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 in reply to the communications filed on February 17, 2026. The Applicant’s Amendment and Request for Reconsideration has been received and entered.
Claims 1-12 are currently pending. Claims 3-9 have been withdrawn in response to the restriction requirement. Claims 1, 2, and 10-12 have been examined in this application.
Application 18/423,860, filed January 26, 2024, is a Continuation of Application 16/264,528, filed January 31, 2019, claims priority from Provisional Application 62/756969, filed November 07, 2018.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 17, 2026 has been entered.
Response to Arguments
Applicant’s amendments necessitated the new grounds of rejection.
Applicant's election with traverse of the restriction (election) requirement is acknowledged. The traversal is on the ground(s) that the claims do not overlap in scope, are not obvious variants, and examination of the entire application could be made without serious burden. This is not found persuasive because
Claims 1, 2, and 10-12 have separate utility such as loading digital data corresponding to a location for the provision of the physical agronomic product package via a web application to a rebate engine, wherein the digital data comprises static data and dynamic data, wherein the dynamic data is selected from the group consisting of purchase history, demographic information, names, types of products desired to purchase, volume required, recommendations based on a general agronomic condition, conditions common to turf managers, and combinations thereof, wherein the conditions common to turf managers comprise control of problematic weeds and diseases common to the turfgrass manager; wherein the dynamic data changes asynchronously over time as new information is added or changed in real time, wherein the static data comprises product use guidelines, contact information, location details, segment, and combinations thereof; wherein the location details comprise size and topography; evaluating the agronomic product package and calculating any available rebate based on rebate parameters assessed using a base algorithm and loyalty parameters assessed using a loyalty algorithm via the rebate engine, wherein calculating the any available rebate is generated and output following parallel processing of the base algorithm and the loyalty algorithm, and wherein at least one of the base algorithm and the loyalty algorithm are configured to run in a circular loop; wherein the base algorithm assesses rebate parameters comprising minimum quantities, volume discounts, strategic bundles, strategic brands, multiple brands, overall purchase value, and combinations thereof; wherein the loyalty algorithm assesses loyalty parameters comprise loyalty data; wherein the loyalty data comprises static and dynamic loyal data, wherein the static loyalty comprises a static value based on a turfgrass manager's prior year loyalty and the value is fixed for a period of time, wherein the static value is reassessed at the end of the fixed period, wherein the dynamic loyalty data comprises dynamic criteria, wherein the dynamic criteria is selected from the group consisting of period-to- date purchase activity, changes in purchase activity compared to a prior period, simulated "share of wallet" calculations, portfolio/strategic brand support, and combinations thereof; wherein the dynamic data is updated in real time to access the information in real time across a supply chain; retrieving a transactional rebate produced by the base algorithm and a loyalty rebate produced by the loyalty algorithm from the rebate engine; calculating multidimensional features in view of the digital data and the transactional rebate produced by the base algorithm and loyalty rebate produced by the loyalty algorithm via the rebate engine to generate a list comprising recommended products, recommended services, available transactional rebates, reward-based programs, loyalty-based programs, or a combination thereof, wherein the multidimensional features comprise a prior purchase by a third party located in the vicinity of the location and criterion selected from the group consisting of a user response to a prompt based on solving a problem the user has identified, pricing based on volume, a user history, and combinations thereof, generating an optimized order protocol for the user; using the personalized and optimized order protocol to generate a product or volume suggestion for the user further comprising a recommendation of a herbicide to reduce the risk of weed resistance; displaying the product or the volume suggestion based on the personalized and optimized order protocol via the web application; finalizing the physical agronomic product package, wherein the method permits a purchaser to obtain requirements for a predetermined period of time for the location; wherein the list and at least the product or the volume suggestion are constantly updated with a second list and at least a second product or a second volume suggestion in light of the adjusted digital data by the user via re-generating the second list and at least the second product or the second volume suggestion by re- calculating the multidimensional feature in view of the adjusted digital data and the transactional rebate and calculated loyalty rebate; and provisioning the finalized physical agronomic product package for the location for purchase by the user, classified in G06Q 30/0621.
Claims 3-9 have separate utility such as assessing profitability of said user's product selection and any rebates associated therewith, determining gross dollar volume of purchase by said user over a predetermined period of time, determining applicability of whether user needs are met by multiple brands, assessing whether user can take advantage of supplier priority brands and/or predetermined agronomic solution bundles, wherein based on the number or variable criteria and combination of static and dynamic data, a built-in algorithm provides unique rebates specific to the user according to variable outcome directives, classified in G06Q 30/0207.
