DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the RCE, Remarks, and Amendments filed 02/05/2026.
Claims 1, 8, 15 have been amended.
Claims 3, 10, 16 are canceled.
Claims 1-2, 4-9, 11-15, and 17-20 have been examined and are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/05/2026, has been entered.
(AIA ) Examiner Note
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Admitted Prior Art
With regard to independent claims 1, 8, 15, the common knowledge declared to be well-known in the art has been taken to be Applicant admitted prior art, as of Final Rejection mailed 10/28/2024, because the Applicant failed to traverse the Examiner’s assertion of Official Notice, in Non-Final Rejection mailed 3/26/2024, in the response to this rejection received 6/26/2024.
This fact is as follows: web applications were well-known to a person of ordinary skill in the art before the effective filing date of the claimed invention, e.g. since 1999 when "web application" concept was introduced in the Java programming language. Therefore, the examiner has found that it would have been obvious to try using this mechanism for presentation and display of Aued’s results/solutions to his system users on his display for all the benefits known regarding web applications, such as deployment via a web-application running on a web-browser makes it possible to deploy on a wider set of user owned hardware platforms, e.g. different computers, and thereby available for a wider audience to use the system for ostensibly lower support and maintenance costs and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Examiner further notes, to adequately traverse such a finding, an applicant must specifically point out the supposed errors in the Examiner’s action, which would include stating why the noticed fact is not considered to be common knowledge or well-known in the art. See 37 CFR 1.111(b). See also Chevenard, 139 F.2d at 713, 60 USPQ at 241 (“[I]n the absence of any demand by Applicant for the examiner to produce authority for his statement, we will not consider this contention.”). A general allegation that the claims define a patentable invention without any reference to the Examiner’s assertion of Official Notice is inadequate. Support for the Applicant’s assertion should be included. Because Applicant failed to traverse Examiner’s Official Notice, the common knowledge or well-known in the art statement is taken to be admitted prior art. See MPEP 2144.03(C).
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, 4-9, 11-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, as follows:
Per Independent claims 1, 8, 15:
“determining… for each of a plurality of sales outlets stored in a database, a probability of closing a sale (Pc) of a product, wherein Pc is determined by combining a sales probability (Ps) and a buyer probability (Pb) in a logistic regression function,…
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Wherein the product is determined from the inventory of the sales outlet, given that the sales outlet is presented to the user…;
Selecting… based at least in part on the determined probability of closing a sale (Pc) of the product to the user for each of the plurality of sales outlets, a list of sales outlets from the plurality of sales outlets; and
Generating and presenting,… to the user interested in purchasing the product,… the selected list of sales outlets… wherein the selected list of sales outlets is a subset of the plurality of sales outlets stored in the database having the highest likelihood of closing a sale.
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within the group Mathematical Concepts (e.g. mathematical relationships; mathematical formulas or equations; mathematical calculations) and Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, these steps, as drafted, are not a technical solution to technical problem but rather are a combination of business decisions to use general mathematical calculations (i.e. a logistic regression function and normalizing characteristics of individual sales outlets; i.e. the calculation of xi is nothing more than what is known as Min-Max Normalization of a feature x) to select targeting criteria for presenting targeted advertising (i.e. presenting the selected list of sales outlets, e.g. retailers or dealers, etc…, to the user interested in purchasing the product…). Note, the calculation of (Pc) is only recited at the highest-level of generality as a mathematical function of two variables (Ps) and (Pb) - each of which are themselves also left to a practitioner to determine how to calculate.
Thus, the claims fall into Mathematical Concepts and Certain Methods of Organizing Human Activity. Again, Examiner notes that there is no technical problem being solved here and there is no technical solution presented for solving a technical problem when recited at this very high-level of generality. Furthermore, the mere nominal recitation of a generic computer or computing components implementing the method does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims (i.e.: “by a computer…”, and “a user device associated with the interested consumer” And “a Web application hosted on a server computer”; and wherein descriptions of data being operated upon by the abstract idea include: “historical sales between the user and the respective sales outlet and a distance between the user and the respective sales outlet” and “…wherein PS represents a probability of a specific sales outlet selling the product to a user interested in purchasing the product, … [the calculation of a feature is for the purpose of] to increase robustness of coefficient estimates against skewed data distributions; …a web application hosted on a server computer [is used for presenting]… wherein Pb represents a probability, from a perspective of a respective sales outlet, of the user buying the product from the respective sales outlet given a historical buying preference of the user, wherein the probability of the user buying the product from the respective sales outlet is based on: demographic features of the user and historical interactions of the user with the respective sales outlet…”) none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
As drafted, the claims as a whole merely describe how to generally “apply” the identified abstract using generic computer components (e.g. by a computer and a user device, presenting information through a generic web app hosted on a server computer ofa distributed and networked system) implementing concepts of general statistical techniques (i.e. mathematical calculations of logistic regression and normalizing the calculation of features which are to be compared which is inherently for the recognized purpose of “increase[ing] robustness of coefficient estimates against skewed data distributions) on generically described customer data (i.e. “including historical sales between the user and the respective sales outlet and a distance between the user and the respective sales outlet”, etc…) which merely serve to link the abstract idea to a field of use (i.e. in this case targeting content/advertising of products offered via various sales outlets) or serve as insignificant extra-solution activity (i.e. the presenting of targeted content is via “a graphical visualization” [e.g. displaying], etc…). The claimed computer components are recited at a very high-level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components and displaying information is not a practical application of the abstract idea.
