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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—the specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation “select a representative sample from the full data population”. There is insufficient antecedent basis for “the full data population” in the claim.
Claims 2-7 depend from claim 1 and do not cure the noted deficiency. Therefore, they are also rejected under 112(b).
Claims 1, 8, and 15 recite the limitation “assign segments from the initial segmentation configuration to intersections” which renders the claim indefinite because the claim never established what an “intersection” is.
Claims 1, 8, and 15 recite the limitation “predict segments for one or more remaining segment intersections” which renders the claim indefinite because the claim never established what a “segment intersection” is.
Claims 2-7, 9-14, and 16-20 depend from claim 1 and do not cure the noted deficiency. Therefore, they are also rejected under 112(b).
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 agile segment analysis select an algorithm with which to perform autonomous multi-dimensional segmentation; select a representative sample from the full data population; 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 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 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; drop any of the one or more features with a relative importance score below a threshold; retain the assigned segments, any of the one or more features that have not been dropped, and the intersections; train one or more supervised machine learning models using the initial segmentation configuration; predict segments for one or more remaining segment intersections using the one or more supervised machine learning models; and assign the predicted segments to the intersections”
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 comprising: a computer, the computer comprising a memory and a processor, the computer configured to:”, “GUI”. 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 comprising: a computer, the computer comprising a memory and a processor, the computer configured to:”, “GUI”. 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, 3-6, 8, 10-13, 15, and 17-20 are rejected under 35 U.S.C. 103 as being un-patentable over Najmi (US11397957) in view of Heckerman (US6529891B1), in view of Kumaresan et al. (US20210027503), in view of Wood et al. (US9514213B2), and in further view of Wick et al. (US11544724 B1).
Regarding claim 1. A system for performing agile 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; select a representative sample from the full data population; 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; drop any of the one or more features with a relative importance score below a threshold; retain the assigned segments, any of the one or more features that have not been dropped, and the intersections; train one or more supervised machine learning models using the initial segmentation configuration; predict segments for one or more remaining segment intersections using the one or more supervised machine learning models; and assign the predicted segments to the intersections.
Najmi teaches a segmentation planner that organizes data into multidimensional segments and intersections using rules or clustering algorithms. Each intersection of segments constitutes a micro-segment, and the micro-segments can themselves be grouped using clustering algorithms, Column 5, lines 18-25. Najmi teaches that each intersection of dimensions constitutes a micro-segment and that a segmentation planner automatically populates and displays those intersections in a GUI grid Column 11, lines 29-47.
Najmi does not specifically teach, however; Heckerman teaches perform segmentation and compute a number of segments autonomously, see Abstract.
Najmi in view of Heckerman does not specifically teach, however; Kumaresan teaches select a representative sample from the full data population, see claim 1 in addition to the Abstract.
Najmi in view of Heckerman and Kumaresan does not specifically teach, however; Wood teaches performing feature selection for clustering by removing attributes, recomputing clusters, and determining whether the removed attributes materially affect the clustering result. If not, the removed attributes are considered unimportant, column 18 lines 43-68
Najmi in view of Heckerman, Kumaresan, and Wood does not specifically teach, however; Wick teaches supervised ML, multidimensional features and feature-importance determination. Wick teaches explainable supervised machine learning using multidimensional predictive features. For example, its disclosed features include store, item, and the two-dimensional item-store feature, with the model trained on historical sales data and used to make predictions, see Abstract.
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 utilizing the segmentation planner of Najmi with the segmentation and computing a number of segments autonomously of Heckerman, the selection of a representative sample of Kumaresan, the performing feature selection for clustering by removing attributes of Wood, and the supervised ML and multidimensional features and feature-importance determination of Wick 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 segment prediction using machine learning.
Regarding claim 3. Najmi in view of Heckerman, Kumaresan, Wood, and Wick teaches all of the limitations of claim 1 (as above). Further, Najmi Teaches wherein the intersections comprise item and product intersections. Najmi Teaches multidimensional intersections and gives product as one of the segmentation dimensions. Each intersection of dimensions is a micro-segment, see figure 4 and column 8, lines 10-31.
Regarding claims 4-5. Najmi in view of Heckerman, Kumaresan, Wood, and Wick teaches all of the limitations of claim 1 (as above). Further, Najmi Teaches wherein the GUI display visualizing the initial segmentation configuration comprises a graph of the assigned segments and the one or more features, 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.
Najmi teaches segmentation planner includes a micro-segment grid chart in which dimensions are placed along rows and columns and the intersections are automatically populated. It also automatically updates graphical representations as group assignments change see figure 5G and column 13, lines 43-52.
