DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 22, 2026, has been entered.
Claims 1, 9, and 16 are amended.
Claims 1-20 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims are subject matter eligible because the claims recite steps that cannot be performed in the human mind. See Remarks pp. 15-16. In response, the Examiner points to the rejection, below, which concludes that the claims are directed to a method of organizing human activity. The Applicant’s arguments with respect to the mental processes category of abstract idea are moot. The newly recited hardware is generic computer hardware that does not provide a practical application or significantly more than the recited abstract idea.
The Applicant additionally submits that the claims recite a method that is rooted in computer technology because the computations require specialized hardware and software. See Remarks p. 16. In response, the Examiner submits that clustering products into categories does not inherently require a computer. The claims merely recite the use of a computer as a tool to perform computations. The claims do not recite any steps that are intrinsically rooted in computer technology.
The Applicant further contends that the claims are subject matter eligible because the claims provide a practical application by providing real-time results. See Remarks p. 17. In response, the Examiner submits that the use of a computer as a tool for performing real-time calculations is understood. Again, the Examiner reiterates that the use of a computer as a tool to process data in real-time is understood. Virtually all client or end user computing devices include a graphical user interface for displaying data, and an input mechanism. Therefore, the user interface and display amount to generic computer components that do not provide a practical application or significantly more than the recited abstract idea. The user input is part of the abstract idea. A human being using pen and paper could record data in the same manner. However, a generic computer is recited for inputting and manipulating data. The data manipulation steps could also be performed on paper by a human being – normalizing data, etc. Lack of conventionality does not imply subject matter eligibility. An abstract idea without significantly more is just that – an abstract idea.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further consideration of the prior art. The independent claims remain obvious over the previously cited prior art.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to providing an optimal unit product value (as evidenced by exemplary independent claim 1; “providing . . . at least the optimal unit product value”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “obtaining . . . cumulative product consumption data;” “re-scaling . . . the cumulative product consumption data;” “obtaining a predefined cluster determination plot;” “storing . . . the predefined cluster determination plot;” “mapping . . . the re-scaled cumulative product consumption data;” “clustering . . . the re-scaled cumulative product consumption data;” “rendering . . . the plurality of clusters . . . for determining an optimal unit product value;” “providing . . . at least the optimal unit product value . . . as feedback;” and “updating . . . the plurality of clusters.” The steps are all steps for managing personal behavior related to the abstract idea of providing an optimal unit product value that, when considered alone and in combination, are part of the abstract idea of providing an optimal unit product value. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of providing an optimal unit product value. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes determining an optimal price based on demand by using cluster analysis.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a processor, memory, graphical user interface, and computing device in independent claims 1 and 9; and a computer-readable medium, processor, memory, graphical user interface, and computing device in independent claim 16). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the training of a machine learning model, but the abstract idea of providing an optimal unit product value is generally linked to a machine learning environment for implementation. Therefore, the recitation of training a machine learning model merely amounts to a technological environment that does not provide a practical application or significantly more than an abstract idea. See MPEP §2106.05(h) The claims require no more than a generic computer (a processor, memory, graphical user interface, and computing device in independent claims 1 and 9; and a computer-readable medium, processor, memory, graphical user interface, and computing device in independent claim 16) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 5, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to Sawarkar et al. (hereinafter ‘SAWARKAR’) in view of US 20230316219 A1 to Agrawal et al. (hereinafter ‘AGRAWAL’), US 20220043691 A1 to Willemain et al. (hereinafter ‘WILLEMAIN’), and US 20090327163 A1 to Swan et al. (hereinafter ‘SWAN’).
Claim 1 (Currently Amended)
SAWARKAR discloses a method comprising: obtaining, by a processor (see ¶[0006]; instructions executing on a processor), cumulative product consumption data for a plurality of products (see abstract and ¶[0031]; obtain price-demand data for a product. Enable accurate responses to disturbances in demand for various products), cumulative product consumption data of each of the plurality of products including at least three data points indicative of varying cumulative product consumption quantities (see abstract and ¶[0065]-[0067]; historic data demand curves with data points for a variety of products) with respect to varying unit product values for a respective product (see again abstract; price and demand data).
SAWARKAR does not specifically disclose, but AGRAWAL discloses, wherein the cumulative product consumption data is generated based on demand curve data, the cumulative product consumption quantity at a unit product value being indicative of a product consumption quantity at the unit product value and at unit product values higher than the unit product value (see abstract; a system predicts whether repositories have excess products or product shortages. The system predicts supply and demand over a period of time including the product supply window and product shortage window).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, re-scaling, by the processor, the cumulative product consumption data of each product by normalizing the varying cumulative product consumption quantities to be within a first predefined numerical range (see ¶[0023]; normalize cumulative demand to be within a range of zero to one).
SAWARKAR does not specifically disclose, but SWAN discloses, normalizing the varying unit product values to be within a second predefined numerical range (see ¶[0223]; scale prices or costs between zero and one).