There are no additional features which are more than generally “apply” the aforementioned abstract idea and link them to a field of use or are insignificant extra-solution activity to the already identified abstract idea and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components using generic mathematical calculations, or “link” them to a field of use (i.e. targeting content/advertising), or as insignificant extra-solution activity (e.g. display of information). For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components and/or gather, transmit, and display data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claims 2 and 9 recite the following: “further comprising: receiving, via the Web application, a request from the user interested in purchasing the product, wherein the selecting and presenting are performed in response to the request from the user interested in purchasing the product.” However, receiving a request before providing information is not applicant’s invention and is squarely in the realm of the aforementioned abstract idea but not significantly more than it. The mention of receiving a request via a well-known channel of communication, i.e. a web-application, does not alter this finding as such web-apps are ubiquitous since ~1999 many years before applicant’s effective date of the claimed invention.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and are therefore patent ineligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (AIA )
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-2, 4-5, 8-9, 11-12, 15, 17-18 are rejected under 35 U.S.C. 103 as obvious over Aued et al. (US 8,255,269 B1; hereinafter, "Aued") in view of Applicant Admitted Prior Art / Official Notice and Pinel et al. (US 2015/0199743 A1; hereinafter, "Pinel").
Claims 1, 8, 15: (Currently Amended)
Pertaining to claims 1, 8, 15 as exemplified in the limitations of method claim 1, Aued as shown teaches the following:
A method for presenting to an interface via a distributed and networked system a visualization of automatically selected sales outlets based on modeling a bilateral decision process using demographic features and historical data associated with a user, the method, comprising:
determining, by a computer of the distributed and networked system for each of a plurality of sales outlets stored in a database, a probability of closing a sale (Pc) of a product, wherein Pc is determined by combining a sales probability (PS) and a buyer probability (Pb) […] (Aued, see at least [4:4 - 7:24], especially [4:39-5:42], teaching e.g.: “Aggregated Success Criteria Function (“ASCF”)” where Applicant’s “Pc” reads on Aued’s “ASCF” which is taught as being a function of [i.e. combining] at least PrSiPj(t) [applicant’s Ps] and PrBiPj(t) [applicant’s Pb]), wherein PS represents a probability of a specific sales outlet selling the product to a user interested in purchasing the product,…, given that the sales outlet is presented to the user […] hosted on a server computer of the distributed and networked system and wherein Pb represents a probability from a perspective of a respective sales outlet, of the user buying the product from the respective sales outlet given a historical buying preference of the user, wherein the probability of the user buying the product from the respective sales outlet is based on demographic features of the user and historical interactions of the user with the respective sales outlet (Aued, again per at least [4:4 - 7:24], teaching e.g.: “Aggregated Success Criteria Function (“ASCF”)” [Pc] where Applicant’s “Pc” reads on Aued’s “ASCF” which is taught as being a function of at least PrSiPj(t) [applicant’s Ps] and PrBiPj(t) [applicant’s Pb];
For example, per [4:39-5:42], Aued teaches:
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; i.e. Applicant’s “Pb” reads on PrBiPj(t) where per [4:38-42]: “…The function PrBiPj(t) determines the probability of Buyer Bi buying product Pj within time t…”; where the probability can have a range of values equal to or between 0 and 1; where as also disclosed: “V2: This variable represents the purchasing or sales history of each member” [historical interactions of the user with the respective sales outlet]; “V3: This variable represents a member's profile information…” [demographic features of the user]; “V1: This variable represents the purchase or selling intentions of a buyer or seller. Members will enter their likelihood of buying or selling a particular product or service” [the specific sales outlet]; note per [5:20-41] Aued teaches: “…These concepts [referring back to PrBiPj(t), etc…] are also expressed through the following variables: …Future Purchase Value of Buyer (“FPVB”): this is a statistic variable representing the future purchase value of a buyer, which is the sum of all non-committed purchases of a specific seller’s product list. … The FPV is a function of the ‘correlated sales forecast for a customer for a seller’ [the probability of the user buying the product from the respective sales outlet], its profile, probability to buy, etc…”; and