Consequently, while the claims may have some overlap in scope, the distinctions in scope are shifted enough to require separate searches in different classifications. Thus, the restriction is still deemed proper and is therefore made FINAL.
Regarding the rejection of claims 1, 2, and 10-12 under 35 USC 101, Applicant’s arguments have been fully considered but they are not persuasive for the reasons set forth infra.
Additionally, Applicant asserts that “The claims as a whole are in fact implemented for creating a physical agronomic product package which is determined based on an order protocol for a specific user for a specific location that are generated based on specific dynamic and static criteria and are informed by a rebate or reward engine to optimize the product package and implement financial leverage. Further, order and package creation optimization is implemented based on the unique method incorporating the system as claimed, and the specified performance of processing the specified digital elements. In some aspects, the inventive concept lies in the improvement over conventional methods of agronomic planning which streamlines those processes through the leverage of specific identified dynamic and static digital data and further leverages financial incentive algorithms. The claimed method provides for the improved provisioning of a physical agronomic product package that enables improved flexibility in product selection, overcomes limiting factors in product availability and selection, and leverages value capture [emphasis added].”
The Examiner respectfully argues that “providing a physical agronomic product package for purchase by a user” and “improved provisioning of a physical agronomic product package that enables improved flexibility in product selection, overcomes limiting factors in product availability and selection, and leverages value capture” is not related to an improvement in any technology, but rather, is an improvement to business functions and operations. (App. Spec. [0002]). As per Applicant’s assertions this improvement results in a business being able to create “a physical agronomic product package which is determined based on an order protocol for a specific user for a specific location that are generated based on specific dynamic and static criteria and are informed by a rebate or reward engine to optimize the product package and implement financial leverage”, which is a business improvement, not a technological improvement on any technology. Indeed, per Applicant’s specification “manufacturer or supplier would enjoy reduced costs via removing a PAK assembly and management, simplifying administration, reducing marketing material and working capital while driving top-line sales” (emphasis added)(App. Spec. [0002]-[0004]). An improvement is not necessarily a technological improvement merely because technology is used in its implementation.
Regarding Applicant’s assertions that “the claims are directed to a technological solution to a technological problem” -- the Examiner respectfully argues that the present invention is directed to a problem that arises in the agronomy and therefore has a pre-Internet analog. As per Applicant’s specification, dated January 3, 2019, “Turfgrass and ornamental plant management professionals (e.g., golf courses, lawn & landscape maintenance companies, sports turf managers, sod farmers) require a significant amount of time and resources to perform their daily functions. A key time-consuming requirement for these managers is agronomic planning which includes agronomic purchase plans designed to optimize financial leverage of seller sales programs of rebates and payment terms. Turfgrass and other ornamental plants are professionally managed in multiple ways to provide functional and aesthetic benefits. Accordingly, turfgrass is a highly sought-after premium, and generally expensive, product. Various pests, such as weed, insect and fungal pests, can pose costly threats to the professional turf and ornamental manager, especially premium or exclusive golf courses, sports fields, residences or commercial properties known for their aesthetics. In fact, the median annual maintenance cost to golf courses is in excess of 1.2 million dollars (see, for example, clubbenchmarking.com/blog/golf-course-maintenance-how-much-should-you-spend). Therefore, there is an ongoing need for efficient and automated means of planning and purchasing agronomic products for maintaining turfgrass and other ornamental plants to efficiently procure such products.” (App. Spec. Para [0002]-[0004]). Indeed, as per specification, the invention is directed to resolve a business problem (through automation/applying it on a computer) that arises from the physical, pre-Internet world.