SAWARKAR further discloses obtaining, by the processor, a predefined cluster (see claim 10; segmenting, by a processor, one or more products into a complementary product cluster or a competitive product cluster based on a demand history data of the one or more products and attributes of the one or more products).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, determination plot generated by the processor (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
SAWARKAR further discloses the predefined cluster determination plot including a plurality of non-overlapping regions, each of the plurality of non-overlapping regions being confined by a subset of values within the first predefined numerical range associated with a cumulative product consumption quantity and a subset of values within the second predefined numerical range associated with a unit product value (see abstract; obtaining price-demand data for a product, macro-clustering the price-demand data to identify a plurality of product categories, building a plurality of demand curves corresponding to the product categories, micro-clustering the demand curves to find a refined set of demand curves for each of the product categories, selecting one of the refined set of demand curves based on a difference between a predicted demand and an observed demand, selecting a price for the product according to the selected one of the demand curves); and
storing, by the processor, the predefined cluster determination plot in at least an internal or external memory of the system (see ¶[0007]; or more embodiments of the invention or elements thereof can be implemented in the form of a system (or apparatus) including a memory, and at least one processor that is coupled to the memory and operative to perform exemplary method steps. See also ¶[0127] and [0140]-[0141]; external disk drive arrays and storage devices).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, mapping, by a cluster generation module, the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions in the stored predefined cluster determination plot (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
SAWARKAR further discloses clustering, by the cluster generation module, the re-scaled cumulative product consumption data of each product into a plurality of clusters based on the mapping of the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions (see abstract; obtaining price-demand data for a product, macro-clustering the price-demand data to identify a plurality of product categories, building a plurality of demand curves corresponding to the product categories, micro-clustering the demand curves to find a refined set of demand curves for each of the product categories, selecting one of the refined set of demand curves based on a difference between a predicted demand and an observed demand, selecting a price for the product according to the selected one of the demand curves);
rendering, by the cluster generation module, a graphical representation of the plurality of clusters on a graphical user interface of a computing device (see ¶[0128]; an input/output interface to a display. Provide results associated with the processing unit. See also ¶[0068], [0084], & [0101] and Figs. 7 & 14; k-means clustering plots and spectral clustering plots), for determining an optimal unit product value for at least one of the plurality of products (see ¶[0011]; determination of a number meta-clustering demand curves for demand learning that optimizes price and resource allocation), wherein the graphical representation enables visualization of the distribution of cumulative product consumption data for each product across the plurality of clusters (see ¶[0078]; the demand for each cluster is visualized and the demand curve patterns are verified); and
providing, by a value prediction module, at least the optimal unit product value for at least one of the plurality of products as feedback to the cluster generation module (see ¶[0011] and [0100]; determination of a number meta-clustering demand curves for demand learning that optimizes price and resource allocation. A feedback mechanism) based on at least a user input, wherein the user input is received via the graphical user interface and is indicative of the optimal unit product value based on the visualized clusters (see ¶[0128] and [0131]; a keyboard and pointing device. Input data to the processing unit. See also ¶[0011], [0044], [0067]. and [0088]-[0089]; optimize price and resource allocation. Optimize resource use. An optimal price is calculated);
in response to the feedback, updating, by the cluster generation module, the plurality of clusters by modifying at least cluster boundaries or assignments based on the received optimal unit product value (see abstract and ¶[0067 & [0100] and Fig. 4; a feedback loop mechanism which periodically determines an optimal price through macro clustering and micro clustering. Macro clustering identifies product categories. An optimal price is calculated. Demand curves are built for each cluster).
training, by the cluster generation module, a machine learning model by adjusting the plurality of non-overlapping regions automatically for predicting at least the optimal unit product value (see ¶[0061] and [0067]-[0069]; an optimal price is calculated that optimizes resource allocation. Automated parameter tuning, including the number of meta clusters, are described), based on the updated plurality of clusters and historical performance of clusters and sales data associated with previously determined optimal unit product values (see ¶[0002]-[0003] and [0030]; conventional demand prediction is performed based on historical data).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. AGRAWAL discloses redistributing product inventory based on a prediction of a shortage. It would have been obvious to include the prediction of a shortage as taught by AGRAWAL in the system executing the method of SAWARKAR with the motivation to redistribute products based on a prediction of supply and demand (see SAWARKAR abstract).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. It would have been obvious to include demand plots of clusters on a normalized scale as taught by WILLEMAIN in the system executing the method of SAWARKAR with the motivation to visualize product demand clusters.
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. It would have been obvious to normalize price as taught by SWAN in the system executing the method of SAWARAKAR and WILLEMAIN with the motivation to provide an appropriate scale for analysis (see SWAN ¶[0222]).
Claim 3 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN discloses the method as set forth in claim 1.
SAWARKAR further discloses wherein the method comprises: generating, by the processor, the cumulative product consumption data of the each product based on demand curve data of the each product, wherein the demand curve data is indicative of product consumption quantities with respect to the varying unit product values (see abstract; demand data and demand curves for a product).
Claim 5 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN discloses the method as set forth in claim 1.
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, wherein the first predefined numerical range is from 0 to 1, wherein the second predefined numerical range is from 0 to 1 (see ¶[0223]; scale prices or costs between zero and one).
SAWARKAR further discloses wherein the plurality of clusters comprises five clusters (see ¶[0067]; According to FIG. 3 a macro-customer segmentation model is created 301 for k=alpha (a), which segments the (potentially incomplete) continuous data space into a number of macro clusters),
wherein a first cluster of the five clusters is associated with a first unique non- overlapping region confined by the cumulative product consumption quantity in a range 0 to 0.4 and the unit product value in a range 0 to 0.2 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a second cluster of the five clusters is associated with a second unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.6 and the unit product value in a range 0.2 to 0.4 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a third cluster of the five clusters is associated with a third unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.8 and the unit product value in a range 0.4 to 0.6 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a fourth cluster of the five clusters is associated with a fourth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.4 to 0.8 and the unit product value in a range 0.6 to 0.8 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation) and
wherein a fifth cluster of the five clusters is associated with a fifth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.6 to 1 and the unit product value in a range 0.8 to 1 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al., US 20230316219 A1 to AGRAWAL et al., US 20220043691 A1 to WILLEMAIN et al., and US 20090327163 A1 to SWAN et al. as applied to claim 1 above, and further in view of US 20150142521 A1 to Aydin et al. (hereinafter ‘AYDIN’).