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; i.e. Applicant’s “Ps” reads on PrSiPj(t), disclosed per at least [4:48] and [5:15-20] as follows: “This equation determines the probability of each seller carrying out his or her present intention of a future sale, as well as predicting the future individualized, aggregated supply curve for a good or service within a certain time period…”; Further, as noted supra, herein Aued teaches: “In the preferred embodiment, the best solution maximizes the Aggregated Success Criteria Function ("ASCF") and the Aggregated Score Rate of Buyers Relative to its Selected Sellers ("ASRB/S"); i.e. the aggregate is the combination of the aforementioned probability functions. Applicant’s “Pc” therefore reads on Aued’s “ASCF” which is an aggregate [combined] function of the previously noted probability functions PrSiPj(t) and PrBiPj(t); Also note per at least [23:1-16]: “System User’s Interface Program: …Buyers can access the system's web page and buyer's web page. If a buyer is already registered, the buyer will be allowed to login; otherwise, they will be asked to register…” [i.e. sales outlet is presented to the user hosted on a system’s web page via a server computer])
[…],
wherein Ps is determined based at least in part on: a first set of features describing the individual sales outlet (Aued, again as noted supra, Applicant’s “Ps” reads on Aued’s “PrSiPj(t)”, disclosed per at least [4:48-68] and [5:15-20]; where “PrSiPj(t)” [i.e. Ps] is determined based upon features of individual sellers, such as variable “V3”, e.g.: “…V3: This variable represents a member's profile [first set of features] information. The profile of each buyer or seller helps predict his or her future transaction intentions…”), and a second set of features describing the individual sales outlet compared to other sales outlets in a cohort responding to a distinct user query wherein at least one feature of the second set is a rescaled variable representing a normalized value ranging from 0 to 1 of a characteristic of the individual sales outlet relative to a maximum and a minimum value of the characteristic identified from all sales outlets within the cohort, calculated as [xi = normalized value of xi or, its compliment: 1- normalized value of xi]1to increase robustness of coefficient estimates against skewed data distributions (Aued, again as noted supra, Applicant’s “Ps” reads on Aued’s “PrSiPj(t)”, disclosed per at least [4:48-68] and [5:15-20]; where “PrSiPj(t)” [i.e. Ps] is additionally determined based upon features [e.g. second set of features] of sellers compared to each other, such as variable “V4”, e.g.: “…V4: This variable represents the… seller score rate [feature describing the individual sales outlet compared to other sales outlets in a cohort responding to a distinct user query]. The system will determine the correlation between the score rate and predictability…”; Aued’s seller score is based on confirmed buyer intention to buy [query] a product from seller [responding to a distinct user query]; e.g. see at least variable “V5” [4:40-5:20]; regarding “V4”, etc… being normalized as claimed, Examiner takes Official Notice of the following facts: Min-Max Normalization [applicant’s function as claimed for calculating xi] is a well-known and perhaps the simplest technique for normalizing feature data which is recognized as useful for accurate interpretation of model coefficient importance, and stable, unbiased, or regularly penalized model training; e.g. for the purpose of increasing robustness of coefficient estimates against skewed data distributions. Therefore, the Examiner understands that the claimed technique is a known technique applicable to model development such as Aued’s model development and there exists motivation to a person of ordinary skill in the art before the effective filing date of the claimed invention to implement this known Min-Max Normalization technique on Aued’s features which he uses to model his various probability functions. Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply this known technique to Aued’s known system/method because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious and/or because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.),
wherein the product is determined from the inventory of the sales outlet (Aued, e.g. per at least [17:12-15] the solutions, i.e. products which are available, must consider seller constraints and limitations including: “…FIG. 4B highlights all the possible solutions that are not feasible considering the inventory constraints [inventory of the sales outlets]…”)
