DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/26/2025 has been entered.
Status of Claims
This action is a NonFinal action on the merits in response to the communications filed on 11/26/2025.
Applicant has amended claims 1, 9, and 16.
Claims 1 – 7, and 9 – 20, are pending in this application.
Response to Remarks
Examiner’s Response to Remarks for Claim Rejections:
I. 35 U.S.C. § 101 Rejections;
II. 35 U.S.C. § 103 Rejections.
Examiner’s Response to I. 35 U.S.C. § 101 Rejections.
Applicant argues the independent claims are not directed to an abstract idea but rather towards technological improvements in software-based techniques for optimizing price metrics associated with a product.
Examiner respectfully disagrees. Applicant’s claims are directed towards an abstract idea, and the claims are not a technological improvement in software-based techniques for optimizing price metrics associated with a product. For instance, the limitations of claim 1 recite retrieving, by a processor and from a data store, historical sales transaction data associated with one or more products; dividing, by the processor, the historical sales transaction data into a plurality of unimodal segments, each segment including transactions of mutually similar products within the one or more products in the historical sales transaction data; generating, by the processor and from each segment of the historical sales transaction data, a normalization of a price metric to be optimized; generating, by the processor, a histogram distribution of the normalized price metric from each segment of the historical sales transaction data; automatically selecting, by the processor, a distribution model to fit the histogram distribution; automatically selecting, by the processor, a right hand side model to fit the right hand side (RHS) of the histogram distribution; fitting. by the processor, the selected RHS model to the histogram distribution model; automatically determining, by the processor, an optimization of the price metric from the fitted model; and reporting, by the processor, the optimized price metric to a user via a user interface. However, this recites the abstract idea of mathematical concepts that are mathematical calculations that is merely data gathering, and performing statistical analysis to generate a resampled distribution. Accordingly, claim 1 recites an abstract idea. Claims 9 and 16 are substantially similar to claim 1, and recite the same abstract idea.
The claims are not integrated into a practical application. The additional elements recited are automatically optimizing price of a product based on distribution elasticity, a program being executable by a processor, a data store, a non-transitory computer readable storage medium, a system, server, processor, and a memory; however, these additional elements recited are generic computer components that perform generic computer functions. The additional elements merely perform data analytics, and these additional elements recited are not significantly more than the judicial exception. There is no technological improvement nor inventive concept as we have here where Applicant’s claim 1 can be performed by merely using Excel Spreadsheets, where historical data can be collected, statistical analysis can be performed on the data, and displayed using a histogram. The claims as a whole are not significantly more than the judicial exception; and Applicant is merely resolving a business problem with mathematical calculations and generic computer components. Applicant’s dependent claims further limit the abstract idea identified above, and the additional limitations of the dependent claims when considered individually do not amount to significantly more than the abstract idea. Accordingly, claims 1 – 7, and 9 – 20 are rejected under 35 U.S.C. § 101.
Examiner’s Response to I. 35 U.S.C. § 103 Rejections.
Applicant argues that Pangerl, Samad-Khan, and Giacobbe, either individually or in combination, fail to disclose the features of the independent claims.
Examiner respectfully disagrees. Applicant has amended claims 1, 9, and 16; and the amendments necessitated a new search where new art is applied. Accordingly, all pending claims are rejected under 35 U.S.C. § 103.
Claim Rejections – 35 U.S.C. §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 – 7 and 9 – 20 are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 9, and 16 recites:
retrieving, historical sales transaction data associated with one or more products;
dividing, the historical sales transaction data into a plurality of unimodal segments, each segment including transactions of mutually similar products within the one or more products in the historical sales transaction data;
generating, and from each segment of the historical sales transaction data, a normalization of a price metric to be optimized;
generating, a histogram distribution of the normalized price metric from each segment of the historical sales transaction data;
automatically selecting, a distribution model to fit the histogram distribution;
automatically selecting, a right hand side model to fit the right hand side (RHS) of the histogram distribution;
fitting, the selected RHS model to the histogram distribution model;
automatically determining, an optimization of the price metric from the fitted model;
and reporting, by the processor, the optimized price metric to a user via a user interface.
