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 .
DETAILED ACTION
1. This Office Action is in response to the communication filed on May 7, 2025, on which paper has been placed of record in the file.
2. Claims 1-20 are pending in this application.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted May 9, 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
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 1, which is analyzed as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a system for comprehensive segment analysis. Thus, the claim is to be a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites a system for comprehensive segment analysis. The Specification, para [0003] described that “Segmentation refers to the process of dividing one or more target markets into sub-sections, or segments, that can be targeted with specific products, communications and communication channels, supply chain logistical procedures, and/or other business processes. Segmentation is a complex task when performed manually, so dynamic segmentation may be employed to speed up the process of segmentation.” The claim recites the steps: select an algorithm with which to perform auto-segmentation; in response to receiving a selection, autonomously perform multi-dimensional segmentation to compute a number of segments; in response to receiving a specified number of segments, generate an initial segmentation configuration using the specified number of segments; perform multi-dimensional segment visualization based on the received selection or based on the received specified number of segments; assign segments to item and product intersections; compute a relative importance of one or more features; and drop any feature associated with a relative importance score lower than a defined threshold, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising marketing or sales activities or behaviors, business relations. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps of: select an algorithm with which to perform auto-segmentation; in response to receiving a selection, autonomously perform multi-dimensional segmentation to compute a number of segments; in response to receiving a specified number of segments, generate an initial segmentation configuration using the specified number of segments; perform multi-dimensional segment visualization based on the received selection or based on the received specified number of segments; assign segments to item and product intersections; compute a relative importance of one or more features; and drop any feature associated with a relative importance score lower than a defined threshold, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “a segmentation planner comprising a computer and a database, the computer comprising a memory and a processor”; “an inventory system”; “a transportation network”; and “retain and store the assigned segments, the item and product intersections and any feature which has not been dropped.”
The additional elements “retain and store the assigned segments, the item and product intersections and any feature which has not been dropped”, are mere data gathering, transmitting, and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, transmitting, and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, transmitting and outputting. See MPEP 2106.05. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Further, the steps of “select an algorithm …; in response to receiving a selection, autonomously perform multi-dimensional segmentation to compute a number of segments; in response to receiving a specified number of segments, generate an initial segmentation…; perform multi-dimensional segment visualization…; assign segments to item and product intersections; compute a relative importance of one or more features; drop any feature associated with a relative importance score lower than a defined threshold; and retain and store the assigned segments, the item and product intersections and any feature which has not been dropped”, are recited as being performed by the processor and a memory. The processor and the memory are recited at a high level of generality. In the limitations “retain and store the assigned segments, the item and product intersections and any feature which has not been dropped”, the processor is used as a tool to perform the generic computer function of gathering and outputting data. See MPEP 2106.05(f). In limitations “select an algorithm …; in response to receiving a selection, autonomously perform multi-dimensional segmentation to compute a number of segments; in response to receiving a specified number of segments, generate an initial segmentation…; perform multi-dimensional segment visualization…; assign segments to item and product intersections; compute a relative importance of one or more features; drop any feature associated with a relative importance score lower than a defined threshold”, the processor is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processor, a memory, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the processor, the memory, the inventory system, the transportation network, they are just merely used as general means for collecting, displaying data and performing the abstract idea.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The additional elements “retain and store the assigned segments, the item and product intersections and any feature which has not been dropped” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional elements of “retain and store the assigned segments, the item and product intersections and any feature which has not been dropped” are recited at a high level of generality. These elements amount to gathering and displaying data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
As discussed in Step 2A, Prong Two above, the recitation of the processor and a memory to perform limitations “select an algorithm …; in response to receiving a selection, autonomously perform multi-dimensional segmentation to compute a number of segments; in response to receiving a specified number of segments, generate an initial segmentation…; perform multi-dimensional segment visualization…; assign segments to item and product intersections; compute a relative importance of one or more features; drop any feature associated with a relative importance score lower than a defined threshold; and retain and store the assigned segments, the item and product intersections and any feature which has not been dropped”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 8 and 15, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 8 directed to a method, independent claim 15 directed to a medium, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent apparatus claim 1.
