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 .
Status of the Application
This action is a first action on the merits in response to the application filed on 08/22/2025.
Status of Claims
Claims 1-20 filed on 08/22/2025 are currently pending and have been examined in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/25/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-20 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of supply chain product segmentation.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “performing comprehensive segment analysis comprising: select an algorithm with which to perform autonomous multi-dimensional segmentation; in response to receiving a selection to perform the autonomous multi-dimensional segmentation: perform the autonomous multi-dimensional segmentation and compute a number of segments autonomously; store the autonomously computed number of segments; and generate one or more GUI displays visualizing the autonomously computed number of segments; in response to receiving a selection to receive a specified number of segments: receive data for the specified number of segments; and access the specified number of segments data and generate an initial segmentation configuration using the specified number of segments data; generate a GUI display visualizing an initial segmentation configuration; access the initial segmentation configuration and pre-processed data, and assign segments from the initial segmentation configuration to intersections; compute a relative importance score for each of one or more features associated with the assigned segments; and drop any of the one or more features with a relative importance score below a threshold.”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite Mental Process because an ordinary person can reasonably perform the claimed product segmentation using pen and paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 8 and 15 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 8 and 15 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-7, 9-14, and 16-20 recite a Mental Process because the claimed elements describe a process for supply chain product segmentation. As a result, claims 2-7, 9-14, and 16-20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “A system for” and “comprising: a computer, the computer comprising a memory and a processor, the computer configured to:”. When considered in view of the claim as a whole, the step of “receiving” does not integrate the abstract idea into a practical application because “receiving” is an insignificant extra solution activity to the judicial exception. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Therefore, the claim is directed to an abstract idea.
As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 8 and 15 recite substantially similar limitations to those recited with respect to claim 1. Although claim 8 further recites “A computer-implemented method” and claim 15 further recites “A non-transitory computer-readable medium”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 8 and 15 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-7, 9-14, and 16-20 do not include any additional elements beyond those recited by independent claims 1, 8, and 15. As a result, claims 2-7, 9-14, and 16-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “A system for” and “comprising: a computer, the computer comprising a memory and a processor, the computer configured to:”. The step of “receiving” does not amount to significantly more than the abstract idea because “receiving” is well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 8 and 15 recite substantially similar limitations to those recited with respect to claim 1. Although claim 8 further recites “A computer-implemented method” and claim 15 further recites “A non-transitory computer-readable medium”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 10 and 19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-7, 9-14, and 16-20 do not include any additional elements beyond those recited by independent claims 1, 8 and 15. As a result, claims 2-7, 9-14, and 16-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-6, 8-13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being un-patentable over Hong Jia et al. “Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number”, Science Direct, August 2013, in view of Lawrence J. Choi et al. (US7398270B1), in further view of Chen et al. (US 20110035379 A1) and Elad Liebman (US 10706323 B1)
Regarding claim 1. Jia teaches A system for performing comprehensive segment analysis, comprising: a computer, the computer comprising a memory and a processor, the computer configured to: select an algorithm with which to perform autonomous multi-dimensional segmentation; [Jia, Abstract, Jia teaches “Accordingly, an iterative clustering algorithm is developed, whose outstanding performance is experimentally demonstrated on different benchmark datasets” wherein selecting an algorithm] in response to receiving a selection to perform the autonomous multi-dimensional segmentation: perform the autonomous multi-dimensional segmentation and compute a number of segments autonomously; [Jia, Abstract, Jia teaches “The embedded competition and penalization mechanisms enable this improved algorithm to determine the number of clusters automatically by gradually eliminating the redundant clusters” wherein compute a number of segments]
Jia does not specifically teach, however, Choi teaches store the autonomously computed number of segments; and generate one or more GUI displays visualizing the autonomously computed number of segments; in response to receiving a selection to receive a specified number of segments: receive data for the specified number of segments; and access the specified number of segments data and generate an initial segmentation configuration using the specified number of segments data; generate a GUI display visualizing an initial segmentation configuration; [Choi, See B. Impact/output, Choi teaches “Bestfit clustering generates a segmentation solution that within the specified constraints maximizes “fi”. These constrains are number of segments…Presence of an initial segmentation solution to be optimized…” wherein Choi deals with segmentation solutions developed using a specific number of segments and optimization of a segmentation solution. Also, activity 10020 of figure 10 teaches cluster identifications are stored]
Jia in view of Choi does not specifically teach, however, Chen teaches access the initial segmentation configuration and pre-processed data, and assign segments from the initial segmentation configuration to intersections; [Chen, para. 0024, Chen teaches “It may be useful, therefore, to map each item (e.g., each item identifier or item ID) to a more general product (e.g., a product identifier or product ID), for example, to derive a map F: I.fwdarw.P, where I is the space of possible item features, and P is the set of product labels. The resulting mapping operation is accordingly a clustering operation. In many similar situations, such as recommendations of books or movies, targeted advertising, or text clustering, a projection map G would be applied from I to a lower dimensional space before clustering is applied. However, it is also possible to apply model-based clustering directly to the item data, such as a "bag-of-words" feature vector for title text” wherein item to product and product to item mapping of data record to resulting clusters]
Jia in view of Choi and Chen does not specifically teach, however, Liebman teaches compute a relative importance score for each of one or more features associated with the assigned segments; and drop any of the one or more features with a relative importance score below a threshold [Liebman, claim 1, Liebman teaches “determining a feature importance ranking for each pair of clusters of a plurality of clusters associated with data to generate a first plurality of feature importance rankings; determining a feature importance ranking between a particular data element of the data and each cluster of the plurality of clusters to generate a second plurality of feature importance rankings; determining a distance value associated with each pair of clusters of the plurality of clusters to generate a plurality of distance values” wherein Liebman teaches determining feature importance/relative importance from differences between clusters and ranking features]
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine multidimensional clustering of Jia with the segmentation using specific number of segment of Choi, with the item/product clustering and mapping of Chen, with determining feature importance and ranking of Liebman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of optimizing segmentation solution.