The limitations of claim 1, under its broadest reasonable interpretation, recites mental processes related to observation and evaluation of data, but for the recitation of generic computer components, and uses a computer as a tool to perform a mental process. For example, claim 1 recites observing historical sales transaction data associated with one or more products; the historical sales transaction data into a plurality of unimodal segments, each segment including transactions of mutually similar products within the one or more products in the historical sales transaction data; evaluating from each segment of the historical sales transaction data, a normalization of a price metric to be optimized; evaluating a histogram distribution of the normalized price metric from each segment of the historical sales transaction data; observing a distribution model to fit the histogram distribution; observing a right hand side model to fit the right hand side (RHS) of the histogram distribution; evaluating the selected RHS model to the histogram distribution model; and evaluating an optimization of the price metric from the fitted model; and all involve observation and evaluation of data where the claim collects data, analyzes the data, and presents the data analysis results. Accordingly, claim 1 recites an abstract idea of mental processes.
Claim 1 recites mathematical concepts. For example claim 1 recites generating and from each segment of the historical sales transaction data, a normalization of a price metric to be optimized; generating a histogram distribution of the normalized price metric from each segment of the historical sales transaction data; fitting, the selected RHS model to the histogram distribution model; and automatically determining an optimization of the price metric from the fitted model; and all involve mathematical calculations where the claim merely performs statistical analysis to generate a distribution. Accordingly, claim 1 recites mathematical concepts.
The limitations of claims 9 and 16, substantially recite the same subject matter of claim 1 and also include the abstract ideas identified above.
The dependent claims encompass the same abstract ideas as well. For instance, claim 2 is directed towards observing distribution model is a lognormal distribution model, claims 3, 11, and 18 are directed towards observing selecting a portion of the distribution data greater than the mean, wherein the right hand side of the histogram model includes the portion of the histogram distribution that is greater than the mean and evaluating the selected model to the portion of data greater than the mean, claims 4, 12, and 19 are directed towards evaluating the selected RHS model and the histogram model includes fitting the models to a two dimensional point, claims 5, 13, and 20 are directed towards observing the two dimensional point is defined by the sum of a mean and a first standard deviation in the distribution model, claims 6 and 14 are directed towards evaluating using a Taylor series expansion, claim 7 and 15 are directed towards evaluating an optimized price metric from the fitted model includes evaluating a distribution model for the price metric from the fitted RHS model; and claims 10 and 17 are directed towards observing the distribution model is a normal, beta, lognormal or another distribution model. Thus the dependent claims further limit the abstract concepts found in the independent claims.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of automatically optimizing price of a product based on distribution elasticity, by a processor, and a data store; in addition to reciting the additional elements of claim 1, claim 9 recites the additional elements of a non-transitory computer readable storage medium, and a program being executable by a processor; and in addition to reciting the additional elements of claim 1, claim 16 recites the additional elements of a system, a server including a memory and a processor, and one or more modules stored in the memory and executed by the processor. The additional elements of automatically optimizing price of a product based on distribution elasticity, a program being executable by a processor, a data store, a non-transitory computer readable storage medium, a system, server, processor, and a memory are generic computer components as per Applicant’s Specifications shown below:
“[0061] The components contained in the computer system900 of FIGURE 9 are those typically found in computer systems that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system900 of FIGURE 9 can be a personal computer, handheld computing device, smart phone, mobile computing device, workstation, server, minicomputer, mainframe computer, or any other computing device. The computer can also include different bus configurations, networked platforms, multi-processor platforms, etc. Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Android, as well as languages including Java, .NET, C, C++, Node.JS, and other suitable languages.”
and thus are not practically integrated nor significantly more.
The claims do not recite additional elements that integrate the judicial exception into a practical application. The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea and are considered generic computer components performing generic computer functions and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 7, 10 – 15, and 17 – 20, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 7, and 9 – 20, are not patent eligible.
Claim Rejections: 35 U.S.C. § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claims 1 – 7, 9 – 20, are rejected under 35 U.S.C. 103 as being unpatentable Montero, Michael et al. (U.S. Publication No. 2023/0133390) hereinafter “Montero” in view of Fano, Andrew E et al. (U.S. Publication No. 2014/0114743) hereinafter “Fano” in view of Giacobbe, Robert A et al. (U.S. Publication No. 2014/017,2493) hereinafter “Giacobbe.
