DETAILED ACTION
Status of Claims
This action is in reply to the Applicant Remarks and Amendments filed on 12/31/2025.
Claims 1, 8, and 15 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
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 .
Response to Arguments
Applicant's arguments, see Pages 7-11, filed 12/31/2025, with respect to the 35 U.S.C. 101 rejection of Claims 1-20 have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of Claims 1-20 has been withdrawn. Examiner notes that the amended independent claims 1, 8, and 15 overcome the 35 U.S.C. 101 rejection. Particularly, the additional elements in the amended independent claims integrate the judicial exception into a practical application because they apply the judicial exception with, or by use of, a particular machine “automated robotic production machinery” (See MPEP 2106.05(b)).
Applicant’s arguments, see Page 11, filed 12/31/2025, with respect to the 35 U.S.C. 103 rejection of Claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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.
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 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 nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over BAJAJ et al. (US PG Pub. No. 2020/0126014 A1; hereinafter "BAJAJ") in view of Najmi; Adeel (US PG Pub. No. 2016/0217406 A1; hereinafter "Najmi"), Gorur Narayana Srinivasa et al. (US PG Pub. No. 2012/0035984 A1; hereinafter "Gorur"), and Tilly; Philippe Jean-Mare (US PG Pub. No. 2019/0295032 A1; hereinafter "Tilly").
Regarding Claim 1, BAJAJ teaches a system for visualizing and modifying a model of a supply chain network, comprising: a computer, comprising a processor and memory, and configured to: generate a network modeler GUI of a modeled supply chain network associated with the … analysis (See “Platform 307 may further be configured to generate visualizations, such as media, charts, graphs, node trees, and the like, for inspection and/or follow-up action by a user.” in Paragraph [0067] and “This vast data store 1711 that is available to the analytics engine may allow for, for example, global end to end visibility throughout a supply chain for a particular product or group of products, as is illustrated in FIGS. 15(A) and 16(B). As indicated in FIGS. 15 and 16, various suppliers, manufacturers and customers in the supply chain may be visually presented for a given product, and summary information with available drill down detail may be provided at each one of these “nodes within the supply chain.” Further available in association with each of these nodes may be a “flowchart” of key elements, such as parts, suppliers, manufacturers, geographies, or the like, such as from the inception of the supply chain to its end upon sale of the product. The end to end visibility throughout the supply chain and its nodes may be enhanced by the providing of alerts, such as based on calculated thresholds by the analytics engine 1703, of areas of enhanced risk within the supply chain, as is illustrated in the exemplary embodiment of FIG. 17 (C).” in Paragraph [0131]); receive a selection to edit one or more node icons of the modeled supply chain network displayed on the network modeler GUI (See “FIG. 8B illustrates an embodiment, where, if a system component is selected in section 802, a functionality window 804 is provided for assigning ownership, comments, entering actions and escalation to components of 802. In this example, window 804 enables entry of ownership (“owner”) for a part number, and an “assigned by” and assignment date entry for each area (safety stock, MOQ). Comments may be entered into window 804 as shown, together with an action drop-down menu allowing automated action entries such as “not started”, “started”, “achieved”, “unachievable” and “in escalation”.” in Paragraph [0098] and see “804” in Fig. 8B); receive a selection to modify the modeled supply chain network; update the … analysis based on at least one of the received selections (See “The system may be configured such that, as other opportunities (i.e., opportunities other than the largest) are selected, the total opportunity bubble 707 automatically recalculates the total opportunity value for immediate review by a user. Such a configuration is particularly advantageous for analyzing primary and secondary opportunities quickly and efficiently.” in Paragraph [0096] and “The bubble data visualization of FIG. 7 may be advantageously configured to provide immediate analytics generated from one or more modules in the system. Turning to FIG. 8A, an exemplary embodiment is provided where opportunity bubble 701 is selected, which in turn launches analytics window 801 comprising graphical 803 and textual 802 representations of the underlying data. In this example, graphical representation 803 comprises a chart illustrating a dollar value opportunity trend spanning a predetermined time period. Textual representation 802 comprises a table, indicating a site location (Ste), part name, ABC code, MOQ, multiple quantity value, reduction value and opportunity value, similar to the embodiments discussed above in connection with FIGS. 5A-F.” in Paragraph [0097]).