Regarding claim 2. Jia in view of Choi, Chen, and Liebman teaches all of the limitations of claim 1 (as above). Further Jia teaches wherein the algorithm is selected based on whether data stored in the pre-processed data is string-based or numerical-based [Jia, Abstract, Jia teaches “Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not the both. In general, it is a nontrivial task to perform clustering on mixed data composed of numerical and categorical attributes because there exists an awkward gap between the similarity metrics for categorical and numerical data. This paper therefore presents a general clustering framework based on the concept of object-cluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, and mixed attributes” wherein Jia addresses the problem of clustering categorial and numerical attributes and develops a framework applicable to both].
Regarding claim 3. Jia in view of Choi, Chen, and Liebman teaches all of the limitations of claim 1 (as above). Jia in view of Choi and Liebman does not specifically teach, however, Chen teaches wherein the intersections comprise item and product intersections [Chen, para. 0024, Chen teaches “It may be useful, therefore, to map each item (e.g., each item identifier or item ID) to a more general product (e.g., a product identifier or product ID), for example, to derive a map F: I.fwdarw.P, where I is the space of possible item features, and P is the set of product labels. The resulting mapping operation is accordingly a clustering operation. In many similar situations, such as recommendations of books or movies, targeted advertising, or text clustering, a projection map G would be applied from I to a lower dimensional space before clustering is applied. However, it is also possible to apply model-based clustering directly to the item data, such as a "bag-of-words" feature vector for title text” wherein item to product and product to item mapping and item to product association]
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine multidimensional clustering of Jia with the segmentation using specific number of segment of Choi, with the item/product clustering and mapping item to product level of Chen, with determining feature importance and ranking of Liebman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of optimizing segmentation solution..
Regarding claim 4. Jia in view of Choi, Chen, and Liebman teaches all of the limitations of claim 1 (as above). Jia in view of Choi and Chen does not specifically teach, however, Liebman teaches wherein the GUI display visualizing the initial segmentation configuration comprises a graph of the assigned segments and the one or more features [Liebman, Figure 1B, Liebman teaches clusters and features association wherein clusters are identified from multiple features dimensions]
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine multidimensional clustering of Jia with the segmentation using specific number of segment of Choi, with the item/product clustering and mapping of Chen, with clusters and features association of Liebman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of optimizing segmentation solution.
Regarding claim 5. Jia in view of Choi, Chen, and Liebman teaches all of the limitations of claim 1 (as above). Jia in view of Choi and Chen does not specifically teach, however, Liebman teaches wherein the computer is further configured to: generate a GUI display to visualize the one or more relative importance scores for the one or more features [Liebman, Abstract, Liebman teaches “determining a feature importance ranking for each pair of clusters of a plurality of clusters to generate a first plurality of feature importance rankings. The method further includes determining a feature importance ranking between a particular data element and each cluster to generate a second plurality of feature importance rankings. A distance value associated with each pair of clusters of the plurality of clusters is determined to generate a plurality of distance values, and a probability value associated with each data element is determined to generate a plurality of probability values” wherein ranking is equivalent to importance score. See also figure 1 B for display. Also see column 9 lines 28-33 “In a particular example, the display device 107 is configured to generate the graphical representation using the GUI 109. Certain examples of graphical representations of aspects of the weighted feature importance ranking data 150 are described further with reference to FIGS. 2A-2C.”]
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine multidimensional clustering of Jia with the segmentation using specific number of segment of Choi, with the item/product clustering and mapping of Chen, with the GUI display to visualize the one or more relative importance scores for the one or more features of Liebman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of optimizing segmentation solution.