Claims 1, 9, and 16:
A method for automatically optimizing price of a product based on distribution elasticity, comprising: retrieving, by a processor and from a data store, historical sales transaction data associated with one or more products; Montero teaches in ¶ 0206, this example process 1900 is shown with the initial aggregation of transaction data by day and store (at 1910) as discussed previously. This may include aggregation of many years of historical pricing and transaction data, when available, and the collection of all future transactions that provide results of the price testing. The data may be validated (at 1920) for accuracy against the assigned price testing since, as discussed, retailers often are not good at deploying the prices as directed. The t-log data is then adjusted (at 1930). This adjustment process is shown in greater detail at Fig. 19B, where corrupt data that has been identified by the price auditors is filtered out (at 1931). The prices may be adjusted by day (at 1933), by store (at 1935) and by any external factors as described previously in considerable detail. The transactions may be normalized (at 1937) and the promotions adjusted by regression method and relative pair-wise method. Montero teaches in 0207, after data has been adjusted the test prices are incrementally calculated (at 1940) by solving for an objective function and using what known elasticity between products that is known, subject to constraints. These test prices are experimented (at 1950), and the results are collected. This allows for better elasticity models to be generated (at 1960). Again the optimization is solved for (at 1970) and this refined set of test prices may be tested (at 1970). This allows for a repetitive set of transaction data to be collected, verified, adjusted and used to update the elasticity models. Each testing iteration allows for prices to be tested that are closer to the optimal price point for each product. Once the optimal price has been identified, it may be deployed to the majority of retailers with minimal ongoing validation occurring. Montero teaches in ¶ 0208, now that the systems and methods for base price optimization through pricing testing have been disclosed in considerable detail, attention will be directed to a series of examples to facilitate the discussion of test design and rollout to a series of retailers within a retail chain. For these examples the focus will center on a retailer chain with 66 stores attempting to determine the base pricing of a class of goods, here butter and margarine spreads. The number of stores and good type are entirely illustrative, and the present systems and methods could be applied to any type of retailer with virtually any number of physical locations. However, it should be noted that for efficiency of testing and minimization of external variable impacts, a minimum number of test stores may be desirable. For example, in fewer than 10 test stores, the number of price changes and redundant testing may need to be increased to get accurate results for the optimal prices. This may increase the per store cost of testing, and as such may be less appealing for a retail chain. Montero teaches in ¶ 0227, Processor(s) 2722 (also referred to as central processing units, or CPUs) are coupled to storage devices, including Memory 2724. Memory 2724 includes random access memory (RAM) and read-only memory (ROM). As is well known in the art, ROM acts to transfer data and instructions uni-directionally to the CPU and RAM is used typically to transfer data and instructions in a bi-directional manner. Both of these types of memories may include any suitable of the computer-readable media described below. A Fixed medium 2726 may also be coupled bi-directionally to the Processor 2722; it provides additional data storage capacity and may also include any of the computer-readable media described below. Fixed medium 2726 may be used to store programs, data, and the like and is typically a secondary storage medium (such as a hard disk) that is slower than primary storage. It will be appreciated that the information retained within Fixed medium 2726 may, in appropriate cases, be incorporated in standard fashion as virtual memory in Memory 2724. Removable Disk 2714 may take the form of any of the computer-readable media described below.