wherein the historical interactions of the user with the respective sales outlet include historical sales between the user and the respective sales outlet (Aued, see at least [4:49-65], teaching his PrBiPj(t) [applicant’s Pb] is a function of various variables including demographics and purchase history e.g.: “…V2: This variable represents the purchasing or sales history [historical sales between the user and the respective sales outlet] of each member… V3: this variable represents a member’s profile information [demographics]…”);
selecting, by the computer based at least in part on the probability of closing a sale (Pc) of the product to the user for each of the plurality of sales outlets, a list of sales outlets from the plurality of sales outlets (Aued, again see at least Figs. 4A-4B in view of [5:50-7:24] and [21:61-23:37], e.g. regarding determination of Aued’s “purchase group”, which includes “selected sellers” which is a best solution [i.e. a selected list of sales outlets], where “…In the preferred embodiment, the best solution [i.e. the selected list of sales outlets] maximizes the Aggregated Success Criteria Function ("ASCF") [i.e. based on the probability of closing a sale (Pc)] and the Aggregated Score Rate of Buyers Relative to its Selected Sellers ("ASRB/S") [i.e. the plurality of sales outlets]…”); and
generating and presenting via a graphical visualization, by the computer to the user interested in purchasing the product […] hosted on the server computer of the distributed and networked system, the selected list of sales outlets on a user device associated with the user interested in purchasing the product (Aued, again see at least Fig. 4C [17:14-22], e.g.: “…After factoring in the limitations of the sellers, there are now 11 out of the original 24 feasible solutions [i.e. a generated selected list of sales outlets which is a subset of the 24 original possible sellers]… FIG. 4C highlights the solutions in the colurm1 entitled "Total Purchase Price Buyer1, Buyer2, Buyer 4." This column represents the minimum purchase price for Product 1. The minimum purchase price for Buyer 1 occurs in solutions 4 and 7, which is $19.40., etc…” and per [23:1-16], teaching a user interface with a display of a selected list of potential sellers, belonging to “purchase groups” from whom a buyer may purchase products, e.g.: “…The user interface will allow buyers to enter their purchase intention information, list all existing common target purchase groups ("CTPG") [list of sales outlets], and subscribe to one or more CTPGs…Once the buyer selects a CTPG, the buyer can set his or her status in each of those groups (e.g., committed or NOT committed). In a committed status, the buyer's purchase order will be processed once the objective of the CTPG is met or exceeded…”);
wherein the selected list of sales outlets is a subset of the plurality of sales outlets stored in the database having the highest likelihood of closing a sale (Aued, again see citations noted supra, e.g. at least Fig. 4C [17:14-22], e.g.: “…After factoring in the limitations of the sellers, there are now 11 out of the original 24 feasible solutions [i.e. a selected list of sales outlets is a subset of the plurality]…”; again, note that per at least [4:35-5:64], e.g.: “…In the preferred embodiment, the system determines the probability of a buyer completing a purchase of a product or service by a future date-Buyer Intended Probability ("BIPr"). The function PrBiPj (t) determines the probability of Buyer Bi buying product Pj within time t, which can have the following range of values:… In a preferred embodiment, the Resolution process of the system only selects committed [highest likelihood of closing a sale] buyers and sellers.”).
The difference between the aforementioned limitations and the teachings of Aued is that Aued may not explicitly teach that he uses a “web application” to effect his display on a computer, via his “user interface”, of products offered by his sellers or to present his results/solutions to users even though he does teach, e.g. per [21:55-59] that “The system may also recommend… buyers purchase the product or service from a traditional web store under the store's terms and conditions.”. However, per Applicant Admitted Prior Art such “web applications” were old and well-known to a person of ordinary skill in the art before the effective filing date of the claimed invention, e.g. since 1999 when "web application" concept was introduced in the Java programming language. Therefore, the examiner finds that it would have been obvious to try using this mechanism for presentation and display of Aued’s offered products and results/solutions to his system users on his display for all the benefits known regarding web applications, such as deployment via a web-application running on a web-browser makes it possible to deploy on a wider set of user owned hardware platforms, e.g. different computers, and thereby available for a wider audience to use the system for ostensibly lower support and maintenance costs and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.)
