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 bulk product processing. 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 performing strategic segmentation. 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 a segmentation workflow depth and generate cleansed data; access the cleansed data…; pre-process features data to generate pre-processed data; perform multi-dimension segmentations on the pre-processed data and compute feature importance to generate segments; assign one or more policy parameters to the generated segments, 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 a segmentation workflow depth and generate cleansed data; access the cleansed data…; pre-process features data to generate pre-processed data; perform multi-dimension segmentations on the pre-processed data and compute feature importance to generate segments; assign one or more policy parameters to the generated segments, 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”, “generate one or more GUI displays to visualize the generated segments”, and “train one or more machine learning models to predict a tactical segmentations of new data and to predict segment intersections.”
The additional elements “generate one or more GUI displays to visualize the generated segments”, 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).
The additional elements “train one or more machine learning models to predict a tactical segmentation of new data and to predict segment intersections” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “train one or more machine learning models to predict a tactical segmentation of new data and to predict segment intersections” are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, this limitation only recites the outcome of “predict a tactical segmentation of new data and to predict segment intersections” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “train one or more machine learning models to predict a tactical segmentation of new data and to predict segment intersections” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “train one or more machine learning models to predict a tactical segmentation of new data and to predict segment intersections” limit the identified judicial exceptions “predict a tactical segmentation of new data and to predict segment intersections”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Further, the steps of “select a segmentation workflow depth and generate cleansed data; access the cleansed data…; pre-process features data to generate pre-processed data; perform multi-dimension segmentations on the pre-processed data and compute feature importance to generate segments; generate one or more GUI displays to visualize the generated segments; assign one or more policy parameters to the generated segments”, 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 “generate one or more GUI displays to visualize the generated segments”, 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 a segmentation workflow depth and generate cleansed data; access the cleansed data…; pre-process features data to generate pre-processed data; perform multi-dimension segmentations on the pre-processed data and compute feature importance to generate segments; assign one or more policy parameters to the generated segments”, 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, the machine learning, they are just merely used as general means for collecting, displaying data and performing an 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.
As explained with respect to Step 2A, Prong Two, the additional elements of “train one or more machine learning models to predict a tactical segmentation of new data and to predict segment intersections” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements “generate one or more GUI displays to visualize the generated segments” 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 “generate one or more GUI displays to visualize the generated segments” 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).
As discussed in Step 2A, Prong Two above, the recitation of the processor and a memory to perform limitations “select a segmentation workflow depth and generate cleansed data; access the cleansed data…; pre-process features data to generate pre-processed data; perform multi-dimension segmentations on the pre-processed data and compute feature importance to generate segments; assign one or more policy parameters to the generated segments”, 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 to select a segmentation workflow depth by: using a comprehensive scenario comprising using all data available and more extensive computation time…, 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 generating cleansed data comprises copying one or more pre-discovered features stored in input data into the cleansed data, 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 discovering features comprises aggregating or dis-aggregating the cleansed data based on one or more segment intersections, 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 aggregating or the disaggregating the cleansed data comprises using one or more direct input features or one or more derived 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 6, 13, and 20, the claims simply refine the abstract idea by further reciting wherein the one or more derived features are computed based on one or more other features stored in the cleansed data, 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 and 14, the claims recite the additional elements wherein at least one of the one or more machine learning models comprises a neural network, which are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, this limitation only recites the outcome of “predict a tactical segmentation of new data and to predict segment intersections” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). These additional elements also merely indicate a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exceptions “predict a tactical segmentation of new data and to predict segment intersections”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). (See claim 1 above). 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
7. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Oliveira Almeida et al. (hereinafter Oliveira Almeida, US 2020/00744370).