dividing, by the processor, the historical sales transaction data into a plurality of unimodal segments, each segment including transactions of mutually similar products within the one or more products in the historical sales transaction data; Montero teaches in ¶ 0019, a typical promotion optimization method may involve examining the sales volume of a particular CPG item over time (e.g., weeks). The sales volume may be represented by a demand curve as a function of time, for example. A demand curve lift (excess over baseline) or dip (below baseline) for a particular time period would be examined to understand why the sales volume for that CPG item increases or decreases during such time period. Montero teaches in ¶ 0022, current promotion and base price optimization approaches tend to evaluate sales lifts or dips as a function of four main factors: discount depth (e.g., how much was the discount on the CPG item), discount duration (e.g., how long did the promotion campaign last), timing (e.g., whether there was any special holidays or event or weather involved), and promotion type when analyzing for promotions (e.g., whether the promotion was a price discount only, whether Brand X cookies were displayed/not displayed prominently, whether Brand X cookies were features/not featured in the promotion literature). Montero teaches in ¶ 0024, for example, that there was a discount promotion for Brand X cookies during the time when lift 110 in the demand curve 102 happens. However, during the same time, there was a breakdown in the distribution chain of Brand Y cookies, a competitor's cookies brand which many consumers view to be an equivalent substitute for Brand X cookies. With Brand Y cookies being in short supply in the store, many consumers bought Brand X instead for convenience sake. Aggregate historical sales volume data for Brand X cookies, when examined after the fact in isolation by Brand X marketing department thousands of miles away, would not uncover that fact. As a result, Brand X marketers may make the mistaken assumption that the costly promotion effort of Brand X cookies was solely responsible for the sales lift and should be continued, despite the fact that it was an unrelated event that contributed to most of the lift in the sales volume of Brand X cookies. Montero teaches in ¶ 0111, test promotions 102a-102d are shown testing three test promotion variables X, Y, and Z, which may represent for example the size of the packaging (e.g., 12 oz. versus 16 oz.), the manner of display (e.g., at the end of the aisle versus on the shelf), and the discount (e.g., 10% off versus 2-for-1). These promotion variables are of course only illustrative and almost any variable involved in producing, packaging, displaying, promoting, discounting, etc. of the packaged product may be deemed a test promotion variable if there is an interest in determining how the consumer would respond to variations of one or more of the test promotion variables. Further, although only a few test promotion variables are shown in the example of Fig. 2A, a test promotion may involve as many or as few of the test promotion variables as desired. For example, test promotion 102e is shown testing four test promotion variables (X, Y, Z, and T). Montero teaches in ¶ 0112, One or more of the test promotion variables may vary from test promotion to test promotion. In the example of Fig. 2A, test promotion 102a involves test variable X1 (representing a given value or attribute for test variable X) while test promotion 102b involves test variable X2 (representing a different value or attribute for test variable X). A test promotion may vary, relative to another test promotion, one test promotion variable (as can be seen in the comparison between test promotions 102a and 102b) or many of the test promotion variables (as can be seen in the comparison between test promotions 102a and 102d). Also, there are no requirements that all test promotions must have the same number of test promotion variables (as can be seen in the comparison between test promotions 102a and 102e) although for the purpose of validating the effect of a single variable, it may be useful to keep the number and values of other variables (e.g., the control variables) relatively constant from test to test (as can be seen in the comparison between test promotions 102a and 102b). Montero teaches in ¶ 0164, The price changes, preferably, are updated over night when the store is closed. For 24 hour retailers, this may be set to a low volume period, and all prices in the store may be updated at the same time. In some cases, a grace period of an hour (or other acceptable timeframe) may be provided by the 24 hour retailer after a price update. Consumers who complete their purchase within this grace period will be afforded the lower of any price that was displayed for the item. For example is ice cream was offered at $3.99 and frozen pizza at $9.99 at 11:59 pm, and the price changed to $4.99 and $9.50 for the ice cream and pizza, respectively, at 12:01 am, if the consumer purchases the items before 1:00 am the prices charged would be $3.99 and $9.50 respectively. Montero teaches in ¶ 0167, the pricing may again be adjusted by a smaller degree (at 1370) and retested in the store from the last ‘best’ price. For example, assume the price of apples is currently $1.49 each, and the price is adjusted to $1.35. There is a margin drop, but it is still within a range that is deemed acceptable by the retailer. Volumes during the testing period don't change much, however, so overall profit actually reduces. The base price thus remains at $1.49, but is now retested at $1.65 each. Again, this is an acceptable margin, and cases a minor reduction in volume. However the profit is higher by a statistically relevant amount (over 95% confidence), so the updated base price is now $1.65. The price is