Claim 2 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN discloses the method as set forth in claim 1.
SAWARAKAR further discloses wherein each of the plurality of clusters is associated to a unique non- overlapping region of the plurality of non-overlapping regions (see claim 10; segmenting, by a processor, one or more products into a complementary product cluster or a competitive product cluster based on a demand history data of the one or more products and attributes of the one or more products).
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN does not specifically disclose, but AYDIN discloses, wherein the mapping of the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions in the predefined cluster determination plot comprises: identifying, by the cluster generation module, a non-overlapping region from the plurality of non-overlapping regions in which a maximum number of data points from amongst at least three data points of the re-scaled cumulative product consumption data for the respective product lie (see ¶[0068]; iteratively keep dividing the cluster with the largest population until the desired number of clusters is reached); and
wherein the clustering comprises: incorporating, by the cluster generation module, the re-scaled cumulative product consumption data in the cluster associated to the identified non-overlapping region (see again ¶[0068]; iteratively keep dividing the cluster with the largest population until the desired number of clusters is reached).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. AYDIN discloses clustering, where the largest cluster is divided until a desired number of clusters is reached. It would have been obvious to include the dividing clusters as taught by AYDIN in the system executing the method of SAWARKAR with the motivation to cluster products for demand learning.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al. in view of US 20230316219 A1 to AGRAWAL et al., US 20220043691 A1 to WILLEMAIN et al., and US 20090327163 A1 to SWAN et al. as applied to claim 1 above, and further in view of US 20120215574 A1 to Driessnack et al. (hereinafter ‘DRIESSNACK’).
Claim 4 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN discloses the method as set forth in claim 1.
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN does not explicitly disclose, but DRIESSNACK discloses, wherein normalizing the varying cumulative product consumption quantities in the first predefined numerical range comprises: dividing, by the processor, each of the varying cumulative product consumption quantities by a maximum value of the varying cumulative product consumption quantities (see ¶[0449]; totals for each discipline transparency score are tabulated (Table 18) and then normalized by dividing by the maximum score (i.e., by 10)); and
wherein normalizing the varying unit product values in the second predefined numerical range comprises: dividing, by the processor, each of the varying unit product values by a maximum value of the varying unit product values (see again ¶[0449]; totals for each discipline transparency score are tabulated (Table 18) and then normalized by dividing by the maximum score (i.e., by 10)).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. DRIESSNACK discloses normalizing scores based dividing by the maximum score. It would have been obvious to normalize scores as taught by DRIESSNACK in the system executing the method with the motivation to normalize values on product demand cluster plots.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al. in view of US 20230316219 A1 to AGRAWAL et al., US 20220043691 A1 to WILLEMAIN et al., and US 20090327163 A1 to SWAN et al. as applied to claim 1 above, and further in view of US 20210304233 A1 to Jain et al. (hereinafter ‘JAIN’).
Claim 6 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN discloses the method as set forth in claim 1.
SAWARKAR does not specifically disclose, but SWAN discloses, wherein the first predefined numerical range is from 0 to 1, wherein the second predefined numerical range is from 0 to 1 (see ¶[0223]; scale prices or costs between zero and one).
The combination of SAWARKAR, WILLEMAIN, and SWAN does not explicitly disclose, but JAIN discloses wherein the plurality of clusters comprises five clusters, wherein a first cluster of the five clusters is associated with a first unique non- overlapping region confined by the cumulative product consumption quantity in a range 0 to 0.2 and the unit product value in a range 0 to 0.2 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a second cluster of the five clusters is associated with a second unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.4 and the unit product value in a range 0.2 to 0.4 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a third cluster of the five clusters is associated with a third unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.4 to 0.6 and the unit product value in a range 0.4 to 0.6 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a fourth cluster of the five clusters is associated with a fourth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.6 to 0.8 and the unit product value in a range 0.6 to 0.8 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation), and
wherein a fifth cluster of the five clusters is associated with a fifth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.8 to 1 and the unit product value in a range 0.8 to 1 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. It would have been obvious to normalize price as taught by SWAN in the system executing the method of SAWARAKAR and WILLEMAIN with the motivation to provide an appropriate scale for analysis (see SWAN ¶[0222]).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. JAIN discloses product clusters based on price. It would have been obvious to define product clusters based on price as taught by JAIN in the system executing the method of SAWRAKAR with the motivation to predict demand of products (see Jain ¶[0002]).
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al. in view of US 20230316219 A1 to AGRAWAL et al., US 20220043691 A1 to WILLEMAIN et al., and US 20090327163 A1 to SWAN et al. as applied to claim 1 above, and further in view of US 20100125563 A1 to Nair et al. (hereinafter ‘NAIR’).
Claim 7 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN discloses the method as set forth in claim 1.
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN does not specifically disclose, but NAIR discloses, wherein the method comprises: upon identifying more than one of the plurality of non-overlapping regions in which a maximum number of data points from amongst at least three data points of the re-scaled cumulative product consumption data for the respective product lie based on the mapping, incorporating, by the cluster generation module, the re- scaled cumulative product consumption data of the respective product in a null cluster (see ¶[0434]; the data cluster identification module 4520 is further configured to output result sets of such searches, where the set comprising identifications of one or more data clusters (or is a null set if no clusters meet query generation criteria.)
