DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/2026 has been entered.
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 .
Disposition of Claims
Claims 1-22 are pending in the instant application, Claims 1-6, 8-14, and 16-22 are rejected herein. Claims 7 and 15 have been cancelled. No claims have been added. Claims 1, 11, and 19 have been amended. The rejection is hereby made non-final.
Response to Remarks
101
Examiner finds Applicant’s amendments and remarks persuasive. The rejection of the pending claims under 35 USC 101 is hereby withdrawn.
103
Due to Applicant’s arguments and amendments the previous office action is now moot and the claims have been given further searching and consideration. Consequently, please find a new rejection below addressing the amended claims.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-6, 8-14, 16-22 are rejected under 35 U.S.C. 103 as being unpatentable over Jacoby et al (US 2006/0047559) further in view of Surendra et al (US 2010/0125489).
Regarding claim 1, the prior art discloses an apparatus comprising: at least one processing device comprising a processor coupled to a memory, the at least one processing device, when executing program code, is configured to: for a given item type stored as inventory at a plurality of sites, wherein a site within the plurality of sites manages inventory items(see at least paragraph [0021] to Jacoby et al wherein an inventory baseline is determined) ; predict, a stock factor for each of the plurality of sites based on historical stock factor data (see at least paragraph [0057] to Jacoby et al wherein historical demand data is used for service parts to simulate stocking locations); predict, an aging items count for each of the plurality of sites based on historical aging item data (see at least paragraph [0026]to Jacoby et al, wherein the total population of the parts may be determined) based on the assigned plurality of site clusters, the predicted stock factor, the predicted aging items count for each of the plurality of sites demand forecast at each site (see at least paragraphs [0066]-[0067] to Jacoby et al, wherein the target stock levels for the segments are calculated based on the deployment algorithm); and determining a transaction cost between the plurality of sites (see at least paragraph [0043] to Jacoby et al, wherein Assigning the service level attempts to balance the cost of holding or carrying the inventory for the segment against the cost of a stock-out in which a needed service part must be ordered).
Jacoby et al does not appear to explicitly disclose assign[ing], using a machine learning algorithm, a plurality of site cluster each comprising a number of the plurality of sites, wherein the assigning is based on a plurality of clustering factors comprising at least a threshold distance between each of the plurality of sites, over a given future time period;
Send[ing] the plan to the two or more of the plurality of sites to initiate one or more actions to enact the plan; using a second machine learning algorithm, using a third machine learning algorithm,
wherein the predicted aging items count comprises a number of items of the given item type with one of a return date limit and an expiry data in the given future time period;
Generating a current supply forecast based on the given demand forecast for the plurality of sites;
and comput[ing], using a fourth machine learning algorithm, a plan to move an amount of the given item type between two or more of the plurality of sites,
and the generated current supply forecast, to amend the current supply forecast to satisfy the given
upon receiving an affirmation to the plan from the two or more of the plurality of sites, amend the current supply forecast to generated a revised supply forecast and initiate one or more actions to move the amount of the given item type between the two or more of the plurality of sites; send the revised supply forecast to one or more suppliers of the given item type to amend a demand from the one or more suppliers for the given item type, the revised supply an updated predicted aging items count for each of the plurality of sites based on the plan;
further wherein the third machine learning algorithm uses at least the return date limit, the expiry date, and the aging due to seasonality of each of the number of items to predict the aging items count for each of the plurality of sites;
wherein the amount of the given item type moved between the two or more of the plurality of sites satisfies the given demand forecast for the plurality of sites;
and wherein the one or more suppliers of the given item type include one or more suppliers external to the plurality of sites.