then adjusted to $1.69 by the system and analysis repeated. The profit now drops due to price elasticity causing a reduced volume. The base remains at $1.65 and is then tested at $1.59. In this example, sales recover sufficiently to make this preferred (statistically significant profit increase and still within margin range) over the previous price. After a number of such iterations, it may be found that the ideal base price is $1.62. Any more or less of a price change results in a lower profitability in this example. This base price may then be disseminated to a wider set of stores within the retailer's chain, particularly to stores serving similar consumer types. Overall sales of this item may be monitored, and should indicate an increase in overall profitability for the base priced item. If no increase is detected, additional testing (possibly in a different set of test stores) may be warranted. The preceding examples illustrates the testing process per product but keep in mind the system is optimizing categories or groups of products with a similar sales-margin objective simultaneously. The optimal price point for every product within a category is set by maximizing the overall objective function of that category which will include product self elasticities and cross-product elasticities influencing the demand of one product in that category versus another. For example, as the system tests prices for shredded cheese, maybe moving price up on Sargento shredded cheese, the substitutability of this category may see shoppers buy more of Kraft shredded cheese. As a result the cross-elastic effect is taken into account and both Sargento and Kraft's prices will be tested and an optimum will be determined for both brands and that optimum will be tested as well to validate the projection. All price changes will be guided by the objective function which in this case would be to grow volume in the shredded cheese category while maintaining a certain level of margin. Montero teaches in ¶ 0209, an illustration of an example rollout of a base price optimization test, shown generally at 2000. In this example, the 66 stores are divided evenly into a three groups. Each group is assigned either a current (historical) price for each stock keeping unit (SKU) of butter (shown in light grey), a lower test price (shown in a medium grey), and a higher test price (shown in the darkest grey). In this example, the lower test price has been incremented ten cents lower than the current price, and the higher price is incremented ten cents above the current price. Which store group receives the lower, current or higher price may be randomized, as may which of the stores are placed into each group of stores. In this example, the prices are then rotated on a weekly basis between the groups of stores. Transaction data from each store is collected from this rollout enabling an elasticity matrix 2100 to be generated, as seen in Fig. 21. In this matrix, each product is listed on the column and row header. The diagonal intersection is thus the self-elasticity of the product (light grey), and the cross elasticity between each given product will be found for each other portion of the matrix (darker grey). As the prices are tested in the various stores and transactions are collected, the degree of elasticity for each of these product pairs may be calculated. In some embodiments all products in the store may be included in this cross elasticity matrix, but due to the low degree of cross elasticity between entirely disparate items, this may not be desirable, particularly give the rather significant processing demands in calculating cross elasticities for such a large group of items. For example, the price of and given brand of butter likely has nearly no impact on the sales of cereal. Calculating a cross elasticity between these items would be basically valueless, but consumes considerable processing resources. As such, it may be desirable to calculate cross elasticities only between products in the same category, and some well-established associated products (such as gram crackers, large marshmallows and Hershey's chocolate bars).
generating, by the processor and from each segment of the historical sales transaction data, a normalization of a price-metric to be optimized; Montero teaches in ¶ 0194, after data verification by the price auditors 1820 a series of adjusters 1830 may modify the data to reduce the impact of external variables, and normalize the data. Montero teaches in ¶ 0194, a store and day adjuster 1833 may modify data by day and store. For example, in many places lift is much higher generally on weekend days as opposed to weekdays. The day adjuster may globally modify the t-log data to account for such day-to-day variations. Additionally, certain days tend to generate greater lift for particular goods or classes of goods. For example, eggs may sell at much higher rates before Easter, and grilled foods on Saturdays during the summer and especially before the 4th of July. Montero teaches in ¶ 0198, after day and store adjustments (and external factor adjustments, if desired) are applied, the t-log data may be normalized by store level attributes. For example, category sales by store maybe a function of percent category sales of the store, average basket size of the store, total store transactions, etc. These performance store attributes can be directly applied to category sales as coefficient adjustments or by normalizing the sales by a modeled value dependent on these attributes via GLM or OLS methods. Lastly promotional adjustment methods may be employed by the promo adjuster 1835. These promotional adjustment methods may include, for example, regressive methods or relative pair-wise methods. Accounting for promotional activity within a category is important given how products interact relative to one another from a