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. NAIR discloses null clusters when a cluster does not meet the criteria for a query. It would have been obvious to include null clusters as taught by NAIR in the system executing the method of SAWARKAR if the data does not meet cluster generation or query criteria.
Claim 8 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN discloses the method as set forth in claim 1.
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, and SWAN does not specifically disclose, but NAIR discloses, wherein the method comprises: upon identifying that none of at least three data points of the re-scaled cumulative product consumption data for the respective product lie in the plurality of non-overlapping regions based on the mapping, incorporating, by the cluster generation module, the re-scaled cumulative product consumption data of the respective product in a null cluster (see ¶[0434]; the data cluster identification module 4520 is further configured to output result sets of such searches, where the set comprising identifications of one or more data clusters (or is a null set if no clusters meet query generation criteria.).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. NAIR discloses null clusters when a cluster does not meet the criteria for a query. It would have been obvious to include null clusters as taught by NAIR in the system executing the method of SAWARKAR if the data does not meet cluster generation or query criteria.
Claim(s) 9, 11, 12, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al. in view of US 20230316219 A1 to AGRAWAL et al., US 20220043691 A1 to WILLEMAIN et al., US 20200057918 A1 to Shin et al. (hereinafter ‘SHIN’), and US 20090327163 A1 to SWAN et al.
Claim 9 (Currently Amended)
SAWARKAR discloses a system comprising : a processor (see ¶[0006]; instructions executing on a processor) to: obtain cumulative product consumption data for a product (see abstract and ¶[0031]; obtain price-demand data for a product. Enable accurate responses to disturbances in demand for various products), the cumulative product consumption data including at least three data points, the at least three data points being indicative of varying cumulative product consumption quantities (see abstract and ¶[0065]-[0067]; historic data demand curves with data points for a variety of products) with respect to varying unit product values for the product (see again abstract; price and demand data).
SAWARKAR does not specifically disclose, but AGRAWAL discloses, wherein the cumulative product consumption data is generated based on demand curve data, the cumulative product consumption quantity at a unit product value being indicative of a product consumption quantity at the unit product value and at unit product values higher than the unit product value (see abstract; a system predicts whether repositories have excess products or product shortages. The system predicts supply and demand over a period of time including the product supply window and product shortage window).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, re-scale the cumulative product consumption data by normalizing the varying cumulative product consumption quantities to be within a first predefined numerical range (see ¶[0023]; normalize cumulative demand to be within a range of zero to one).
SAWARKAR does not specifically disclose, but SWAN discloses, and normalizing the varying unit product values to be within a second predefined numerical range (see ¶[0223]; scale prices or costs between zero and one).
SAWARKAR further discloses, and obtain a predefined cluster (see claim 10; segmenting, by a processor, one or more products into a complementary product cluster or a competitive product cluster based on a demand history data of the one or more products and attributes of the one or more products).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, determination plot generated by the processor (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
SAWARKAR further discloses the predefined cluster determination plot including a plurality of non-overlapping regions, each of the plurality of non-overlapping regions being confined by a subset of values within the first predefined numerical range associated with a cumulative product consumption quantity (see abstract; obtaining price-demand data for a product, macro-clustering the price-demand data to identify a plurality of product categories, building a plurality of demand curves corresponding to the product categories, micro-clustering the demand curves to find a refined set of demand curves for each of the product categories, selecting one of the refined set of demand curves based on a difference between a predicted demand and an observed demand, selecting a price for the product according to the selected one of the demand curves)
SAWARKAR does not specifically disclose, but SHIN discloses, as ordinates and a subset of values within the second predefined numerical range associated with a unit product value as abscissas (see Fig. 1; a curve of demand v. price).
SAWARKAR further discloses store the predefined cluster determination plot in at least an internal or external memory of the system (see ¶[0007]; or more embodiments of the invention or elements thereof can be implemented in the form of a system (or apparatus) including a memory, and at least one processor that is coupled to the memory and operative to perform exemplary method steps. See also ¶[0127] and [0140]-[0141]; external disk drive arrays and storage devices).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, a cluster generation module to: map the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions in the stored predefined cluster determination plot (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
SAWARKAR further discloses, incorporate the re-scaled cumulative product consumption data in a cluster from a plurality of clusters based on the mapping of the re-scaled cumulative product consumption data with respect to the plurality of non- overlapping regions in the predefined cluster determination plot (see abstract; obtaining price-demand data for a product, macro-clustering the price-demand data to identify a plurality of product categories, building a plurality of demand curves corresponding to the product categories, micro-clustering the demand curves to find a refined set of demand curves for each of the product categories, selecting one of the refined set of demand curves based on a difference between a predicted demand and an observed demand, selecting a price for the product according to the selected one of the demand curves); and
render a graphical representation of the plurality of clusters on a graphical user interface of a computing device (see ¶[0128]; an input/output interface to a display. Provide results associated with the processing unit. See also ¶[0068], [0084], & [0101] and Figs. 7 & 14; k-means clustering plots and spectral clustering plots), to determine an optimal unit product value for the product (see ¶[0011]; determination of a number meta-clustering demand curves for demand learning that optimizes price and resource allocation), wherein the graphical representation enables visualization of the distribution of cumulative product consumption data for each product across the plurality of clusters (see ¶[0078]; the demand for each cluster is visualized and the demand curve patterns are verified);
provide, by a value prediction module, at least the optimal unit product value for at least one of the plurality of products as feedback to the cluster generation module (see ¶[0011] and [0100]; determination of a number meta-clustering demand curves for demand learning that optimizes price and resource allocation. A feedback mechanism) based on at least the user input, wherein the user input is received via the graphical user interface and is indicative of the optimal unit product value based on the visualized clusters (see ¶[0128] and [0131]; a keyboard and pointing device. Input data to the processing unit. See also ¶[0011], [0044], [0067]. and [0088]-[0089]; optimize price and resource allocation. Optimize resource use. An optimal price is calculated);
in response to the feedback, update, by the cluster generation module, the plurality of clusters by modifying at least cluster boundaries or assignments based on the received optimal unit product value (see abstract and ¶[0067 & [0100] and Fig. 4; a feedback loop mechanism which periodically determines an optimal price through macro clustering and micro clustering. Macro clustering identifies product categories. An optimal price is calculated. Demand curves are built for each cluster).