Moreover, Applicant asserts “the claims cannot be distilled into any idea that can be practicably carried out in the human mind, which MPEP § 2106.04(a)(2)(III)(A) states that ‘a claim with limitations that cannot practically be performed in the human mind does not recite a mental process.’ Accordingly, at the very least the claims cannot be said to merely be a mental process.” The Examiner respectfully argues that the following steps of “loading data corresponding to a location for the provision of the physical agronomic product package to a rebate, wherein the data comprises static data and dynamic data, wherein the dynamic data is selected from the group consisting of purchase history, demographic information, names, types of products desired to purchase, volume required, recommendations based on a general agronomic condition, conditions common to turf managers, and combinations thereof, wherein the conditions common to turf managers comprise control of problematic weeds and diseases common to the turfgrass manager; wherein the dynamic data changes asynchronously over time as new information is added or changed in real time, wherein the static data comprises product use guidelines, contact information, location details, segment, and combinations thereof; wherein the location details comprise size and topography; evaluating the agronomic product package and calculating any available rebate based on rebate parameters assessed using a base algorithm and loyalty parameters assessed using a loyalty algorithm via the rebate, wherein calculating the any available rebate is generated and output following the base algorithm and the loyalty algorithm; wherein the base algorithm assesses rebate parameters comprising minimum quantities, volume discounts, strategic bundles, strategic brands, multiple brands, overall purchase value, and combinations thereof; wherein the loyalty algorithm assesses loyalty parameters comprise loyalty data; wherein the loyalty data comprises static and dynamic loyal data, wherein the static loyalty comprises a static value based on a turfgrass manager's prior year loyalty and the value is fixed for a period of time, wherein the static value is reassessed at the end of the fixed period, wherein the dynamic loyalty data comprises dynamic criteria, wherein the dynamic criteria is selected from the group consisting of period-to-date purchase activity, changes in purchase activity compared to a prior period, simulated "share of wallet" calculations, portfolio/strategic brand support, and combinations thereof; wherein the dynamic data is updated in real time to access the information in real time across a supply chain; retrieving a transactional rebate produced by the base algorithm and a loyalty rebate produced by the loyalty algorithm from the rebate engine; calculating multidimensional features in view of the digital data and the transactional rebate produced by the loyalty algorithm and loyalty rebate produced by the algorithm via the rebate to generate a list comprising recommended products, recommended services, available transactional rebates, reward-based programs, loyalty-based programs, or a combination thereof, wherein the multidimensional features comprise a prior purchase by a third party located in the vicinity of the location and criterion selected from the group consisting of a user response to a prompt based on solving a problem the user has identified, pricing based on volume, a user history, and combinations thereof, generating an optimized order protocol for the user; using the personalized and optimized order protocol to generate a product or volume suggestion for the user further comprising a recommendation of a herbicide to reduce the risk of weed resistance; displaying the product or the volume suggestion based on the personalized and optimized order protocol; finalizing the physical agronomic product package, wherein the method permits a purchaser to obtain requirements for a predetermined period of time for the location; wherein the list and at least the product or the volume suggestion are constantly updated with a second list and at least a second product or a second volume suggestion in light of the adjusted data by the user via re-generating the second list and at least the second product or the second volume suggestion by re-calculating the multidimensional feature in view of the adjusted data and the transactional rebate and calculated loyalty rebate; and provisioning the finalized physical agronomic product package for the location for purchase by the user” (emphasis added) relates to mental processes, including, inter alia, the observation and evaluation of information. Indeed, "courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation." (MPEP § 2106). Moreover, claims that could be performed in the human mind but are recited as being performed by computer are still "mental processes." While the claims, recite “parallel processing”, the Examiner respectfully argues that parallel processing merely implements the abstract idea on a computer environment. The claim limitation “provisioning the finalized physical agronomic product package for the location for purchase by the user” (emphasis added) is recited at a high level of generality and could include anything to do with the preparation to fulfill the package, thus still falling under the abstract idea.
The Examiner respectfully notes that as the “core inventive concept” is “creating a physical agronomic product package which is determined based on an order protocol for a specific user for a specific location that are generated based on specific dynamic and static criteria and are informed by a rebate or reward engine to optimize the product package and implement financial leverage” (emphasis added), as reflected by the claims -- the claims also relate to certain methods of organizing human activity, particularly commercial interactions including advertising, marketing, and sales activities/behaviors.
Applicant further asserts “the claims are directed to patent eligible subject matter because the claims contain significantly more than any abstract idea.”