consumer's buying preference. Given the time, store and specific product line groups on promotion, price elasticity measurement for non-promoted products are estimated by ensuring that promotional factors or variables are considered in, for example, a regression based model that looks to extract such elasticity coefficients while also accounting for promotional effects. Another approach looks to estimate these elasticity coefficients only when promotional activity on promoted line groups within a category is homogeneous across stores that have different test price points for non-promoted product line groups. Pair-wise comparisons of these particular types of stores will ensure that the cross-elastic promotional effect is experienced equally for the non-promoted tested product line groups. Montero teaches in ¶ 0199, After day and store adjustments (and external factor adjustments, if desired) are applied, the t-log data may be normalized by store level attributes. For example, category sales by store maybe a function of percent category sales of the store, average basket size of the store, total store transactions, etc. These performance store attributes can be directly applied to category sales as coefficient adjustments or by normalizing the sales by a modeled value dependent on these attributes via GLM or OLS methods. Lastly promotional adjustment methods may be employed by the promo adjuster 1835. These promotional adjustment methods may include, for example, regressive methods or relative pair-wise methods. Accounting for promotional activity within a category is important given how products interact relative to one another from a consumer's buying preference. Given the time, store and specific product line groups on promotion, price elasticity measurement for non-promoted products are estimated by ensuring that promotional factors or variables are considered in, for example, a regression based model that looks to extract such elasticity coefficients while also accounting for promotional effects. Another approach looks to estimate these elasticity coefficients only when promotional activity on promoted line groups within a category is homogeneous across stores that have different test price points for non-promoted product line groups. Pair-wise comparisons of these particular types of stores will ensure that the cross-elastic promotional effect is experienced equally for the non-promoted tested product line groups.
and reporting, by the processor, the optimized price metric to a user via a user interface; Montero teaches in ¶ 0147, if the goal is to maximize profit for the sale of a certain newly created brand of potato chips, embodiments of the invention optimally and adaptively, without using required human intervention, plan the test promotions, iterate through the test promotions to test the test promotion variables in the most optimal way, learn and validate such that the most result-effective set of test promotions can be derived, and provide such result-effective set of test promotions as recommendations for generalized public promotion to achieve the goal of maximizing profit for the sale of the newly created brand of potato chips.
While Montero teaches recommendations for generalized public promotion, normalize the data, and historical pricing and transaction data, Montero does not explicitly teach a histogram. However, Fano teaches the following:
generating, by the processor, a histogram distribution of the normalized price metric from each segment of the historical sales transaction data; Fano teaches in ¶ 0063, The replenishment interval at tj may include the number of days at tj since a product category pi was acquired. The frequency of interval at tj may be obtained by, for each product category pi, by building a frequency histogram for the interval at acquisition binned into several ranges (for example, 3-5 days, 7-9 days), and normalizing the frequency histogram by the total number of times the product category was acquired. The range into which the current acquisition falls may be the same as the ranges indicated for the frequency of interval at tj. Fano teaches in ¶ 0064, For each transaction t, in addition to encoding features of a current transaction, traits from prior transactions (the historical transaction data) may be extracted.
automatically determining, by the processor, an optimization of the price metric from the fitted model; Fano teaches in ¶ 0074, There may be different levels of detail in determining price sensitivity, such as at the individual level and at the cluster level. At the individual level, price sensitivities for each customer may be derived with respect to each product. And, shrinkage-like techniques may be used to smooth these estimates. The output of the derivations may comprise a tree of price sensitivities for each customer. The estimates at the leaf nodes may be determined in the following way: given customer C, product P, calculate pairs (R.sub.i.epsilon.R, P(R.sub.i)) where R is the set of all unique prices for product P during all of customer C visits, and ¶ 0075, P(Ri)=(number of times customer C visited the store and bought product P at price R)/(number of times customer C visited the store and price of product P was R). Fano teaches in ¶ 0076, given pairs (Ri, P(Ri)), a least squares fit may be performed to obtain a linear equation relating Pi and P(Ri). The slope of that line may be the price sensitivity and the R2 is the confidence. These individual price sensitivities may be aggregated and used to calculate price sensitivities at sub-category and category levels.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero with method and system for using individualized customer models when operating a retail establishment of Fano to assist businesses with using histograms with time segments that represent product acquisition (Fano Spec. ¶ 0063).
