goods and the information associated with the at least two goods; and wherein the information comprises one or more of marginal costs of the at least two goods, cost prices, historical point of sale data and pricing plan, which helps estimate a demand curve, price sensitivity of customers, category spillovers, substitute products, long run-stockout costs in conjunction with inventory policy, elements of long-run strategy, store positioning/image, competitors inventory policy/stockout costs brand strength, or brand prioritization; apply the one or more algorithms to the identified at least two goods to: calculate a price of the at least two goods; and modify the calculated price of the at least two goods during the time period to optimize a target function, wherein the optimization depends on the one or more price- calculation factors associated with the shelf-state of the at least two goods and the one or more price-calculation factors associated with the future stock of the at least two goods; and the at least one server being configured to transmit at least one calculated price of the at least two goods based on an expiration date of the at least two goods to a system managing one or more displays in real-time, wherein the system managing one or more displays in real-time further comprises at least one retailer server, the at least one other server in communication with the retailer server, and the at least one other server possessing algorithms and analytics tools that are able to provide the retailer server and a retailer with data, the data comprising information on maximizing profit, optimal product portfolio configurations and/or food spoilage.” As shown here, the Applicant’s claims have been amended to state calculating the price of for at least two goods, and modifying the calculated price to maximize a target function. This claim does not recite a portfolio of goods being two or more different types of goods, nor does it disclose that the items are bundled together. As such, the Applicant’s arguments are not persuasive as not being reflective of the claimed invention. Third, with respect to the cited art, it is noted that Wohlert states in paragraph 33, “In one example, AS 120 and/or AS 125 may perform various functions relating to providing a notification of a discount price for one or more items according to an expiration date. However, in one example, AS 120 and/or AS 125 may further interact with AS 127 to provide such functions. For instance, AS 125 may calculate recommended price points for an item based upon data that is received from the merchant via AS 127, and/or from other merchants. AS 125 may then communicate the recommended price points to AS 127. As such, AS 127 may offer to a customer using endpoint device 190 a discount price for the item at a particular time based upon the recommended price points received from the other AS 125. In this regard, it should be noted that any one or more of AS 120, 125 and/or 127 may calculate discount prices for one or more items based upon approaching expiration dates using aspects of historical data supplied from one or more sources, “local” data regarding inventory and expiration date(s), or a hybrid approach combining both aspects to determine the dynamic pricing of various item being sold by a merchant. Other, further and different variations of the same or a similar nature are all contemplated within the scope of the present disclosure.” (Emphasis added). Wohlert continues in paragraph 49, “At optional step 320, the method 300 calculates a discount price for the at least one item based upon the expiration date. For example, as described above price points may be determined as a function of an item's expiry time and a sales probability at a given price point. In one example, the sales probability is based upon historical sales rates observed at various times prior to a product's expiration and at various offered prices. In one example, historic data includes data not just from the merchant's store, but may be aggregated over a plurality of stores, e.g., in a geographic area or region. In another example, the method implements an algorithm-based approach that accounts for “local” data such as a current level of inventory of the same the item in the store and the various expiration dates of the stock of inventory. In still another example, the discount pricing decision implements a hybrid approach that accounts for historic data as well as the available “local” data.” (Emphasis added). As shown and emphasized here, Wohlert has disclosed calculating a price and discounted price for a plurality of items based on price-calculation factors, including the expiration date of the items. It is also noted that Rendahl states in paragraph 21, “The illustrative embodiments provide an improved computing tool and improved computing tool functionality that implements a plurality of artificial intelligence (AI) computer models that are trained through machine learning processes to make various predictions regarding product sales opportunities, and especially with regard to bulk or bundled sales. Bulk sales refers to a plurality of products beings sold together at the same time to a customer, e.g., 5 toothpaste tubes, where these products may be of the same or different types or classifications. Bundled sales may be synonymous to bulk sales, or may be considered a subset of bulk sales, specifically directed to a plurality of different types of products or products having different classifications being sold at the same time to a customer, e.g., shower gel and a loofah sponge, toothpaste and toothbrushes, etc. While the products may be related to one another in bundled sales, such as in these previous examples, because customers tend to want to buy bundled products that address a common situation or issue, having a relationship is not a requirement for bundled sales, e.g., one may bundle chocolate treats with a tool set. For purposes of the present description, the terms “bulk” and “bundled” will be used interchangeably hereafter to reference sales opportunities involving