The Examiner respectfully argues that while the claims may recite detailed steps, they are merely detailed steps of the abstract idea, as established supra, and thus, do not amount to significantly more than the abstract idea. The technical elements of performing the steps using digital data and parallel processing and via a web application and engine -- merely implements the abstract idea on a computer environment. As stated supra, the claim limitation “provisioning the finalized physical agronomic product package for the location for purchase by the user” (emphasis added) is recited at a high level of generality and could include anything to do with the preparation to fulfill the package, thus still falling under the abstract idea. Similarly, the limitations are not indicative of integration into a practical application if they merely add the words “apply it” (or an equivalent) with the judicial exception, or are merely instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). In the present application, as established supra, taking the claim elements separately, the additional elements of performing the steps using digital data and parallel processing and via a web application and an engine merely implement the abstract idea on a computer environment. Considered in combination, the steps of Applicant’s method add nothing that is not already present when the steps are considered separately. Thus, the claims do not recite additional elements that integrate the judicial exception into a practical application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, and 10-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A – Prong One. If the claims fall within one of the statutory categories, it must then be determined whether the claims recite an abstract idea, law of nature, or natural phenomenon.
Step 2A – Prong Two. If the claims recite an abstract idea, law of nature, or natural phenomenon, it must then be determined whether the claims recite additional elements that integrate the judicial exception into a practical application. If the claims do not recite additional elements that integrate the judicial exception into a practical application, then the claims are directed to a judicial exception.
Step 2B. If the claims are directed to a judicial exception, it must be evaluated whether the claims recite additional elements that amount to an inventive concept (i.e. “significantly more”) than the recited judicial exception.
In the instant case, claims 1, 2, and 10-12 are directed to a process.
A claim “recites” an abstract idea if there are identifiable limitations that fall within at least one of the groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG). In the instant case, claim 1 recites the steps of: loading data corresponding to a location for the provision of the physical agronomic product package to a rebate, wherein the data comprises static data and dynamic data, wherein the dynamic data is selected from the group consisting of purchase history, demographic information, names, types of products desired to purchase, volume required, recommendations based on a general agronomic condition, conditions common to turf managers, and combinations thereof, wherein the conditions common to turf managers comprise control of problematic weeds and diseases common to the turfgrass manager; wherein the dynamic data changes asynchronously over time as new information is added or changed in real time, wherein the static data comprises product use guidelines, contact information, location details, segment, and combinations thereof; wherein the location details comprise size and topography; evaluating the agronomic product package and calculating any available rebate based on rebate parameters assessed using a base algorithm and loyalty parameters assessed using a loyalty algorithm via the rebate, wherein calculating the any available rebate is generated and output following the base algorithm and the loyalty algorithm, and wherein at least one of the base algorithm and the loyalty algorithm are configured to run in a circular loop; wherein the base algorithm assesses rebate parameters comprising minimum quantities, volume discounts, strategic bundles, strategic brands, multiple brands, overall purchase value, and combinations thereof; wherein the loyalty algorithm assesses loyalty parameters comprise loyalty data; wherein the loyalty data comprises static and dynamic loyal data, wherein the static loyalty comprises a static value based on a turfgrass manager's prior year loyalty and the value is fixed for a period of time, wherein the static value is reassessed at the end of the fixed period, wherein the dynamic loyalty data comprises dynamic criteria, wherein the dynamic criteria is selected from the group consisting of period-to-date purchase activity, changes in purchase activity compared to a prior period, simulated "share of wallet" calculations, portfolio/strategic brand support, and combinations thereof; wherein the dynamic data is updated in real time to access the information in real time across a supply chain; retrieving a transactional rebate produced by the base algorithm and a loyalty rebate produced by the loyalty algorithm from the rebate engine; calculating multidimensional features in view of the digital data and the transactional rebate produced by the loyalty algorithm and loyalty rebate produced by the algorithm via the rebate to generate a list comprising recommended products, recommended services, available transactional rebates, reward-based programs, loyalty-based programs, or a combination thereof, wherein the multidimensional features comprise a prior purchase by a third party located in the vicinity of the location and criterion selected from the group consisting of a user response to a prompt based on solving a problem the user has identified, pricing based on volume, a user history, and combinations thereof, generating an optimized order protocol for the user; using the personalized and optimized order protocol to generate a product or volume suggestion for the user further comprising a recommendation of a herbicide to reduce the risk of weed resistance; displaying the product or the volume suggestion based on the personalized and optimized order protocol; finalizing the physical agronomic product package, wherein the method permits a purchaser to obtain requirements for a predetermined period of time for the location; wherein the list and at least the product or the volume suggestion are constantly updated with a second list and at least a second product or a second volume suggestion in light of the adjusted data by the user via re-generating the second list and at least the second product or the second volume suggestion by re-calculating the multidimensional feature in view of the adjusted data and the transactional rebate and calculated loyalty rebate; and provisioning the finalized physical agronomic product package for the location for purchase by the user -- these claims relate to certain methods of organizing human activity, particularly commercial interactions including advertising, marketing, and sales activities/behaviors. Additionally, these steps relate to mental processes, particularly concepts performed in the human mind, including, inter alia, the observation and evaluation of information.