BAJAJ does not explicitly teach; however, Najmi teaches perform a … analysis using one or more goals and one or more levers (See “In some embodiments, planning if supply chain planner sets a goal for one or more supply chain entities 120 as a 95% fill rate with 35 days of inventory, automated KPI monitoring 614 will determine if one or more supply chain entities 120 is actually achieving this goal by monitoring the KPIs, e.g. fill rate and days of inventory. If actual performance is underperforming, such as, for example, 37 days of inventory and a 90% fill rate, a KPI report 616 indicates the underperformance. Automated KPI monitoring 614 determines that the days of inventory are lower than the goal. Automated KPI monitoring 614 determines low inventory is caused by not enough inventory being stocked initially, and automated KPI monitoring 614 indicates this fact in KPI report 616.” in Paragraph [0064]); receive a selection to add one or more transportation lanes to the modeled supply chain network (See “For example only and not by way of limitation, if one of the one or more supply chain entities 120 has a supply problem with parts, for example, a late part, and the most appropriate lever 372 is to expedite an impending shipment by switching from a regular truck to a team truck, then self-learning system 110 exercises a lever 372 predetermined to be effective to resolve the supply chain problem or, alternatively, presents to a user the option to select a lever that will be effective to resolve the supply chain problem. In this example, the lever 372 would switch the supply of the part from a regular truck to a team truck. As part of the self-learning process, self-learning system 110 also monitors and stores in levers effectiveness and optimization module 376 data concerning the eventual delivery of the part, and how the switching of the delivery of the part from a regular truck to a team truck affects other orders in the supply chain.” in Paragraph [0045] wherein the “user’s selection to switch from a regular truck to a team truck” is considered to be “selection to add one or more transportation lanes to the modeled supply chain network”); receive a selection to add one or more levers to the modeled supply chain network (See “Self-learning system 110 displays to a user one or more levers 372, which, when selected by a user, will enact one or more resolutions to the misalignment with the supply chain plan. In the example just mentioned comprising a misaligned supply chain plan due to a late order, the levers, when selected by a user, may redirect product from another order, split demand, and/or utilize an alternate workflow. Other corrective actions include, for example, expending material in transport, increasing the priority for a manufacturing lot, utilizing material from a first order to fulfill a second order, marking down products, expediting transportation, adding overtime to increase capacity, and offloading work to alternate resources.” in Paragraph [0043] and “For example only and not by way of limitation, if one of the one or more supply chain entities 120 has a supply problem with parts, for example, a late part, and the most appropriate lever 372 is to expedite an impending shipment by switching from a regular truck to a team truck, then self-learning system 110 …, alternatively, presents to a user the option to select a lever that will be effective to resolve the supply chain problem. In this example, the lever 372 would switch the supply of the part from a regular truck to a team truck.” in Paragraph [0045]); generate and display a response plan based on the updated … analysis (See “Self-learning system 110 provides a planner supply chain performance dashboards 701 by calculating and displaying “Performance to Plan” metrics for production, sales, and/or inventory. In some embodiments, supply chain performance dashboards 701 determine guided analysis paths for augmenting supply chain performance dashboards 701, which enable a planner to identify root causes by navigating from metrics (including top level metrics) to root causes of performance deviations.” in Paragraph [0040], Fig. 7, “Self-learning system 110 stores levers 372 in planning levers library 240. Levers 372 comprise workflows that automate corrective actions. For example and not by way of limitation, if the supply chain performance of one or more supply chain entities 120 is not aligned with the supply chain plan due to a late order, some potential resolutions include redirecting product from another order, splitting demand, and utilizing other workflows. Self-learning system 110 displays to a user one or more levers 372, which, when selected by a user, will enact one or more resolutions to the misalignment with the supply chain plan. In the example just mentioned comprising a misaligned supply chain plan due to a late order, the levers, when selected by a user, may redirect product from another order, split demand, and/or utilize an alternate workflow. Other corrective actions include, for example, expending material in transport, increasing the priority for a manufacturing lot, utilizing material from a first order to fulfill a second order, marking down products, expediting transportation, adding overtime to increase capacity, and offloading work to alternate resources.” in Paragraph [0043], “In some embodiments, planning livers library 240 comprises a conditional analysis planner 374 which is utilized by self-learning system 110 to evaluate feasibility and/or impact of utilizing a lever 376. In some embodiments, self-learning system 110 utilizes conditional analysis planner 374 to generate simulations of the utilization of one or more levers 372. The simulations compute and display the feasibility, impact, cost, or the like of implementing one or more levers 372 in resolution playbooks 308.” in Paragraph [0046], and “In some embodiments, process playbook 656 provides step-by-step guidelines that indicate what actions to take, e.g., which levers to use when certain events occur. As an example only and not by way of limitations, a computer supplier may have a process play 658 indicating that they will ship a computer quicker if a customer pays for premium shipping. Another example of a process play 658 may provide that, if a computer supplier is running low on inventory, e.g., a 14-inch monitor, the playbook may provide to offer another item for the same price, e.g., a 15-inch monitor for the same price as a 14-inch monitor.” in Paragraph [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of BAJAJ to include performing an analysis using one or more goals and one or more levers, receiving a selection to add one or more transportation lanes to the modeled supply chain network, receiving a selection to add one or more levers to the modeled supply chain network, and generating and displaying a response plan based on the updated analysis, as taught by Najmi, in order to make the supply chain performance optimization process more efficient and effective.