train a machine learning model by adjusting the plurality of non-overlapping regions automatically for predicting at least the optimal unit product value (see ¶[0061] and [0067]-[0069]; an optimal price is calculated that optimizes resource allocation. Automated parameter tuning, including the number of meta clusters, are described), based on the updated plurality of clusters and historical performance of clusters and sales data associated with previously determined optimal unit product values (see ¶[0002]-[0003] and [0030]; conventional demand prediction is performed based on historical data).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. AGRAWAL discloses redistributing product inventory based on a prediction of a shortage. It would have been obvious to include the prediction of a shortage as taught by AGRAWAL in the system executing the method of SAWARKAR with the motivation to redistribute products based on a prediction of supply and demand (see SAWARKAR abstract).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. It would have been obvious to include demand plots of clusters on a normalized scale as taught by WILLEMAIN in the system executing the method of SAWARKAR with the motivation to visualize product demand clusters.
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. SHIN discloses training artificial intelligence to predict utilization of resources that includes a curve of demand versus price. It would have been obvious to plot demand vs. price as taught by SHIN in the system executing the method of SAWARKAR with the motivation to predict utilization of resources.
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. It would have been obvious to normalize price as taught by SWAN in the system executing the method of SAWARAKAR and WILLEMAIN with the motivation to provide an appropriate scale for analysis (see SWAN ¶[0222]).
Claim 11 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the system as set forth in claim 9.
SAWARKAR further discloses wherein the system comprises: a value prediction module to provide the optimal unit product value for the product as feedback to the cluster generation module (see ¶[0011] and [0100]; determination of a number meta-clustering demand curves for demand learning that optimizes price and resource allocation. A feedback mechanism).
Claim 12 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the system as set forth in claim 9.
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, wherein the processor is to: obtain demand curve data indicative of a product consumption quantity of the product with respect to each of the varying unit product values; and compute a cumulative product consumption quantity of the varying cumulative product consumption quantities with respect to a unit product value of the varying unit product values by adding the product consumption quantity at the unit product value and the product consumption quantity at the varying unit product values higher than the unit product value (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. It would have been obvious to include demand plots of clusters on a normalized scale as taught by WILLEMAIN in the system executing the method of SAWARKAR with the motivation to visualize product demand clusters.
Claim 16 (Currently Amended)
SAWARKAR discloses a non-transitory computer readable medium (see ¶[0007]; a computer readable medium) having instructions stored thereon, the instructions, when executed by a processor (see ¶[0006]; instructions executable by a processor), cause the processor to perform operations comprising:
obtaining demand curve data for a plurality of products (see abstract and ¶[0031]; obtain price-demand data for a product. Enable accurate responses to disturbances in demand for various products), demand curve data of each of the plurality of products including at least three data points, the at least three data points being indicative of a product consumption quantity (see abstract and ¶[0065]-[0067]; historic data demand curves with data points for a variety of products) with respect to each unit product value of varying unit product values for a respective product (see again abstract; price and demand data).
SAWARKAR does not specifically disclose, but AGRAWAL discloses, wherein the cumulative product consumption data is generated based on demand curve data, the cumulative product consumption quantity at a unit product value being indicative of a product consumption quantity at the unit product value and at unit product values higher than the unit product value (see abstract; a system predicts whether repositories have excess products or product shortages. The system predicts supply and demand over a period of time including the product supply window and product shortage window).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, generating, for the each product, cumulative product consumption data that is indicative of varying cumulative product consumption quantities with respect to varying unit product values for the each product, wherein a cumulative product consumption quantity of the varying cumulative product consumption quantities with respect to a unit product value of the varying unit product values is computed by adding the product consumption quantity at the unit product value and the product consumption quantity at the varying unit product values higher than the unit product value (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
re-scaling the cumulative product consumption data of the each product by normalizing the varying cumulative product consumption quantities to be within a first predefined numerical range (see ¶[0023]; normalize cumulative demand to be within a range of zero to one).
SAWARKAR does not specifically disclose, but SWAN discloses, normalizing the varying unit product values to be within a second predefined numerical range (see ¶[0223]; scale prices or costs between zero and one).