The limitations of the claims are not indicative of integration into a practical application. Taking the claim elements separately, the additional elements of performing the steps using digital data and parallel processing and via a web application and an engine -- merely implements the abstract idea on a computer environment. Considered in combination, the steps of Applicant’s method add nothing that is not already present when the steps are considered separately.
The claim limitations recited in the dependent claims merely narrows the abstract idea and do not recite further additional elements. Thus, claims 1, 2, and 10-12 are directed to an abstract idea.
Regarding the independent claims, the technical elements of performing the steps using digital data and parallel processing and via a web application and engine -- merely implements the abstract idea on a computer environment. The dependent claims do not recite further technical elements. When considering the elements and combinations of elements, the claim(s) as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not amount to an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment; the claims merely amounts to the application or instructions to apply the abstract idea on a computer; or the claims amounts to nothing more than requiring a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.
The analysis above applies to all statutory categories of invention. Accordingly, claims 1, 2, and 10-12 are rejected as ineligible for patenting under 35 USC 101 based upon the same rationale.
Allowable Subject Matter
Claims 1, 2, and 10-12 are rejected under 35 U.S.C. 101, but would be allowable if this rejection were overcome. The following is a statement of reasons for the indication of allowable subject matter:
Upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of Applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
Iwig (US PGP 2006/0293913) teaches [a] computer-implemented method for providing a suggested agronomic product package for purchase by a user comprising:
loading digital data corresponding to a location for the provision of the physical agronomic product package via a web application to a rebate engine, wherein the digital data comprises static data and dynamic data; (Iwig: Fig. 13; Para [0088]-[0089])
wherein the dynamic data is selected from the group consisting of purchase history, demographic information, names, types of products desired to purchase, volume required, recommendations based on a general agronomic condition, conditions common to turf managers, and combinations thereof, (Iwig: Fig. 13; Para [0088]-[0089])
wherein the conditions common to turf managers control of problematic weeds and diseases common to the turfgrass manager; (Iwig: Para [0050]-[0056], Para [0073])
wherein the dynamic data changes asynchronously over time as new information is added or changed in real time; (Iwig: Fig. 13; Para [0047]-[0048]; Para [0088]-[0089])
wherein the static data comprises product use guidelines, contact information, location details, segment, and combinations thereof; (Iwig: Para [0048]-[0049]; Para [0057]-[0061])
wherein the location details comprise size and topography; (Iwig: Para [0048]-[0049]; Para [0057]-[0061], Para [0089])
wherein the static value is reassessed at the end of the fixed period; (Ben-Eliezer: Para [0057]-[0061], Para [0089])
wherein the dynamic data is updated in real time to access the information in real time across a supply chain; (Iwig: Fig. 13; Para [0047]-[0048]; Para [0088]-[0089])
retrieving a transactional rebate produced by the base algorithm . . . ; (Iwig: Fig. 10; Fig. 13; Para [0088]-[0089])
calculating multidimensional features in view of the digital data . . . to generate a list comprising recommended products, recommended services, available transactional rebates, reward-based programs, loyalty-based programs, or a combination thereof, (Iwig: Fig. 10; Fig. 13; Para [0088]-[0089])
wherein the multidimensional features comprise a prior purchase by a third party located in the vicinity of the location and criterion selected from the group consisting of a user response to a prompt based on solving a problem the user has identified, pricing based on volume, a user history, and combinations thereof, (Iwig: Fig. 12 and 13, Para [0088]-[0089]))
generating an optimized order protocol for the user; (Iwig: Figs. 12 and 13; Para [0088]-[0089])
using the personalized and optimized order protocol to generate a product or volume suggestion for the user further comprising a recommendation of a herbicide to reduce the risk of weed resistance; (Iwig: Figs. 12 and 13; Para [0088]-[0089]; Para [0101]))