Regarding claim 6. Najmi in view of Heckerman, Kumaresan, Wood, and Wick teaches all of the limitations of claim 1 (as above). Najmi in view of Heckerman, Kumaresan, and Wick does not specifically teach, however; Wood teaches wherein the relative importance score for each of the one or more features are computed using a boundary analysis. Wood teaches determines feature importance in clustering by removing attributes and measuring changes in cluster assignments column 19, lines 9-27 “Tri-point arbitration can be used to perform feature selection for similarity measurements and clustering. For example, a first similarity matrix can be computed and then a set of attributes can be removed. A second similarity matrix is computed without the set of attributes. If there are no significant changes, it can be inferred that the removed attributes are not important for the calculations. The same computations can be made for clusters by using tri-point clustering to create clusters, remove a set of attributes, recompute the clusters, and determine if there are any changes. If there are no changes, it can be inferred that the removed attributes are not important for clustering purpose. If there are a small number of changes, an error metric can be used to determine if the changes are within a threshold that is acceptable given the dimensionality reduction achieved”.
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 utilizing the segmentation planner of Najmi with the segmentation and computing a number of segments autonomously of Heckerman, the selection of a representative sample of Kumaresan, the performing feature selection for clustering by removing attributes of Wood, the supervised ML and multidimensional features and feature-importance determination of Wick with the determines feature importance in clustering by removing attributes and measuring changes in cluster assignments of Wood 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 segment prediction using machine learning.
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 Najmi claim 7.
Regarding claims 10-13, claims 10-13 recite substantially similar limitations as claim 3-6, respectively; therefore, claims 10-13 are rejected with the same rationale, reasoning, and motivation provided above for claims 3-6, respectively. Claims 3-6 are system claims while claims 10-13 are directed to a method which is anticipated by Najmi claim 7.
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 Najmi claim 14.
Regarding claims 17-20, claims 17-20 recite substantially similar limitations as claim 3-6, respectively; therefore, claims 17-20 are rejected with the same rationale, reasoning, and motivation provided above for claims 3-6, respectively. Claims 3-6 are system claims while claims 17-20 are directed to a non-transitory computer-readable medium which is anticipated by Najmi claim 14.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being un-patentable over Najmi in view of Heckerman, in view of Kumaresan, in view of Wood, in view of Wick, and in view of Acharya et al. (US7844557B2)
Regarding claim 2. Najmi in view of Heckerman, Kumaresan, Wood, and Wick teaches all of the limitations of claim 1 (as above). Najmi in view of Heckerman, Kumaresan, Wood, and Wick does not specifically teach, however; Acharya teaches wherein the algorithm is selected based on whether data stored in the pre-processed data is string-based or numerical-based.
Acharya teaches numerical datasets from categorical datasets and explains that algorithms suited to numerical data may not be appropriate for categorical data because categorical values lack the numerical similarity metric/centroid used by conventional partitional clustering, column 1, lines 45-59
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 utilizing the segmentation planner of Najmi with the segmentation and computing a number of segments autonomously of Heckerman, the selection of a representative sample of Kumaresan, the performing feature selection for clustering by removing attributes of Wood, the supervised ML and multidimensional features and feature-importance determination of Wick with the algorithm selection of Acharya 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 segment prediction using machine learning.
Regarding claim 9, the claim recites analogous limitations to claim 2 above and is therefore rejected on the same premise. Claim 2 is a system claim while claim 9 is directed to a method which is anticipated by Najmi claim 7.
Regarding claim 16, the claim recites analogous limitations to claim 2 above and is therefore rejected on the same premises. Claim 2 is a system claim while claim 16 is directed to a non-transitory computer-readable medium which is anticipated by Najmi claim 14.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being un-patentable over Najmi in view of Heckerman, in view of Kumaresan, in view of Wood, in view of Wick, and in view of Olkin et al. (US 20170161249 A1).
Regarding claim 7. Najmi in view of Heckerman, Kumaresan, Wood, and Wick teaches all of the limitations of claim 1 (as above). Najmi in view of Heckerman, Kumaresan, Wood, and Wick 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 utilizing the segmentation planner of Najmi with the segmentation and computing a number of segments autonomously of Heckerman, the selection of a representative sample of Kumaresan, the performing feature selection for clustering by removing attributes of Wood, the supervised ML and multidimensional features and feature-importance determination of Wick 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.
Regarding claim 14, the claim recites analogous limitations to claim 7 above and is therefore rejected on the same premise. Claim 7 is a system claim while claim 14 is directed to a method which is anticipated by Najmi claim 7.
Conclusion
The following prior arts made of record and not relied upon are considered pertinent to applicant's disclosure. Hong Jia et al. “Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number”, Science Direct, August 2013. Hong teaches: 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. Accordingly, an iterative clustering algorithm is developed, whose outstanding performance is experimentally demonstrated on different benchmark datasets. Moreover, to circumvent the difficult selection problem of cluster number, we further develop a penalized competitive learning algorithm within the proposed clustering framework. The embedded competition and penalization mechanisms enable this improved algorithm to determine the number of clusters automatically by gradually eliminating the redundant clusters. The experimental results show the efficacy of the proposed approach.
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.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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