Regarding dependent claims 2-7, 9-14, and 16-20, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claims 2, 9, and 16, the claims simply refine the abstract idea by further reciting wherein the algorithm is selected based on whether data stored in pre-processed data is string-based or numerical-based, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 3, 10, and 17, the claims simply refine the abstract idea by further reciting wherein the computer is further configured to: assign segments to the one or more features, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 4, 11, and 18, the claims simply refine the abstract idea by further reciting wherein the relative importance score is computed based on a boundary analysis of how each feature participates in interacting with one or more segment, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 5, 12, and 19, the claims simply refine the abstract idea by further reciting wherein the one or more features comprise one or more of: orders, forecast volume, average demand interval and coefficient of variation, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 6, 13, and 20, the claims simply refine the abstract idea by further reciting in response to one or more features being dropped, repeat the receiving a selection., that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 7, 14, and 20, the claims simply refine the abstract idea by further reciting in response to one or more features being dropped, repeat the receiving a specified number of segments., that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claim Rejections - 35 USC § 102
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
7. Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lillo (US 2022/0284261).
Regarding claim 1, Lillo discloses a system for comprehensive segment analysis, comprising:
a supply chain network comprising a segmentation planner, an inventory system, a transportation network and one or more supply chain entities, the segmentation planner further comprising a computer and a database, the computer comprising a memory and a processor (figure 12 and para [0146], the computing system 1200 may include: one or more computer processors 1202, such as physical central processing units (“CPUs”) or graphics processing units (“GPUs”); one or more network interfaces 1204, such as a network interface cards (“NICs”); one or more computer readable medium drives 1206),, the computer configured to:
select an algorithm with which to perform auto-segmentation (para [0108], For example, the feature vectors and associated errors may be analyzed using an automated clustering algorithm. The number of clusters may be preconfigured or determined dynamically as part of algorithm execution);
in response to receiving a selection, autonomously perform multi-dimensional segmentation to compute a number of segments (para [0059], a distribution may be modeled as one or more clusters using a clustering algorithm. In clustering, a data set is partitioned into different clusters in which each data point belongs to one of the clusters such that the data points are separated into clusters of “similar” data points. By modeling an entire data set as a collection of individual subsets represented by individual clusters, complex data sets with irregular distributions may be modeled. In the present example, the feature space in which the model is configured to operate may be modeled as sets of different clusters corresponding to TPs, TNs, FPs, and FNs);
in response to receiving a specified number of segments, generate an initial segmentation configuration using the specified number of segments (para [0059], One example method for determining the clusters of similar feature vectors 114 may be implemented using the k-means clustering algorithm as follows: make initial value assignments to the mean values (or “centroids”) of each of the k individual clusters, where k is a positive integer; compute the Euclidian distance between each point in the data set and each of the k clusters; assign each point in the data set to the cluster to which it is the closest; calculate new means of the data points assigned to each cluster; and iteratively repeat the distance calculation, assignment, and means calculation steps until a convergence criterion has been met. This process may be performed separately for each of the four categories (TPs, TNs, FPs, and FNs) and/or a single clustering process may be performed for all feature vectors);
perform multi-dimensional segment visualization based on the received selection or based on the received specified number of segments (para [0060], FIG. 3B is a diagram of an illustrative distribution 350 of points in a two-dimensional feature space as modeled using k-means clustering. In the illustrated example, there are two separate centroids 352 and 354. As shown, there is a hard separation 360 in the distribution 350. Thus, individual data points are each assigned to only one cluster with one centroid 352 or 354, respectively, depending upon the distance of the individual data points from the centroids 352, 354. The distribution 350 is shown in two-dimensional form for purposes of illustration only. In some embodiments, the feature space modeled by the clusters may have 1, 2, 3, or more dimensions, and thus the points in the feature space represented by feature vectors may also have 1, 2, 3, or more dimensions);
assign segments to item and product intersections (para [0128], the particular distributions modeled by the mixture density functions may be based on subsets of all training data that would otherwise be included in the respective data sets. Rather than using a data set of all TP, TN, FP, and/or FN classifications and associated feature space points, a subset thereof may be selected based on one or more attributes. The attributes may be attributes of the input vectors from which the feature vectors and classification determinations are based, such as particular values of particular elements of the vectors. For example, an input vector may represent an item or event with a number of different properties);
compute a relative importance of one or more features (para [0019], Some conventional machine learning models are configured and trained to produce classification scores that reflect the likelihood or “confidence” that a particular input is properly classified or not classified in a particular classification. For example, input may be analyzed using a machine learning model, and the output of the analysis for a particular classification may be a classification score in the range [0.0, 1.0]. A higher score indicates a higher probability or confidence that the input is properly classified in the particular classification, and a lower score indicates a lower probability or confidence that the input is properly classified in the particular classification);
drop any feature associated with a relative importance score lower than a defined threshold (para [0235], wherein the feature space point is included in the distribution of feature space points based on the feature space point being associated with one of: an error value having a magnitude that is less than a threshold, a positive error value having a magnitude greater than the threshold, or a negative error value having a magnitude greater than the threshold) ; and
retain and store the assigned segments, the item and product intersections and any feature which has not been dropped (para [0143], A training data store 1120 may be used to store training data. Training data may include data from which a machine learning model can be trained, and from which training-supported-based distributions, metrics, or the like may be generated. In some embodiments, the training data store 1120 may be local to the analysis system 1102, may be remote from the analysis system 1102, and/or may be a network-based service itself ).