displaying the product or the volume suggestion based on the personalized and optimized order protocol via the web application; and (Iwig: Figs. 12 and 13; Para [0088]-[0089]);
finalizing the physical agronomic product package, (Iwig: Para [0014]; Para [0098]; Fig. 6; Para [0060]-[0072])
wherein the method permits a purchaser to obtain requirements for a predetermined period of time for the location; (Iwig: Para [0014]; Para [0098]; Fig. 6; Para [0060]-[0072])
. . . physical agronomic product package according to the purchase agreement, wherein the physical agronomic product package comprises physical agronomic products. (Iwig: Para [0014]; Para [0098]; Fig. 6; Para [0050]-[0056], Para [0060]-[0073])
provisioning the finalized physical agronomic product package for the location for purchase by the user. (Iwig: Para [0014]; Para [0098]; Fig. 6; Para [0060]-[0072])
Ben-Eliezer (US PGP 2016/0063511) teaches
evaluating the agronomic product package and calculating any available rebate based on rebate parameters assessed using a base algorithm and loyalty parameters assessed using a loyalty algorithm via the rebate engine, wherein calculating the any available rebate is generated and output following parallel processing of the base algorithm and the loyalty algorithm, . . . ; (Ben-Eliezer: Para [0028]; Fig. 3; Para [0053]-[0060]; Para [0029])
wherein the base algorithm assesses rebate parameters comprising minimum quantities, volume discounts, strategic bundles, strategic brands, multiple brands, overall purchase value, and combinations thereof; (Ben-Eliezer: Para [0028]; Fig. 3; Para [0053]-[0060]; Para [0029])
wherein the loyalty algorithm assesses loyalty parameters comprise loyalty data; (Ben-Eliezer: Para [0014]-[0015]; Para [0039]-[0044]; Para [0048])
wherein the loyalty data comprises static and dynamic loyal data; (Ben-Eliezer: Para [0014]-[0015]; Para [0039]-[0044]; Para [0048])
wherein the dynamic loyalty data comprises dynamic criteria; (Ben-Eliezer: Para [0014]-[0015]; Para [0039]-[0044]; Para [0048])
wherein the dynamic criteria is selected from the group consisting of period-to-date purchase activity, changes in purchase activity compared to a prior period, simulated “share of wallet” calculations, portfolio/strategic brand support, and combinations thereof; (Ben-Eliezer: Para [0014]-[0015]; Para [0039]-[0044]; Para [0048])
calculating, by the base algorithm, a transactional rebate; (Ben-Eliezer: Para [0028]; Fig. 3; Para [0053]-[0060]; Para [0029]; Para [0014]-[0015]; Para [0039]-[0044]; Para [0048])
calculating, by the loyalty algorithm from the rebate engine, a loyalty rebate; (Ben-Eliezer: Para [0028]; Fig. 3; Para [0053]-[0060]; Para [0029]; Para [0014]-[0015]; Para [0039]-[0044]; Para [0048])
retrieving . . . a loyalty rebate produced by the loyalty algorithm from the rebate engine; (Ben-Eliezer: Para [0028]; Fig. 3; Para [0053]-[0060]; Para [0029]; Para [0014]-[0015]; Para [0039]-[0044]; Para [0048])
PTO 892 Reference U (hereinafter “Reference U”) teaches fulfilling and delivering the physical . . . product package according to the purchase agreement, wherein the physical . . . product package comprises physical . . . products. (Reference U: Page 1-2)
However, Reference U does not disclose:
. . . and wherein at least one of the base algorithm and the loyalty algorithm are configured to run in a circular loop;
wherein the static loyalty comprises a static value based on a turfgrass manager's prior year loyalty and the value is fixed for a period of time;
wherein the list and at least the product or the volume suggestion are constantly updated with a second list and at least a second product or a second volume suggestion in light of the adjusted digital data by the user via re-generating the second list and at least the second product or the second volume suggestion by re-calculating the multidimensional feature in view of the adjusted digital data and the transactional rebate and loyalty rebate; and
Indeed, none of the relevant pieces of prior art discussed above Iwig, Ben-Eliezer, PTO 892 Reference U, or any of the other cited references teach, suggest, or otherwise render obvious the entirety of the above cited features.
Thus, the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. Moreover, the combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence at hand to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
Claims 2 and 10-12 are dependents from claim 1 and are thus allowable subject matter for the reasons stated above with respect to claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER V LEE whose telephone number is (571)272-4778. The examiner can normally be reached Monday - Friday 9AM - 5PM EST.
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/JENNIFER V LEE/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688