While Montero teaches recommendations for generalized public promotion, normalize the data, and historical pricing and transaction data, and Montero teaches histogram, neither Montero nor Fano explicitly teach histogram distribution and skewness. However, Giacobbe teaches the following:
automatically selecting, by the processor, a distribution model to fit the histogram distribution; Giacobbe teaches in claim 26, selecting a PDF based on the histogram and the fitting test; further teaches automated in ¶ 0068;
automatically selecting, by the processor, a right hand side model to fit the right hand side (RHS) of the histogram distribution; Giacobbe teaches in ¶ 0060, visualizing the skewness of the distribution, where the skewness may be fitted to the right hand side of the model;
fitting, by the processor, the selected RHS model to the histogram distribution model; Giacobbe teaches in ¶ 0061, distribution fitting tests may be performed on the data, predefined probability distributions may be compared with the fitness tests. Such tests may generate a relative score (out of 100, for example) based on the distribution parameters.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero and method and system for using individualized customer models when operating a retail establishment of Fano with calculating the baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels of Giacobbe to assist businesses with optimizing target stock levels for the inventory of parts and building a best fit model for replenishment and forecasting (Giacobbe, Spec. ¶ 0009).
Claims 7 and 15:
Montero, Fano, and Giacobbe teach claims 1, 9, and 16. Giacobbe further teaches the following:
wherein automatically determining the optimized price metric from the fitted model includes deriving a distribution model for the price metric from the fitted RHS model; Giacobbe teaches in ¶ 0007, a method to prioritize the management of a parts inventory through optimization of the model; Giacobbe teaches in claim 1 above automated and fitting the model where the distribution may be skewed.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero and method and system for using individualized customer models when operating a retail establishment of Fano with calculating the baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels of Giacobbe to assist businesses with optimizing target stock levels for the inventory of parts and building a best fit model for replenishment and forecasting (Giacobbe, Spec. ¶ 0009).
6. Claims 2, 10, and 17, are rejected under 35 U.S.C. 103 as being unpatentable Montero, Michael et al. (U.S. Publication No. 2023/0133390) hereinafter “Montero” in view of Fano, Andrew E et al. (U.S. Publication No. 2014/0114743) hereinafter “Fano” in view of Giacobbe, Robert A et al. (U.S. Publication No. 2014/017,2493) hereinafter “Giacobbe” in view of Samad-Khan, Ali (U.S. Publication No. 2012/015,0570) hereinafter “Samad-Khan”.
Claims 2, 10, and 17:
Montero, Fano, and Giacobbe teach claims 1, 9, and 16. Neither Montero, Fano, nor Giacobbe explicitly teach lognormal distribution model. However, Samad-Khan teaches the following:
The method of claim 1, wherein the distribution model is a lognormal distribution model; Samad-Khan teaches in ¶ 0019, normal or lognormal distribution.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero and method and system for using individualized customer models when operating a retail establishment of Fano and calculating the baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels of Giacobbe with calculation of relevant metrics used as a basis for estimating risk-based economic capital and/or to make informed risk based decisions of Samad-Khan to assist businesses in making informed risk-based business decisions, in real time. (Samad-Khan, Spec. ¶ 0079).
7. Claims 3 – 5, 11 – 13, and 18 – 20, are rejected under 35 U.S.C. 103 as being unpatentable Montero, Michael et al. (U.S. Publication No. 2023/0133390) hereinafter “Montero” in view of Fano, Andrew E et al. (U.S. Publication No. 2014/0114743) hereinafter “Fano” in view of Giacobbe, Robert A et al. (U.S. Publication No. 2014/017,2493) hereinafter “Giacobbe” in view of Pangerl, Christian Josef (U.S. Publication No. 2020/021,1131) hereinafter “Pangerl”.