BAJAJ in view of Najmi does not explicitly teach “a polytope analysis”; however, Gorur teaches a polytope analysis (See “The present invention specifies information (parameters for any optimization problem e.g. a supply chain, graph problem, etc) as convex polytopes and is called the Convex Polyhedral Specification.” in Paragraph [0035], “The set of constraints (which define a complex polytope) imposed on the system can be changed as described therein, to exemplarily increase volume (reduce information assumptions about the future, or alternatively improve robustness), change the type of constraint (e.g. from constraints on major product lines to those on minor product lines, etc), while keeping the amount of information controlled in terms of number of bits. This process of using information quantification can be applied to both inputs and outputs (using multiple outputs).” in Paragraph [0067], and “FIG_14 shows the output of the Information Theory Module.Num. of success: It gives the number of attempts of success. Num. of bits: This value is returned by information theory module. This gives the number of bits that are required to represent the information contained by the polytope (represented by the input constraint set). Relative volume: This gives the volume enclosed by the polytope in space formed by input equations at various stages of operation with respect to the last set of input equations which is treated as 100. Minimum or Maximum: the solution returned by the LP solver for equation subjective to minimum or maximum.” in Paragraph [0102]).
The claim limitations are being considered obvious since one of ordinary skill in the art would have been capable of applying this known technique to a known device (method or product) that was ready for improvement and the results would have been predictable to one of ordinary skill in the art. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a polytope analysis of Gorur in the system of BAJAJ in view of Najmi since it would help visualizing, simplifying, and solving large-scale supply chain planning problems.
Although Najmi teaches that a manufacturer produces finished goods such as products (See Paragraph [0024]), BAJAJ in view of Najmi and Gorur does not explicitly teach “automated robotic production machinery configured to: produce products based, at least in part, on the response plan”. However, Tilly teaches automated robotic production machinery configured to: produce products based, at least in part, on the response plan (See “Manufacturers 144 may comprise automated robotic production machinery 145 that produce products based, at least in part, on a returns forecast determined by the one or more demand planners 110, mappings of one or more items in the supply chain networks, characteristics of one or more customer segments, and/or one or more other factors described herein.” in Paragraph [0026]).
The claim limitations are being considered obvious since one of ordinary skill in the art would have been capable of applying this known technique to a known device (method or product) that was ready for improvement and the results would have been predictable to one of ordinary skill in the art. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use an automated robotic production machinery in producing products based on the response plan of Tilly in the system of BAJAJ in view of Najmi and Gorur to increase productivity, improve precision, lower labor costs, and ensure consistent quality.
Regarding Claim 2, BAJAJ in view of Najmi, Gorur, and Tilly teaches all the limitations of Claim 1 as described above. Gorur also teaches wherein the network modeler GUI comprises a display of one or more of: an info pane, a map pane, one or more node icons and one or more transportation lanes (See “The system screen layout may contain a plurality of workspace and navigation areas. A cross-function pane may be provided to present functional areas of a business which are included in the platform... A main content pane may be provided as a main workspace of the platform which contains data and informational content.” in Paragraph [0145], “FIG. 29 (20A) illustrates an exemplary simplified interactive map screenshot, which allows users to access nodes such as customer nodes, manufacturing nodes and supplier nodes.” in Paragraph [0146], “For example, as illustrated in FIG. 30 (20B), a plurality of suppliers located about the same geographical location may be visually clustered into a shape, such as a bubble, for manipulation by a user on a map interface, for example. Each cluster, which may contain more than one bubble, may be populated with the number of suppliers based on the level of view such that the number of suppliers may be easily ascertainable by a user. For example, as illustrated in FIG. 30, the map view presented clusters 15 suppliers in the center of the Macau into a single bubble, while also allowing for several smaller clusters which may be readily discernable by the user a separate clusters given the level of map view. In this way, a user may quickly and easily determine at least the general geographic concentration of suppliers in a particular area.” in Paragraph [0147], and Figs. 29 and 30).