SAWARKAR further discloses obtaining a predefined cluster (see claim 10; segmenting, by a processor, one or more products into a complementary product cluster or a competitive product cluster based on a demand history data of the one or more products and attributes of the one or more products).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, determination plot generated by the processor (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
SAWARKAR further discloses, the predefined cluster determination plot including a plurality of non-overlapping regions, wherein each of the plurality of non-overlapping regions is confined by a subset of values within the first predefined numerical range associated with a cumulative product consumption quantity (see abstract; obtaining price-demand data for a product, macro-clustering the price-demand data to identify a plurality of product categories, building a plurality of demand curves corresponding to the product categories, micro-clustering the demand curves to find a refined set of demand curves for each of the product categories, selecting one of the refined set of demand curves based on a difference between a predicted demand and an observed demand, selecting a price for the product according to the selected one of the demand curves)
SAWARKAR does not specifically disclose, but SHIN discloses, as ordinates and a subset of values within the second predefined numerical range associated with a unit product value as abscissas (see Fig. 1; a curve of demand v. price).
SAWARKAR further discloses storing, by the processor, the predefined cluster determination plot in at least an internal or external memory of the system (see ¶[0007]; or more embodiments of the invention or elements thereof can be implemented in the form of a system (or apparatus) including a memory, and at least one processor that is coupled to the memory and operative to perform exemplary method steps. See also ¶[0127] and [0140]-[0141]; external disk drive arrays and storage devices).
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, mapping the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions in the stored predefined cluster determination plot (see abstract and ¶[0003]-[0005]; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of homogeneous groups or clusters based on calculated distance metrics; and generating a visualization of each cluster).
SAWARKAR further discloses, clustering the re-scaled cumulative product consumption data of the each product into a plurality of clusters based on the mapping of the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions in the predefined cluster determination plot (see abstract; obtaining price-demand data for a product, macro-clustering the price-demand data to identify a plurality of product categories, building a plurality of demand curves corresponding to the product categories, micro-clustering the demand curves to find a refined set of demand curves for each of the product categories, selecting one of the refined set of demand curves based on a difference between a predicted demand and an observed demand, selecting a price for the product according to the selected one of the demand curves);
rendering a graphical representation of the plurality of clusters on a graphical user interface of a computing device (see ¶[0128]; an input/output interface to a display. Provide results associated with the processing unit. See also ¶[0068], [0084], & [0101] and Figs. 7 & 14; k-means clustering plots and spectral clustering plots), for determining an optimal unit product value for at least one of the plurality of products (see ¶[0011]; determination of a number meta-clustering demand curves for demand learning that optimizes price and resource allocation), wherein the graphical representation enables visualization of the distribution of cumulative product consumption data for each product across the plurality of clusters (see ¶[0078]; the demand for each cluster is visualized and the demand curve patterns are verified); and
providing at least the optimal unit product value for at least one of the plurality of products as feedback to a cluster generation module. (see ¶[0011] and [0100]; determination of a number meta-clustering demand curves for demand learning that optimizes price and resource allocation. A feedback mechanism) based on at least a user input, wherein the user input is received via the graphical user interface and is indicative of the optimal unit product value based on the visualized clusters (see ¶[0128] and [0131]; a keyboard and pointing device. Input data to the processing unit. See also ¶[0011], [0044], [0067]. and [0088]-[0089]; optimize price and resource allocation. Optimize resource use. An optimal price is calculated);
in response to the feedback, updating, by the cluster generation module, the plurality of clusters by modifying at least cluster boundaries or assignments based on the received optimal unit product value (see abstract and ¶[0067 & [0100] and Fig. 4; a feedback loop mechanism which periodically determines an optimal price through macro clustering and micro clustering. Macro clustering identifies product categories. An optimal price is calculated. Demand curves are built for each cluster).
train a machine learning model by adjusting the plurality of non-overlapping regions automatically for predicting at least the optimal unit product value (see ¶[0061] and [0067]-[0069]; an optimal price is calculated that optimizes resource allocation. Automated parameter tuning, including the number of meta clusters, are described), based on the updated plurality of clusters and historical performance of clusters and sales data associated with previously determined optimal unit product values (see ¶[0002]-[0003] and [0030]; conventional demand prediction is performed based on historical data).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. AGRAWAL discloses redistributing product inventory based on a prediction of a shortage. It would have been obvious to include the prediction of a shortage as taught by AGRAWAL in the system executing the method of SAWARKAR with the motivation to redistribute products based on a prediction of supply and demand (see SAWARKAR abstract).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. It would have been obvious to include demand plots of clusters on a normalized scale as taught by WILLEMAIN in the system executing the method of SAWARKAR with the motivation to visualize product demand clusters.
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. SHIN discloses training artificial intelligence to predict utilization of resources that includes a curve of demand versus price. It would have been obvious to plot demand vs. price as taught by SHIN in the system executing the method of SAWARKAR with the motivation to predict utilization of resources.
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. It would have been obvious to normalize price as taught by SWAN in the system executing the method of SAWARAKAR and WILLEMAIN with the motivation to provide an appropriate scale for analysis (see SWAN ¶[0222]).
Claim 19 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the non-transitory computer readable medium as set forth in claim 16.
SAWARKAR does not specifically disclose, but WILLEMAIN discloses, wherein the first predefined numerical range is from 0 to 1 wherein the second predefined numerical range is from 0 to 1, (see ¶[0223]; scale prices or costs between zero and one).