Furthermore, although Aued teaches the above limitations, including Applicant’s feature of determining a probability of closing a sale (Pc) of a product, wherein Pc is determined by combining a sales probability PS and a buyer probability Pb, , e.g. as noted supra per at least [4:4 - 7:24], where applicant’s (Pc) reads on Aued’s “Aggregated Success Criteria Function (“ASCF”)” and Aued teaches, e.g. per [18:28-57] there exist different methodologies for solving for ASCF [i.e. solving for Pc], where: “…The uses of those methodologies depend on the nature of the ASCF and its constraints. If the ASCF is linear and subject to linear constraints, the system can use linear programming methodology ("LPM") to find the maximum and optimum solutions…” which implies use of techniques which rely on linear models to predict probability, such as logistic regression, to solve his ASCF which is a function [a combination] of the probability functions PrSiPj(t) [Ps] and PrBiPj(t) [Pb], Aued nonetheless may not explicitly state he uses a logistic regression function to solve for his ASCF. Furthermore, although Aued teaches his PrBiPj(t) [applicant’s Pb] is a function of various variables including demographics and purchase history e.g.: “V2: This variable represents the purchasing or sales history [historical sales between the user and the respective sales outlet] of each member… V3: this variable represents a member’s profile information [demographics], etc…”, he may not explicitly teach his member’s profile information [demographic features] include a distance between the user and the respective sales outlet. However, regarding these nuances, Aued in view of Pinel teaches the following:
in a logistic regression function to model a binomial sale versus no-sale outcome (Pinel, see at least [0023]-[0025] teaching “logistic multi-variable regression analysis” may be used to determine a “probability of accepting an offer” [Pc]; applicant’s statement of intended use “to model a binomial sale versus no-sale outcome” is met by virtue of performing the step “in a logistic regression function”; e.g. the intended use statement does not alter the method steps nor the system or computer program product as currently recited)
wherein the demographic features of the user include a distance between the user and the respective sales outlet (Pinel, see at least [0023]-[0040], e.g.: “[0034] a9 [variable]: Geographical distance between buyer [user] and seller [respective sales outlet];” note per [0023]-[0025] “logistic multi-variable regression analysis” may be used to determine a “probability of accepting an offer”, where the parameters a1 , a2 , a3 , a3 , a4 , a5 , and so forth [including a9], are real numbers representing relevant parameters (e.g., attributes of the product (such as a vehicle), seller, buyer and the combination of buyer and seller)…”);
Therefore, the Examiner understands that the limitations in question are merely applying known techniques of Pinel (directed towards predicting offer acceptance [Pc] using logistic regression on the aforementioned demographic variable between buyer/seller including a distance between buyer and seller [a distance between the user and the respective sales outlet], for example in the context of vehicles dealers and vehicle buyers, where the product is a vehicle) which are applicable to a known base device/method of Aued (who is already directed towards determining an “Aggregated Success Criteria Function (“ASCF”)” as a function of probability of a buyer purchasing a product within a particular time period and a probability of a seller offering a product for sale within a particular time frame which each are predicated upon variables such as V2: purchasing or sales history [historical sales between the user and the respective sales outlet] of each member… and V3: representing a member’s profile information [demographics]) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Pinel to the device/method of Aued such that Aued’s probability of purchase is also based on these particular demographics, such as Geographical distance between buyer [user] and seller [respective sales outlet], because Aued and Pinel are analogous art in the same field of endeavor (at least G06Q30/02) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 2, 9: (previously presented)
Aued/AAP/ON/Pinel teaches the limitations upon which these claims depend. Furthermore, Aued teaches the following:
… further comprising: receiving, via the Web application, a request from the user interested in purchasing the product, wherein the selecting and presenting are performed in response to the request from the user interested in purchasing the product (Aued, see at least [7:37-42] and [13:40-62], e.g.: Buyer enters request to purchase product P1 with purchasing criteria, and results/solutions are presented in response to evaluating this request: “…Buyer1 Success Criteria Function ("BlSCF"). An exemplary OC Bl/Pl or BlSCF/Bl could be: Objective: Minimize Purchase Price ("PP") of Product P1. Constraints: QT=2 units, DT<=10 days, PT=Credit Card, PB=1 (committed)…”; and “The buyers preferably enter their BSCF in their buyers database for single or multiple products…”; i.e. Buyer1 commits to purchasing 2 units of product P1 within 10days and payment terms is via credit card. And again, similar to the findings noted supra, because such web applications were well-known to a person of ordinary skill in the art before the effective filing date of the claimed invention, e.g. since 1999 when "web application" concept was introduced in the Java programming language, the examiner finds that it would have been obvious to try using this mechanism for receiving his user’s inputs to his system for all the benefits known regarding web applications, such as deployment via a web-application running on a web-browser makes it possible to deploy on a wider set of user owned hardware platforms, e.g. different computers, and thereby available for a wider audience to use the system at ostensibly lower support and maintenance costs and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious).