Regarding Claim 3, BAJAJ in view of Najmi, Gorur, and Tilly teaches all the limitations of Claims 1 and 2 as described above. Gorur also teaches wherein the map pane comprises one or more of: a source window, a make window, a distribute window and a sell window (See Fig. 29 and “FIG. 29 (20A) illustrates an exemplary simplified interactive map screenshot, which allows users to access nodes such as customer nodes, manufacturing nodes and supplier nodes. A graphic overlay on the node geographical location may provide processed data results for the node. Exemplary attributes that may be displayed include, but are not limited to, demand, service level, inventory, excess, obsolete inventory, AMP opportunity, safety stock, risk attribute score and critical shortages. A supplier location count may also be provided to quickly access numbers of suppliers available at a given location.” in Paragraph [0146]).
Regarding Claim 4, BAJAJ in view of Najmi, Gorur, and Tilly teaches all the limitations of Claim 1 as described above. BAJAJ does not explicitly teach; however, Najmi teaches wherein the selection to modify the modeled supply chain network comprises one or more of: cloning one or more nodes, adding one or more transportation lanes, and adding one or more levers (See “Self-learning system 110 displays to a user one or more levers 372, which, when selected by a user, will enact one or more resolutions to the misalignment with the supply chain plan. In the example just mentioned comprising a misaligned supply chain plan due to a late order, the levers, when selected by a user, may redirect product from another order, split demand, and/or utilize an alternate workflow. Other corrective actions include, for example, expending material in transport, increasing the priority for a manufacturing lot, utilizing material from a first order to fulfill a second order, marking down products, expediting transportation, adding overtime to increase capacity, and offloading work to alternate resources.” in Paragraph [0043] and “For example only and not by way of limitation, if one of the one or more supply chain entities 120 has a supply problem with parts, for example, a late part, and the most appropriate lever 372 is to expedite an impending shipment by switching from a regular truck to a team truck, then self-learning system 110 …, alternatively, presents to a user the option to select a lever that will be effective to resolve the supply chain problem. In this example, the lever 372 would switch the supply of the part from a regular truck to a team truck.” in Paragraph [0045] wherein the “user’s selection to switch from a regular truck to a team truck” is considered to be “selection to add one or more transportation lanes to the modeled supply chain network”.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of BAJAJ to include wherein the selection to modify the modeled supply chain network comprises one or more of: cloning one or more nodes, adding one or more transportation lanes, and adding one or more levers, as taught by Najmi, in order to make the supply chain performance optimization process more efficient and effective.
Regarding Claim 5, BAJAJ in view of Najmi, Gorur, and Tilly teaches all the limitations of Claim 1 as described above. BAJAJ does not explicitly teach; however, Najmi teaches wherein the one or more added transportation lanes each comprise one or more parameters (See “For example, the problems in the supply chain inputs may include, but are not limited to, new unforecasted orders, new orders, changes to existing orders or forecasts, changes to in-transit shipments, changes to work in progress or work in process, changes in inventory, new capacity, reduced capacity, changes to external supply, and the like. In addition, according to one example, these problems may be classified into categories such as, for example, supply changes, inventory changes, capacity changes, demand changes, and the like. Although example categories of problems are described, embodiments contemplate any type of disruptions, plan problems, perturbations, changes, events, or categories of disruptions, perturbations, changes, and/or events, according to particular needs.” in Paragraph [0030], “For example only and not by way of limitation, if one of the one or more supply chain entities 120 has a supply problem with parts, for example, a late part, and the most appropriate lever 372 is to expedite an impending shipment by switching from a regular truck to a team truck, then self-learning system 110 exercises a lever 372 predetermined to be effective to resolve the supply chain problem or, alternatively, presents to a user the option to select a lever that will be effective to resolve the supply chain problem. In this example, the lever 372 would switch the supply of the part from a regular truck to a team truck. As part of the self-learning process, self-learning system 110 also monitors and stores in levers effectiveness and optimization module 376 data concerning the eventual delivery of the part, and how the switching of the delivery of the part from a regular truck to a team truck affects other orders in the supply chain.” in Paragraph [0045], and “Early detection is provided by early warning sensors that detect risks, in real-time, thereby providing notification to generate contingency plans. In this manner, each monitoring system may be used to generate continuous feedback, thereby providing a supply chain planner the ability to adjust one or more supply chain parameters and validate how adjustment of a parameter affects other goals of a supply chain plan. Each closed loop of FIG. 6A will be discussed in the following FIGS. 6B-6D.” in Paragraph [0061]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of BAJAJ to include wherein the one or more added transportation lanes each comprise one or more parameters, as taught by Najmi, in order to make the supply chain performance optimization process more efficient and effective.