a plurality of product units, which may be of the same or different types/classifications.” (Emphasis added). As shown and emphasized here, Rendahl has disclosed the use of bulk sales to sell two or more products to customers, wherein the products can be the same or different. Rendahl continues in paragraph 28, “The AI pipeline of the illustrative embodiments provides trained AI computer models, trained through machine learning processes, that perform specific pattern recognition and generate corresponding predictions/classifications based on these recognized patterns with regard to product and customer data of various types, so as to identify bundled or bulk product sales opportunities and generate recommendations regarding such bundled/bulk product sales. For example, the AI computer models of the illustrative embodiments predict which product units, out of available inventory, can be created as a bulk sale option versus being sold individually. The AI computer models of the illustrative embodiments also identify what the bulk size and product mix (units from the inventory) should be and recommend the price of each set of bulk product units to maximize profit in the shortest amount of time. The AI pipeline of the illustrative embodiments generates predictions, recommendations, and creates bulk size offerings based on customer base, type of products, and patterns of previous purchases. These predictions of products to be sold in bulk and the bulk size are dynamic over time, and may be determined based on evaluations of how soon the inventory should be sold out based on inventory attributes. In some illustrative embodiments, the results generated by the AI pipeline may be serve as a basis for dynamically creating temporal social network channels to market the bulk/bundled combinations.” (Emphasis added). Rendahl continues in paragraph 66 states, “In a sixth stage logic 226 operation, the product bulk/bundle recommendation engine 220 operates to determine the expansion/contraction of the geographical reach for bulk/bundled product sales based on live inventory. In this sixth stage logic 226 operation, the product bulk/bundle recommendation engine 220 identifies regional retail establishments, e.g., retailer corresponding to retailer inventor computing system 280, that need to execute bulk/bundled product sales in order to push their inventory at a lower price due to their current inventory levels, taking into account the expiration dates, processing requirements, spoilage, etc. For retail establishments that need to execute such bulk/bundled product sales, predictions regarding additional staffing and space may be determined.” (Emphasis added). As shown and emphasized here, Rendahl has disclosed grouping multiple items together, wherein prices for the bundled items are determined in order to maximize sales, and wherein the prices are adjusted based on expiration date and information. As such, Rendahl has additionally disclosed generating a “portfolio” of goods and pricing the “portfolio,” contrary to the Applicant’s assertions; and thus, Wohlert and Rendahl disclose the claimed invention. Therefore, the Examine maintains that this rejection is proper.
Priority
The Applicant’s claimed priority as a Continuation-in-Part to 18204975 is recognized. With respect to this, however, claims 1-25 will receive priority only to the filing date of this office action, as the parent application does not provide support for, “wherein the system managing one or more displays in real-time further comprises at least one retailer server, the at least one other server in communication with the retailer server, and the at least one other server possessing algorithms and analytics tools that are able to provide the retailer server and a retailer with data, the data comprising information on maximizing profit, optimal product portfolio configurations and/or food spoilage,” (as disclosed in claim 1). Additionally, With respect to claims 1, 14, and 25, application 18204975 does not disclose, “a system for optimizing pricing of a portfolio of goods to maximize profit while reducing waste in a retail setting, the system comprising: at least two goods; readable identification tags affixed to said at least two tags…,” as now claimed. Specifically, 18204975 refers to optimizing the price for a single good, which has a tag attached to it, not a portfolio of goods as now claimed. Additionally, with respect to claim 1, 18204975 does not disclose, “wherein the information comprises one or more of marginal costs of the at least two goods, cost prices, historical point of sale data and pricing plan, which helps estimate a demand curve, price sensitivity of customers, category spillovers, substitute products, long run-stockout costs in conjunction with inventory policy, elements of long-run strategy, store positioning/image, competitors inventory policy/stockout costs brand strength, or brand prioritization.” Additionally, with respect to claim 14 has been amended state, “apply the one or more algorithms to the identified at least two goods to: calculate a price of the at least two goods; and modify the calculated price of the at least two goods during the time period to optimize a target function, wherein the optimization depends on the one or more price-calculation factors associated with the shelf-state of the at least two goods and the one or more price-calculation factors associated with the future stock of the at least two goods; and the at least one pricing server being configured to transmit at least one calculated price of the at least two goods based on an expiration date of the at least two goods to at least one retailer server managing one or more displays in real-time, wherein each of the one or more displays is selected from the group consisting of: an electronic shelf-label, a screen affixed to an entire width of a shelf, a display of a mobile device, a sticker, a display associated with the POS terminal, and a display displaying optimal product mix configurations to reduce waste and maximize profit,” which lacks disclosure for similar reasons