Claims 4, 11, 17: (previously presented)
Aued/AAP/ON/Pinel teaches the limitations upon which these claims depend. Furthermore, Aued teaches the following:
… wherein the first set of features or the second set of features include at least a historical close rate of an individual sales outlet (Aued, [4:36-6:64] see at least “V5: This variable represents the accuracy level of purchase predictions and real purchases performed…”), a product price by the individual sales outlet (Aued, see at least [9:50-55] e.g.: “…seller's product price sensitivity analysis revealing a reduction in product prices as purchase volume increases…”), or a drive time between an individual customer and the individual sales outlet.
Claims 5, 12, 18: (original)
Aued/AAP/ON/Pinel teaches the limitations upon which these claims depend. Furthermore, Aued teaches the following:
…wherein the historical close rate is determined based on a measure of historical performance of the individual sales outlet in a period of time, and wherein the measure of historical performance is defined as a number of sales by the individual sales outlet in a locality over the period of time (Aued, see at least [7:15-9:15] e.g.: "ASCF" [Pc] is predicated upon “V3” for sellers: This variable represents the seller's previous sales history. For example, frequency of participation, frequency rate of sales, and total volume of sales by product category. Furthermore, “…The Aggregation process derives buyer and seller grouping rules that maximizes system objectives.…Members can be grouped, for instance, by product or category of products, demographics (e.g., location [locality], sex, age, etc.), objectives (e.g., minimize purchase price, minimize delivery time, or maximize sales volume), sensitivities (e.g., all sellers whose unit prices of a product decreases by increment of purchase volume), SR ranges, etc…or all buyers committed to buying product X and living in region [locality] Y…”).
Although Aued teaches the above limitations, and he teaches location or region [locality] by which a seller and or buyer may be grouped, he may not explicitly teach such grouping is associated with a user’s [e.g. a buyer’s] search parameter. However, because Aued teaches buyers and sellers may enter constraints, and a grouping is type of constraint, the Examiner finds that there is motivation to try allowing Aued’s users to enter constraints such as region or location (i.e. the locality associated with a customer search parameter of the Web application) because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.
Claims 6, 7, 13, 14, 19, 20 are rejected under 35 U.S.C. 103 as obvious over Aued in view of Applicant Admitted Prior Art/Official Notice, Pinel and further in view of Stepaniants et al. (US 10,152,458 B1; hereinafter, "Stepaniants").
Claims 6, 13, 19: (original)
Although Aued/AAP/ON/Pinel teaches the limitations upon which this claim depends, including calculation of probabilities of buyers buying a particular good/service and probabilities of sellers selling the same, etc… which may be based on historical data, he may not delve into the minutia of statistically testing such hypothesis including use of data over various recent durations. However, regarding such features, Aued in view of Stepaniants teaches the following:
… wherein the period of time is 45 days (Stepaniants, see at least [23:14-16]: “For example, user data 208 indicative of forty-five days of previous interactions of a client device 102 with an experiment state may be used”).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Stepaniants which is applicable to a known base device/method of Aued to yield predictable results. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Stepaniants to the device/method of Aued such that the historical data considered by Aued for a seller’s history was 45 days because Stepaniants is reasonably pertinent to the statistical techniques of Aued and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 7, 14, 20: (original)
Aued/AAP/ON/Pinel/Stepaniants teach the limitations upon which these claims depend. Furthermore, as shown, Aued teaches the following: … wherein the historical close rate is determined based on counts of sales and leads within [either] 60 miles drive distance of the individual sales outlet, a designated market area average, or a geographic boundary average (Aued, see at least [4:36-6:64] and [9:5-28], e.g. “V3”: This variable represents the seller's previous sales history. For example, frequency of participation, frequency rate of sales, and total volume of sales by product category; and sellers may be grouped by location or region [a geographical boundary average]).
Response to Arguments
Applicant amended claims 1, 8, 15 and canceled 3, 10, 16 on 02/05/2026. Applicant's arguments (hereinafter “Remarks”) also filed 02/05/2026, have been fully considered but are moot in view of the new grounds of rejection necessitated by applicant’s amendments. Note the new 35 USC 101 and 103 prior art rejection with updated citations not Aued in view of Applicant Admitted Prior Art /Official Notice, and Pinel.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621
1 Applicant’s mathematical expression is not his invention but instead is nothing more than the a well-known mathematical expression of what is known as Min-Max Normalization used for feature normalization, e.g. feature xi. e.g. see https://en.wikipedia.org/wiki/Feature_scaling
Prosecution Insights