Claims 3, 11, and 18:
Montero, Fano, and Giacobbe teach claims 1, 9, and 16. Neither Montero, Fano, nor Giacobbe explicitly teach residuals slightly right skewed and normal distribution. However, Pangerl teaches the following:
selecting, by the processor, a portion of the distribution data greater than the mean, wherein the right hand side of the histogram model includes the portion of the histogram distribution that is greater than the mean; Pangerl teaches in ¶ 0165, the residuals are slightly right skewed; further teaches in ¶ 0168, a histogram model with a normal distribution
and fitting, by the processor, the selected model to the portion of data greater than the mean; Pangerl teaches residuals may be skewed right; Pangerl teaches in ¶ 0033, fitting a mathematical model.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero and method and system for using individualized customer models when operating a retail establishment of Fano and calculating the baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels of Giacobbe with predicting rental price in a real estate management system and modifying the expected price based on an expected level of occupancy of Pangerl to assist businesses with estimating rental sales using the mean (Pangerl Spec. ¶ 0099).
Claims 4, 12, and 19:
Montero, Fano, and Giacobbe teach claims 1, 9, and 16. Neither Montero, Fano, nor Giacobbe explicitly teach residuals slightly right skewed and two dimension. However, Pangerl teaches the following:
wherein fitting the selected RHS model and the histogram model includes fitting the models to a two-dimensional point; Pangerl teaches in claim 2 fitting the model skewed to the right; Pangerl further teaches in ¶ 0124, two dimensional.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero and method and system for using individualized customer models when operating a retail establishment of Fano and calculating the baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels of Giacobbe with predicting rental price in a real estate management system and modifying the expected price based on an expected level of occupancy of Pangerl to assist businesses with estimating rental sales using the mean (Pangerl Spec. ¶ 0099).
Claims 5, 13, and 20:
While Montero, Fano, and Giacobbe teach claims 1, 9, and 16; and Montero further teaches values of the log being above a certain standard deviation from the average log value for the given product in the same geographic location; and Fano teaches a two dimensional Kalman filter can be used to estimate and update both elasticity and the success rate and he model coefficients are generally significant, by at least one standard deviation; neither Montero, Fano, nor Giacobbe explicitly teach sum of a mean. However, Pangerl teaches the following:
wherein the two-dimensional point is defined by the sum of a mean and a first standard deviation in the distribution model; Pangerl teaches in ¶ 0099, a sum of the mean; Pangerl further teaches in ¶ 0158, the model coefficients are generally significant, by at least one standard deviation, where at least one standard deviation is likened to a first standard deviation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero and method and system for using individualized customer models when operating a retail establishment of Fano and calculating the baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels of Giacobbe with predicting rental price in a real estate management system and modifying the expected price based on an expected level of occupancy of Pangerl to assist businesses with estimating rental sales using the mean (Pangerl Spec. ¶ 0099).
8. Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable Montero, Michael et al. (U.S. Publication No. 2023/0133390) hereinafter “Montero” in view of Fano, Andrew E et al. (U.S. Publication No. 2014/0114743) hereinafter “Fano” in view of Giacobbe, Robert A et al. (U.S. Publication No. 2014/017,2493) hereinafter “Giacobbe” in view of in view of Anderson, Russell Wayne (U.S. Publication No. 2020/025,0185).
Claims 6 and 14:
While Montero, Fano, and Giacobbe teach claims 1, 9, and 16, Neither Montero, Fano, nor Giacobbe teach explicitly teach Taylor series expansion. However, Anderson teaches the following:
wherein fitting includes using a Taylor series expansion; Anderson teaches in ¶ 0111, using Taylor series for model fitting the parameters.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine systems and methods for optimizing base pricing of products within a physical retailer of Montero and method and system for using individualized customer models when operating a retail establishment of Fano and calculating the baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels of Giacobbe with method and system is disclosed for storing and manipulating customer transaction data received from a plurality of sources to assist businesses with estimating product variables using Taylor series (Anderson, Spec. ¶ 0111).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Hills, Eric et al. (U.S. Publication No. 2012/0290361) discloses a system and method for efficiently estimating the sensitivity, or elasticity, of customer demand to changes in price in a business-to-business market environment.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
1/09/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625