However, Surendra et al discloses systems and methods for root cause analysis and early warning of inventory problems, further comprising assign[ing], using a machine learning algorithm (see at least paragraph [0025] to Surendra et al, “system 100 provides for a Plan-Do-Check-Act process that facilitates an auto-learning and self-tuning system”), a plurality of site cluster each comprising a number of the plurality of sites, wherein the assigning is based on a plurality of clustering factors comprising at least a threshold distance between each of the plurality of sites, over a given future time period (see at least paragraph [0027] to Surendra et al, wherein supply chain planner 110 analyzes demand patterns 116a and quantifies the deviation (i.e., quantification of risk), to account for lumpiness, data hygiene factor, and the aggregation level across product geography and time (i.e., appropriate selection of level of abstraction across product, geography and time));
Send[ing] the plan to the two or more of the plurality of sites to initiate one or more actions to enact the plan (see at least paragraph [0032] to Surendra et al, wherein supply chain planner 110 publishes updated inventory policy parameters 116d from step 208 to one or more planning engines 114a, for example, supply chain replenishment planning engines, of one or more supply chain entities 120a-120n. For example, the published inventory policy parameters 116d are input into the one or more supply chain replenishment planning engines to generate an executable supply chain plan); using a second machine learning algorithm, using a third machine learning algorithm,
wherein the predicted aging items count comprises a number of items of the given item type with one of a return date limit and an expiry data in the given future time period (see at least paragraph [0050] to Surendra et al, wherein Than the inventory is aging inventory, after the item has reached its end of life and the aging inventory needs to be cleared up);
Generating a current supply forecast based on the given demand forecast for the plurality of sites (see at least paragraph [0052] to Surendra et al);
and comput[ing], using a fourth machine learning algorithm, a plan to move an amount of the given item type between two or more of the plurality of sites (see at least paragraph [0052] to Surendra et al, wherein some of the typical actions that supply chain planner 110 provides is adjustments to demand forecasts, adjustments to Material Requirements Plans (MRP) published to suppliers, changes to shipment plans, adjustments to published inventory policy parameters (such as safety stock targets), actions to re-assign or dispose aging inventory, and corrections to data in source systems),
upon receiving an affirmation to the plan from the two or more of the plurality of sites, amend the current supply forecast to generated a revised supply forecast and initiate one or more actions to move the amount of the given item type between the two or more of the plurality of sites; send the revised supply forecast to one or more suppliers of the given item type to amend a demand from the one or more suppliers for the given item type, the revised supply an updated predicted aging items count for each of the plurality of sites based on the plan (see at least paragraph [0052] to Surendra et al, wherein some of the typical actions that supply chain planner 110 provides is adjustments to demand forecasts, adjustments to Material Requirements Plans (MRP) published to suppliers, changes to shipment plans, adjustments to published inventory policy parameters (such as safety stock targets), actions to re-assign or dispose aging inventory, and corrections to data in source systems);
further wherein the third machine learning algorithm uses at least the return date limit, the expiry date, and the aging due to seasonality of each of the number of items to predict the aging items count for each of the plurality of sites (see at least paragraph [0050] to Surendra et al);
wherein the amount of the given item type moved between the two or more of the plurality of sites satisfies the given demand forecast for the plurality of sites (see at least paragraph [0052] to Surendra et al, wherein some of the typical actions that supply chain planner 110 provides is adjustments to demand forecasts, adjustments to Material Requirements Plans (MRP) published to suppliers, changes to shipment plans, adjustments to published inventory policy parameters (such as safety stock targets), actions to re-assign or dispose aging inventory, and corrections to data in source systems);
and wherein the one or more suppliers of the given item type include one or more suppliers external to the plurality of sites (see at least paragraph [0014] to Surendra et al, wherein .
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the system and method for managing an inventory of service parts, as disclosed by Jacoby et al and the system and method for root cause analysis and early warning of inventory problems, as taught by Surendra et al, in order to dynamically adjust inventory levels across a plurality of locations according to demand data, could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 2, the prior art discloses the apparatus of claim 1, wherein the at least one processing device, when executing program code, is further configured to cause the plan to be initiated by the two or more of the plurality of sites (see at least paragraph [0050] to Jacoby et al, wherein each segment is assigned to a best fit planning model).