Regarding to claim 1, Oliveira Almeida discloses a system for performing strategic segmentation, 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 (para [0014], dynamic supply chain planning system comprising: a database comprising historical lead time data related to a supply chain; a machine learning server in communication with the database, the machine learning server comprising a machine learning service and a data preparation module, wherein: the machine learning server is in communication with the database; the data preparation module is in communication with the database to obtain the historical lead time data; the data preparation module processes the historical lead time data into a processed historical lead time dataset for use by the machine learning service; the machine learning service comprises at least one of a forecasting module and a clustering module; the forecasting module is configured to forecast future lead times of the supply chain based on the processed historical lead time data set; the clustering module is configured to provide a plurality of clusters based on pre-defined features; figure 12 and para 0149], The computer system 1200 includes processor(s) 1201, such as a central processing unit, application specific integrated circuit (ASIC) or other type of processing circuit; input/output devices 1202, such as a display, mouse keyboard, etc.; a network interface 1208, such as one or more interfaces for connecting to a Local Area Network (LAN), a wireless 802.11x LAN, a 7G or 4G mobile WAN or a WiMax WAN, or other type of network, and a computer readable medium 1204), the computer configured to autonomously:
select a segmentation workflow depth and generate cleansed data (para [0072]; A machine learning server (12) is integrated with a supply chain planning platform that comprises a supply chain planning application and data servers (30). The Machine learning service (16) may be triggered via a user interface (32) that is part of the supply chain planning platform. Once triggered, the machine learning service operates with the data preparation module (18), forecasting module (20) and clustering module (22), as described above (with reference to FIG. 2A). The forecasting results and clustering results are uploaded onto the database (14); then communicated to an analytics module (34), which then communicates the analyzed results to a user via the user interface (32); para [0082], Before analysis by machine learning, the data may be “cleansed” to remove data errors and outliers. Examples of data errors may include records with null values, duplicate records, negative lead times, etc.);
access the cleansed data to discover features (para [0073], The architecture 25 includes an application service integration and communication layer 15 that supports data collection from the data sources 17, and optionally other systems and applications (not shown). The layer 15 may also provide secure access with a user interface 32, which may allow users to log into the supply chain planning platform 30 to view data or perform other operations);
pre-process features data to generate pre-processed data (para [0080], FIG. 5 illustrates a flow chart of a data preparation module in FIGS. 3 and 4. When the historical data is downloaded (70) from the database, it is first checked (72) to make sure there is valid data. It can then be pre-processed (74) (e.g. cleaned up, as described below), and then checked once again (76) to make sure there is a valid dataset for machine learning. If the processed dataset is valid, it may then be formatted into a format appropriate for forecasting and clustering, respectively (78, 80));
perform multi-dimension segmentation on the pre-processed data and compute feature importance to generate segments (para [0129], validation showed that in some scenarios, clustering full feature space (e.g. where data and each feature are orthogonal dimensions) did not perform as expected as the data points were too noisy or equidistant from each other, making it difficult to have reliable clustering. It was found that by projecting the higher dimensionality dataset to 2-dimensional space, noise was reduced, and clustering performance was improved in some scenarios and the approach was adopted; para [0130], a computationally efficient clustering technique was developed that enabled identification of potential issues with a client's supply lines. Given the large amounts of data, it was found that conventional clustering algorithms could not be used in a standard manner. A clustering strategy was developed that uses derived statistics such as trends and seasonality to improve the performance of the clustering algorithms. Additionally, a technique was developed to reduce the noise of results by projecting onto two-dimensional space);
generate one or more GUI displays to visualize the generated segments (para [0073], The layer 15 may also provide secure access with a user interface 32, which may allow users to log into the supply chain planning platform 30 to view data or perform other operations. The layer 15 may utilize a full featured web services library to provide a connection for the user interface 15 to the platform 30. Generally, the layer 15 provides a mechanism for interfacing with the different systems and web interfaces);
assign one or more policy parameters to the generated segments (para [0114], for each of the five forecast machine algorithms that were being tested, it was hypothesized that most of the listed parameters (of each algorithm) should have a negligible effect on the quality of final forecasting. Experiments were performed to determine the importance of each parameter in the model quality. Finally, a few parameters were chosen for each of the algorithms and a grid search was performed on those parameters on the historical data); and
train one or more machine learning models, based on the generated segments, to predict a tactical segmentation of new data and to predict segment intersections (para [0105], FIG. 6 illustrates a flow chart of the Forecasting component of FIG. 3. Once the data is processed by the data preparation module, it is ready for the forecasting machine learning process. First the data can be grouped by key fields (82) (for example, a customer ID, a parts ID, etc). The data is also grouped into a first portion (called a training portion) and second portion (called a testing portion). Then, a plurality of forecasting machine learning algorithms are each trained on the training portion; the forecasting of each is tested on the testing portion. In FIG. 6, four algorithms (84a, 84b, 84c, 84d) are trained and tested. However, fewer or more algorithms may be used for training and testing. The most accurate model (86) is then selected and retrained on the full historical dataset; para [0110], The forecasting machine learning algorithm may be first trained before it is used to forecast. As discussed above, due to the variability of the historical lead time data, a plurality of machine learning algorithms may be independently trained on a portion of the data. For example, if 2 years of data is provided, each forecasting machine learning algorithm may be trained on 70% of the data (i.e. approximately the first 17 months).