SAWARKAR further discloses wherein the plurality of clusters comprises five clusters (see ¶[0067]; According to FIG. 3 a macro-customer segmentation model is created 301 for k=alpha (a), which segments the (potentially incomplete) continuous data space into a number of macro clusters),
wherein a first cluster of the five clusters is associated with a first unique non- overlapping region confined by the cumulative product consumption quantity in a range 0 to 0.4 and the unit product value in a range 0 to 0.2 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a second cluster of the five clusters is associated with a second unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.6 and the unit product value in a range 0.2 to 0.4 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a third cluster of the five clusters is associated with a third unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.8 and the unit product value in a range 0.4 to 0.6 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a fourth cluster of the five clusters is associated with a fourth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.4 to 0.8 and the unit product value in a range 0.6 to 0.8 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation), and
wherein a fifth cluster of the five clusters is associated with a fifth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.6 to 1 and the unit product value in a range 0.8 to 1 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. It would have been obvious to normalize price as taught by SWAN in the system executing the method of SAWARAKAR and WILLEMAIN with the motivation to provide an appropriate scale for analysis (see SWAN ¶[0222]).
Claim(s) 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al. in view of US 20230316219 A1 to AGRAWAL et al., , US 20220043691 A1 to WILLEMAIN et al., US 20200057918 A1 to SHIN et al., and US 20090327163 A1 to SWAN et al. as applied to claim 9 above, and further in view of US 20150142521 A1 to AYDIN et al.
Claim 10 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the system as set forth in claim 9.
SAWARAKAR further discloses wherein each of the plurality of clusters is associated to a unique non- overlapping region of the plurality of non-overlapping regions (see claim 10; segmenting, by a processor, one or more products into a complementary product cluster or a competitive product cluster based on a demand history data of the one or more products and attributes of the one or more products).
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN does not specifically disclose, but AYDIN discloses, wherein to map the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions in the predefined cluster determination plot, the cluster generation module is to: identify a non-overlapping region from the plurality of non-overlapping regions in which a maximum number of data points from amongst at least three data points of the re-scaled cumulative product consumption data lie (see ¶[0068]; iteratively keep dividing the cluster with the largest population until the desired number of clusters is reached); and
wherein the re-scaled cumulative product consumption data is incorporated in the cluster that is associated to the identified non-overlapping region (see again ¶[0068]; iteratively keep dividing the cluster with the largest population until the desired number of clusters is reached).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. AYDIN discloses clustering, where the largest cluster is divided until a desired number of clusters is reached. It would have been obvious to include the dividing clusters as taught by AYDIN in the system executing the method of SAWARKAR with the motivation to cluster products for demand learning.
Claim 17 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the non-transitory computer readable medium as set forth in claim 16.
SAWARAKAR further discloses wherein each of the plurality of clusters is associated to a unique non- overlapping region of the plurality of non-overlapping regions (see claim 10; segmenting, by a processor, one or more products into a complementary product cluster or a competitive product cluster based on a demand history data of the one or more products and attributes of the one or more products).
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN does not specifically disclose, but AYDIN discloses, wherein the mapping of the re-scaled cumulative product consumption data with respect to the plurality of non-overlapping regions in the predefined cluster determination plot comprises: identifying a non-overlapping region from the plurality of non- overlapping regions in which a maximum number of data points from amongst at least three data points of the re-scaled cumulative product consumption data for the respective product lie (see ¶[0068]; iteratively keep dividing the cluster with the largest population until the desired number of clusters is reached); and wherein the clustering comprises: incorporating the re-scaled cumulative product consumption data in the cluster associated to the identified non-overlapping region (see again ¶[0068]; iteratively keep dividing the cluster with the largest population until the desired number of clusters is reached).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. AYDIN discloses clustering, where the largest cluster is divided until a desired number of clusters is reached. It would have been obvious to include the dividing clusters as taught by AYDIN in the system executing the method of SAWARKAR with the motivation to cluster products for demand learning.
Claim(s) 13, 14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al. in view of US 20230316219 A1 to AGRAWAL et al., US 20220043691 A1 to WILLEMAIN et al., US 20200057918 A1 to SHIN et al., and US 20090327163 A1 to SWAN et al. as applied to claim 9 above, and further in view of US 20120215574 A1 to DRIESSNACK et al.
Claim 13 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the system as set forth in claim 9.
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN does not explicitly disclose, but DRIESSNACK discloses, wherein to normalize the varying cumulative product consumption quantities in the first predefined numerical range, the processor is to: divide each of the varying cumulative product consumption quantities by a maximum value of the varying cumulative product consumption quantities (see ¶[0449]; totals for each discipline transparency score are tabulated (Table 18) and then normalized by dividing by the maximum score (i.e., by 10);; and
wherein to normalize the varying unit product values in the second predefined numerical range, the processor is to: divide each of the varying unit product values by a maximum value of the varying unit product values (see again ¶[0449]; totals for each discipline transparency score are tabulated (Table 18) and then normalized by dividing by the maximum score (i.e., by 10)).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. DRIESSNACK discloses normalizing scores based dividing by the maximum score. It would have been obvious to normalize scores as taught by DRIESSNACK in the system executing the method with the motivation to normalize values on product demand cluster plots.
Claim 14 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, SWAN, and DRIESSNACK discloses the system as set forth in claim 13.
SAWARKAR further discloses wherein the plurality of clusters comprises five clusters (see ¶[0067]; According to FIG. 3 a macro-customer segmentation model is created 301 for k=alpha (a), which segments the (potentially incomplete) continuous data space into a number of macro clusters),
wherein a first cluster of the five clusters is associated with a first unique non- overlapping region confined by the cumulative product consumption quantity in a range 0 to 0.4 and the unit product value in a range 0 to 0.2 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a second cluster of the five clusters is associated with a second unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.6 and the unit product value in a range 0.2 to 0.4 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a third cluster of the five clusters is associated with a third unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.8 and the unit product value in a range 0.4 to 0.6 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a fourth cluster of the five clusters is associated with a fourth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.4 to 0.8 and the unit product value in a range 0.6 to 0.8 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation), and
wherein a fifth cluster of the five clusters is associated with a fifth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.6 to 1 and the unit product value in a range 0.8 to 1 (see ¶[0079]; each cluster has a distinct demand range and characteristics. Examiner Note: the effect of multiple clusters in multiple demand ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
Claim 18 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the non-transitory computer readable medium as set forth in claim 16.