Regarding Claim 6, BAJAJ in view of Najmi, Gorur, and Tilly teaches all the limitations of Claim 1 as described above. BAJAJ does not explicitly teach; however, Najmi teaches wherein the network modeler GUI further comprises a display of one or more assumptions and one or more goals (See “FIG. 7 illustrates dashboard 701 to monitor the performance of self-learning system 110, according to an embodiment. In some embodiments, dashboard 701 is tailored to each planner's role and the set of tasks needs to be accomplished throughout the execution cycle process 501. Dashboard 701 comprises KPIs and one or more of work-lists 720, watch-lists 725, favorites 730, plan calendars 735, instant collaboration links to peers 740, and performance scorecards 745. The KPIs are represented by charts 705, 710, and 715 which summarize one or more operational metrics of the supply chain that the planner is tracking. Embodiments contemplate any number or combination of any metrics, charts, or KPIs, according to particular needs.” in Paragraph [0073], “Work-list 720 comprises a list of tasks assigned or owned by the planner. Self-learning system 110 sorts work list 720 in order of triage priority configured by the planner based on task severity, urgency, status and other criteria. Watch-list 725 comprises a list of tasks, such as, for example, the progress of specific orders, expedited lots, critical resources, projects, and the like.” in Paragraph [0074], and Fig. 7 showing an example of GUI that is capable of displaying one or more assumptions and one or more goals).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of BAJAJ to include wherein the network modeler GUI further comprises a display of one or more assumptions and one or more goals, as taught by Najmi, in order to make the supply chain performance optimization process more efficient and effective.
Regarding Claim 7, BAJAJ in view of Najmi, Gorur, and Tilly teaches all the limitations of Claim 1 as described above. BAJAJ also teaches wherein the network modeler GUI further comprises one or more selectable elements configured to hide displayed information and one or more selectable elements configured to expand displayed information (See “FIG. 32 (34) illustrates an exemplary interactive map that may be displayed as part of the supply radar module. Here, different nodes may be simultaneously displayed, including customer nodes, manufacturing nodes and supplier nodes. The system may be configured to display a global sourcing footprint. In one embodiment, geographic areas containing a large concentration of, e.g., supplier, may be configured to cluster the locations into a bubble, where the cluster may contain a count of the units (suppliers) included in the cluster. To view which units (suppliers) make up the cluster, the cluster bubble may be selected and zoomed to expand the cluster... The exemplary interactive map of FIG. 32 may be customized to provide maps pertaining to various attributes including, but not limited to, demand, service level, inventory, excess, obsolete inventory, AMP opportunity, safety stock, risk attribute score and critical shortages.” in Paragraph [0148] wherein the “cluster bubbles” are considered to be the “one or more selectable elements configured to hide and expand displayed information”.).
Claims 8-14 are method claims corresponding to system Claims 1-7. All of the limitations in Claims 8-14 are found reciting the same scopes of the respective limitations in Claims 1-7. Accordingly, Claims 8-14 are considered obvious (rejection) by the same rationales presented in the rejection of Claims 1-7, respectively set forth above.
Claims 15-20 are product claims corresponding to system Claims 1-6. All of the limitations in Claims 15-20 are found reciting the same scopes of the respective limitations in Claims 1-6. Accordingly, Claims 15-20 are considered obvious (rejection) by the same rationales presented in the rejection of Claims 1-6, respectively set forth above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAYAR M KYU whose telephone number is (571)272-3419. The examiner can normally be reached Mon-Fri 9:00 am - 6:00 pm.
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/T.M.K./Examiner, Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628