Regarding to claim 2, Oliveira Almeida discloses the system of Claim 1, wherein the computer is further configured to select a segmentation workflow depth by:
using a comprehensive scenario comprising using all data available and more extensive computation time; or using an agile scenario comprising using a sample of data and less computation time (para [0120], The planner can then use the machine learning forecasting results of lead times (that are uploaded onto the platform) in a rapid forecasting scenario of the “Rapid Response” platform to see if the scheduled lead times should be ignored or accepted).
Regarding to claim 3, Oliveira Almeida discloses the system of Claim 1, wherein generating cleansed data comprises copying one or more pre-discovered features stored in input data into the cleansed data (para [0082], Before analysis by machine learning, the data may be “cleansed” to remove data errors and outliers. Examples of data errors may include records with null values, duplicate records, negative lead times, etc.).
Regarding to claim 4, Oliveira Almeida discloses the system of Claim 1, wherein discovering features comprises aggregating or dis-aggregating the cleansed data based on one or more segment intersections (para [0134], Clustering may be performed on each of the three groups (i.e. “sparse”, “flat” and “rich”) by finding an optimum number of clusters (98). The optimum number may be between 2 and 100 clusters. Once the number of clusters is established (for each group), an initial clustering is performed. Within each group, there is an optional step of splitting large clusters (99) further into smaller sub-clusters).
Regarding to claim 5, Oliveira Almeida discloses the system of Claim 4, wherein the aggregating or the disaggregating the cleansed data comprises using one or more direct input features or one or more derived features (para [0128], It was hypothesized that performance may be improved by augmenting the raw data with features (e.g. higher order statistics and available metadata). After iterative experimentation with different features and algorithms, two features were identified that significantly improved the performance of the clustering algorithms: linearity (obtained through linear regressions) and seasonality (obtained through Fourier transform)).
Regarding to claim 6, Oliveira Almeida discloses the system of Claim 5, wherein the one or more derived features are computed based on one or more other features stored in the cleansed data (para [0038], When accessing the machine learning clustering module, the method comprises: separating the processed historical lead time data into groups based on a density of data points and linearity of historical lead times; performing a cluster analysis based on each of the pre-defined features within each group, with the pre-defined features selected from seasonality and linearity; pruning each cluster; dividing each cluster into a plurality of subclusters; and uploading the clusters to the database).
Regarding to claim 7, discloses the system of Claim 1, wherein at least one of the one or more machine learning models comprises a neural network (para [0110], The forecasting machine learning algorithm may be first trained before it is used to forecast. Example of forecasting machine learning algorithms include Adaboost Regressor, FBProphet, linear regression, mean and median).
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 1-6 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:
Makhija et al. (US 2022/0180274) disclose a data processing system and method for demand sensing and forecasting.
Bikumala et al. (US 2022/0129803) disclose detecting supply chain issues in connection with inventory management using machine learning techniques.
Willis (US 2020/0034860) discloses a supply chain inventory management and information sharing system comprises a plurality of tag readers and a computer server.
Sharpe et al. (US 2013/0085812) disclose a method for providing a supply chain performance management tool may include receiving an identification of supply chain entities and corresponding operational activities therebetween for an organization to generate a functional representation of the supply chain.
Zhaoyan et al. (WO 2020/186183) discloses a system and a method for adjusting communication settings based on user segmentation. An activity-based communication management system retrieves behavioral and demographic data of at least one user. The system inputs the behavioral data and the demographic data into machine learning models.
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.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (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 USA OR CANADA) or 571-272-1000.
/NGA B NGUYEN/Primary Examiner, Art Unit 3625 September 2, 2026
Prosecution Insights