Regarding claim 6. Jia in view of Choi, Chen, and Liebman teaches all of the limitations of claim 1 (as above). Jia in view of Choi and Chen does not specifically teach, however, Liebman teaches wherein the relative importance score for each of the one or more features are computed using a boundary analysis [Liebman, column 14 lines 33-44, Liebman teaches “The example of the method 300 of FIG. 3 illustrates that feature importance data can be weighted to improve usefulness of the feature importance data. For example, by weighting features that distinguish the particular data element 108 from the cluster C5 more than features that distinguish the particular data element 108 from the clusters C1, C2, C3, and C4, characteristics of the particular data element 108 can be “localized” to the neighboring environment of the particular data element 108 within a feature space, enabling identification of characteristics of the particular data element 108 contributing to classification of the particular data element 108 as an outlier” wherein Chen determines features importance based on which features distinguish one cluster form another]
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine multidimensional clustering of Jia with the segmentation using specific number of segment of Choi, with the item/product clustering and mapping of Chen, with the features are computed using a boundary analysis of Liebman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of optimizing segmentation solution.
Regarding claim 8, the claim recites analogous limitations to claim 1 above and is therefore rejected on the same premise. Claim 1 is a system claim while claim 8 is directed to a method which is anticipated by Jia introduction.
Regarding claims 9-13, claims 9-13 recite substantially similar limitations as claim 2-6, respectively; therefore, claims 9-13 are rejected with the same rationale, reasoning, and motivation provided above for claims 2-6, respectively. Claims 2-6 are system claims while claims 9-13 are directed to a method which is anticipated by Jia introduction.
Regarding claim 15, the claim recites analogous limitations to claim 1 above and is therefore rejected on the same premises. Claim 1 is a system claim while claim 15 is directed to a non-transitory computer-readable medium which is anticipated by Jia introduction.
Regarding claims 16-20, claims 16-20 recite substantially similar limitations as claim 2-6, respectively; therefore, claims 16-20 are rejected with the same rationale, reasoning, and motivation provided above for claims 2-6, respectively. Claims 2-6 are system claims while claims 16-20 are directed to a non-transitory computer-readable medium which is anticipated by Jia introduction.
Claims 7, 14, and 19 are rejected under 35 U.S.C. 103 as being un-patentable over Hong Jia et al. “Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number”, Science Direct, August 2013, in view of Lawrence J. Choi et al. (US7398270B1), in further view of Chen et al. (US 20110035379 A1), Elad Liebman (US 10706323 B1), and Olkin et al. (US 20170161249 A1)
Regarding claim 7. Jia in view of Choi, Chen, and Liebman teaches all of the limitations of claim 1 (as above). Jia in view of Choi, Chen, and Liebman does not specifically teach, however Olkin teaches wherein the computer is further configured to: standardize units of measure or currency for each of the one or more features [Olkin, para. 0032, Olkin teaches “The spreadsheet application determines a valid measure (95.50), a valid unit of measure (currency in U.S. dollars), and an operator (ROUNDUP) with an argument specifying the number of decimal digits to round to (0). In the event the unit of measure and the operator are compatible, the spreadsheet application determines the resulting measure by applying the operator to the measure. In the previous example, the operator (ROUNDUP) is compatible with the unit of measure (currency in U.S. dollars), and the operator is applied to the measure (95.50) to calculate a resulting measure (96). The spreadsheet application further determines the resulting unit of measure. In some embodiments, the resulting unit of measure for the ROUNDUP operator is the same unit of measure initially entered in the cell. In the example discussed, the resulting unit of measure is currency in U.S. dollars. In some embodiments, the resulting unit of measure is a default unit of measure. For example, a default unit of measure is currency in euros when the operator is performed on currency” wherein Olkin standardizing feature values to common units before performing the analysis]
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine multidimensional clustering of Jia with the segmentation using specific number of segment of Choi, with the item/product clustering and mapping of Chen, and determining feature importance and ranking of Liebman with the standardize units of measure or currency for each of the one or more features of Olkin since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of optimizing segmentation solution..
Conclusion
The following prior arts made of record and not relied upon are considered pertinent to applicant's disclosure. Zhdanov et al. (US 10489802 B1) teaches cluster each of the plurality of products into one or more of a plurality of clusters based on similarities among the determined plurality of demand patterns, determine, for the respective demand property, an average demand pattern for each cluster of the plurality of clusters, and refine each cluster's average demand pattern for the respective demand property; wherein a product of the plurality of products belongs to each of two or more clusters, each cluster corresponding to a respective one of the two or more demand properties.
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3734.
Information regarding the status of an application may be obtained from the patent application information retrieval (PAIR) system. Status information of published applications may be obtained from either private PAIR or public PAIR. Status information of unpublished applications is available through private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have any questions on access to the private PAIR system, contact the electronic business center (EBC) at (866) 271-9197 (toll-free). If you would like assistance from a USPTO customer service representative or access to the automated information system, call (800) 786-9199 (in US or Canada) or (571) 272-1000.
/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
Prosecution Insights