The combination of SAWARKAR, WILLEMAIN, SHIN, and SWAN does not specifically disclose, but DRIESSNACK discloses, wherein normalizing the varying cumulative product consumption quantities in the first predefined numerical range comprises: dividing each of the varying cumulative product consumption quantities by a maximum value of the varying cumulative product consumption quantities (see ¶[0449]; totals for each discipline transparency score are tabulated (Table 18) and then normalized by dividing by the maximum score (i.e., by 10)); and
wherein normalizing the varying unit product values in the second predefined numerical range comprises: dividing each of the varying unit product values by a maximum value of the varying unit product values (see again ¶[0449]; totals for each discipline transparency score are tabulated (Table 18) and then normalized by dividing by the maximum score (i.e., by 10)).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. DRIESSNACK discloses normalizing scores based dividing by the maximum score. It would have been obvious to normalize scores as taught by DRIESSNACK in the system executing the method with the motivation to normalize values on product demand cluster plots.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al. in view of US 20230316219 A1 to AGRAWAL et al., , US 20220043691 A1 to WILLEMAIN et al., US 20200057918 A1 to SHIN et al., and US 20090327163 A1 to SWAN et al. as applied to claim 16 above, and further in view of US 20210304233 A1 to JAIN et al.
Claim 20 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN discloses the non-transitory computer readable medium as set forth in claim 16.
SAWARKAR does not specifically disclose, but SWAN discloses, wherein the first predefined numerical range is from 0 to 1,wherein the second predefined numerical range is from 0 to 1 (see ¶[0223]; scale prices or costs between zero and one).
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, and SWAN does not explicitly disclose, but JAIN discloses wherein the plurality of clusters comprises five clusters, wherein a first cluster of the five clusters is associated with a first unique non- overlapping region confined by the cumulative product consumption quantity in a range 0 to 0.2 and the unit product value in a range 0 to 0.2 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a second cluster of the five clusters is associated with a second unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.4 and the unit product value in a range 0.2 to 0.4 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a third cluster of the five clusters is associated with a third unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.4 to 0.6 and the unit product value in a range 0.4 to 0.6 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a fourth cluster of the five clusters is associated with a fourth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.6 to 0.8 and the unit product value in a range 0.6 to 0.8 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation), and
wherein a fifth cluster of the five clusters is associated with a fifth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.8 to 1 and the unit product value in a range 0.8 to 1 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. WILLEMAIN discloses demand profiles of product clusters that includes normalizing demand plots on a scale of zero to one. SWAN discloses a choice engine that includes normalizing price on a plot between one and zero. It would have been obvious to normalize price as taught by SWAN in the system executing the method of SAWARAKAR and WILLEMAIN with the motivation to provide an appropriate scale for analysis (see SWAN ¶[0222]).
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. JAIN discloses product clusters based on price. It would have been obvious to define product clusters based on price as taught by JAIN in the system executing the method of SAWRAKAR with the motivation to predict demand of products (see Jain ¶[0002]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220138786 A1 to SAWARKAR et al., US 20220043691 A1 to WILLEMAIN et al., US 20200057918 A1 to SHIN et al., US 20090327163 A1 to SWAN et al., and US 20120215574 A1 to DRIESSNACK et al. as applied to claim 13 above, and further in view of US 20120215574 A1 to DRIESSNACK et al. and further in view of US 20210304233 A1 to JAIN et al.
Claim 15 (Original)
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, SWAN, and DRIESSNACK discloses the system as set forth in claim 13.
The combination of SAWARKAR, AGRAWAL, WILLEMAIN, SHIN, SWAN, and DRIESSNACK does not specifically disclose, but JAIN discloses, wherein the plurality of clusters comprises five clusters, wherein a first cluster of the five clusters is associated with a first unique non- overlapping region confined by the cumulative product consumption quantity in a range 0 to 0.2 and the unit product value in a range 0 to 0.2 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a second cluster of the five clusters is associated with a second unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.2 to 0.4 and the unit product value in a range 0.2 to 0.4 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a third cluster of the five clusters is associated with a third unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.4 to 0.6 and the unit product value in a range 0.4 to 0.6 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
wherein a fourth cluster of the five clusters is associated with a fourth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.6 to 0.8 and the unit product value in a range 0.6 to 0.8 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation), and
wherein a fifth cluster of the five clusters is associated with a fifth unique non-overlapping region confined by the cumulative product consumption quantity in a range 0.8 to 1 and the unit product value in a range 0.8 to 1 (see ¶[0038]; clusters the plurality of products at the store level based on price-range. Examiner Note: the effect of multiple clusters in multiple price ranges from 0 to 1 would be any number of clusters with ranges between 0 and 1, meeting the claim limitation),
SAWARAKAR discloses an automated demand learning module that that clusters products based on price demand and product categories. JAIN discloses product clusters based on price. It would have been obvious to define product clusters based on price as taught by JAIN in the system executing the method of SAWRAKAR with the motivation to predict demand of products (see Jain ¶[0002]).
Conclusion
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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