Regarding claim 3, the prior art discloses the apparatus of claim 1, wherein the at least one processing device, when executing program code, is further configured to revise a supply forecast based on the plan (see at least paragraph [0050] to Jacoby et al “planning model describes the deployment, replenishment, forecasting and review parameters for the segment”).
Regarding claim 4, the prior art discloses the apparatus of claim 3, wherein the at least one processing device, when executing program code, is further configured to cause the revised supply forecast to be implemented with respect to one or more suppliers of the given item type (see at least paragraph [0050] to Jacoby et al “planning model describes the deployment, replenishment, forecasting and review parameters for the segment”).
Regarding claim 5, the prior art discloses the apparatus of claim 1, wherein the stock factor at each of the plurality of sites is computed as an items count in inventory divided by an estimated demand (see at least paragraph [0057] to Jacoby et al “historical demand data for the services parts and used that data to simulate the stocking locations. The simulation would be run to find the first pass fill rate ("FPFR"). Once the FPFR was known, one would run the simulation again on an iterative basis, slowly (and clumsily) backing into the number hopefully associated with the distribution function”).
Regarding claim 6, the prior art discloses the apparatus of claim 5, wherein the items count in inventory comprises on hand items and in transit items (see at least paragraph [0008] to Jacoby et al, wherein inventory includes central depots, field depots, customer depots, and mobile stock).
Regarding claim 8, the prior art discloses the apparatus of claim 1, wherein the at least one processing device, when executing program code, is further configured to assign each of the plurality of sites to two or more site clusters (see at least paragraph [0050] to Jacoby et al, wherein each of the segments is assigned to a best fit planning model).
Regarding claim 9, the prior art discloses the apparatus of claim 8, wherein the at least one processing device, when executing program code, is further configured to compute the plan based on which of the plurality of sites are assigned to which of the two or more site clusters (see at least Figure 4, to Jacoby et al, “supplier stocking locations, full stocking parts depots, customer proximity depots, customer stocking locations”).
Regarding claim 10, the prior art discloses the apparatus of claim 1, wherein the at least one processing device, when executing program code, is further configured to compute the plan based on one or more transportation factors associated with moving an amount of the given item type between two or more of the plurality of sites (see at least paragraphs [0050] and [0051] to Jacoby et al “Such a planning model describes the deployment, replenishment, forecasting and review parameters for the segment. FIG. 5 is a diagram of a planning model continuum that illustrates the variation in the planning models from the most basic (on the left) to the most advanced approaches (on the right)” and Figures 4 and 5 to Jacoby et al “variation from delivery commit data”).
Claims 11-14 and 16-20 each contain recitations substantially similar to those addressed above and, therefore, are likewise rejected.
Regarding claim 21, the prior art discloses the computer program product of claim 19, wherein the program code when executed by at least one processing device further causes the at least one processing device to assign each of the plurality of sites to two or more site clusters (see at least paragraph [0024] to Surendra et al, wherein network 130 may include the Internet and any appropriate local area networks (LANs), metropolitan area networks (MANS), or wide area networks (WANs) coupling supply chain planner 110 and one or more supply chain entities 120a-120n. For example, data may be maintained by supply chain planner 110 at one or more locations external to supply chain planner 110 and one or more supply chain entities 120a-120n and made available to one or more associated users of one or more supply chain entities 120a-120n using network 130 or in any other appropriate manner).
Regarding claim 22, the prior art discloses the computer program product of claim 19, to compute the plan based on one or more transportation factors associated with moving an amount of the given item type between two or more of the plurality of sites (see at least paragraph [0025] to Surendra et al, wherein system 100 comprises key inputs that include demand forecast waterfall history and future forecast, shipment/sales history, inventory history, receipts history and forecast (Advance Ship Notices/Commits), inventory targets history and forecast, plus a library of parameterized business rules).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST.
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, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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.
/TALIA F CRAWLEY/Primary Examiner, Art Unit 3627