Regarding claim 2, Lillo discloses the system of Claim 1, wherein the algorithm is selected based on whether data stored in pre-processed data is string-based or numerical-based (para [0059], One example method for determining the clusters of similar feature vectors 114 may be implemented using the k-means clustering algorithm as follows: make initial value assignments to the mean values (or “centroids”) of each of the k individual clusters, where k is a positive integer).
Regarding claim 3, Lillo discloses the system of Claim 1, wherein the computer is further configured to: assign segments to the one or more features (para [0059], compute the Euclidian distance between each point in the data set and each of the k clusters; assign each point in the data set to the cluster to which it is the closest; calculate new means of the data points assigned to each cluster).
Regarding claim 4, Lillo discloses the system of Claim 1, wherein the relative importance score is computed based on a boundary analysis of how each feature participates in interacting with one or more segments (para [0070], At block 508, the computing system 1200 can generate initial classification data 422 from the feature vector 412. In some embodiments, the initial classification data 422 generated from the current feature vector 412 may be an initial classification score denoted C1(x), where x is the feature space point represented by the current feature vector 412. Illustratively, the initial classification score C1(x) may be a score in a range between two endpoints, where one endpoint indicates the highest confidence that the feature space point x is properly classified in a particular class, and the other endpoint indicates the lowest confidence that the feature space point x is properly classified in the particular class).
Regarding claim 5, Lillo discloses the system of Claim 1, wherein the one or more features comprise one or more of: orders, forecast volume, average demand interval and coefficient of variation (para [0136], In this example, if delivery of materials that the manufacturer takes as inputs has slowed in a way never seen in the training data, or has slowed in a way seen infrequently enough to be poorly trained, the model's predicted delivery speed may indicate a false nominal time. Such an inaccurate predication may have widespread consequences to downstream agents in a supply chain).
Regarding claim 6, Lillo discloses the system of Claim 1, wherein the computer is further configured to: in response to one or more features being dropped, repeat the receiving a selection (para [0059], calculate new means of the data points assigned to each cluster; and iteratively repeat the distance calculation, assignment, and means calculation steps until a convergence criterion has been met. This process may be performed separately for each of the four categories (TPs, TNs, FPs, and FNs) and/or a single clustering process may be performed for all feature vectors).
Regarding claim 7, Lillo discloses the system of Claim 1, wherein the computer is further configured to: in response to one or more features being dropped, repeat the receiving a specified number of segments (para [0059], calculate new means of the data points assigned to each cluster; and iteratively repeat the distance calculation, assignment, and means calculation steps until a convergence criterion has been met. This process may be performed separately for each of the four categories (TPs, TNs, FPs, and FNs) and/or a single clustering process may be performed for all feature vectors).
Claims 8-14 are written in method and contain the same limitations described in claims 1-7 above, therefore, are rejected by the same rationale.
Claims 15-20 are written in method and contain the same limitations described in claims 1-7 above, therefore, are rejected by the same rationale.
Conclusion
8. Claims 1-20 are rejected.
9. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Kumar et al. (US 2024/0220515) disclose the supply chain management interfaces may provide visualizations of one or more standardization constructs associated with an organization and locations thereof.
Yesudas et al. (US 2022/0343244) disclose a process across a plurality of systems associated with a supply chain. A context is determined for an order, where at least one of the features of the context includes information from a networked sensing device. One or more first machine learning models identify a plurality of orders of an order history having features corresponding to the order.
Farooq (US 2022/0327439) discloses a method of forecasting raw material demand. The method comprises retrieving news articles related to raw material suppliers as well as raw material industry articles related to specified raw materials.
Devarakonda et al. (Us 2020/0210947) disclose methods and systems for controlling inventory in a supply chain. The system receives supply chain data including input signals comprising operational plans and observed supply chain operational metrics.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 September 3, 2026
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