as claim 1 above. In addition, claim 21 has been amended to state, “ wherein the one or more price-calculation factors comprise a first expiration date of the at least two goods, a second expiration date of the at least two goods, the quantity the at least two goods in the store, a quantity of similar items from the same or a different seller, a seller's inventory, a sale strength of a brand associated with the at least two goods, a catalog price listed for the at least two goods, a predetermined minimum price allowed for the at least two goods, a demographic area in which the at least two goods are sold, a day of the week in which the at least two goods are sold, any particularly significant days of sale for the at least two goods, an hour of the day in which the at least two goods are sold, a stock protection of the at least two goods, a demand curve for the at least two goods, a system model for each of the at least two goods, sale elasticity factors for the at least two goods, whether certain goods are otherwise already discounted, whether the at least two goods are packaged, type of packaging, a packaged quantity of the at least two goods, whether the at least two goods has any competitors, a last price that was charged for the at least two goods, any rounding rules applicable for the at least two goods, whether there is an active advertising campaign for the at least two goods, a shelf state for the at least two goods, or a price history of the at least two goods.” Particularly, while it is noted that column 7 line 48 through column 8 line 10 of 11341520 recites similar elements, these recitations are for a singular good, not “a portfolio of goods,” as now claimed in the present invention, which as noted above, is not disclosed in the previous patent 11341520.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-4 and 6-13 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 3, 4, and 5 of U.S. Patent No. 11341520 (hereinafter 11341520), in view of Wohlert et al. (US 2015/0317667 A1) (hereinafter Wohlert), and further of in view of Rendahl et al. (US 2024/0311853 A1) (hereinafter Rendahl).
With respect to claim 1, the Applicant claims:
at least two goods; readable identification tags affixed to said at least two goods
at least one server comprising: a dynamic pricing engine comprising one or more algorithms; a memory; a processor; and
a database configured to store information associated with the at least two goods, wherein the information comprises one or more price-calculation factors associated with a shelf-state of the at least two goods and one or more price-calculation factors associated with a future stock of the at least two goods; and
a barcode scanner connected to a point-of-sale (POS) terminal in a store, the POS terminal being connected to the at least one server, wherein the barcode scanner is configured to scan the readable identification tags affixed to the at least two goods during a time period;
the dynamic pricing engine being configured to: query, dynamically and in real-time, the database to identify, from the readable identification tags, the at least two goods and the information associated with the at least two goods; and
wherein the information comprises one or more of marginal costs of the at least two goods, cost prices, historical point of sale data and pricing plan, which helps estimate a demand curve, price sensitivity of customers, category spillovers, substitute products, long run-stockout costs in conjunction with inventory policy, elements of long-run strategy, store positioning/image, competitors inventory policy/stockout costs brand strength, or brand prioritization
apply the one or more algorithms to the identified at least two goods to: calculate a price of the at least two goods; and
modify the calculated price of the at least two goods during the time period to optimize a target function, wherein the optimization depends on the one or more price- calculation factors associated with the shelf-state of the at least two goods and the one or more price-calculation factors associated with the future stock of the at least two goods; and
the at least one server being configured to transmit at least one calculated price of the at least two goods based on an expiration date of the at least two goods to a system managing one or more displays in real-time,
wherein the system managing one or more displays in real-time further comprises at least one retailer server, the at least one other server in communication with the retailer server, and
the at least one other server possessing algorithms and analytics tools that are able to provide the retailer server and a retailer with data, the data comprising information on maximizing profit, optimal product portfolio configurations and/or food spoilage.
Claim 1 of 11341520 states:
a good; a readable identification tag affixed to a good;
a server comprising: a dynamic pricing engine comprising one or more algorithms; a memory; a processor; and
a database configured to store information associated with the good, wherein the information comprises one or more price-calculation factors associated with a shelf-state of the good and one or more price-calculation factors associated with a future stock of the good;
the dynamic pricing engine being configured to: query, dynamically and in real-time, the database to identify information associated with the good;
dynamically access the one or more price-calculation factors associated with the shelf-state of the good and the one or more price-calculation factors associated with the future stock of the good;
apply the one or more algorithms to the identified good to: calculate a first price of the good during a first time period;
modify the calculated price of the good during the first time period to optimize a target function, wherein the optimization depends on the one or more price-calculation factors associated with the shelf-state of the good and the one or more price-calculation factors associated with the future stock of the good;
calculate a second price of the good during a second time period;