Prosecution Insights
Last updated: April 19, 2026
Application No. 18/640,075

METHOD AND SYSTEM FOR SIMULATING FULFILLMENT OF DIGITAL ORDERS

Final Rejection §101
Filed
Apr 19, 2024
Examiner
KONERU, SUJAY
Art Unit
3624
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Target Brands Inc.
OA Round
4 (Final)
58%
Grant Probability
Moderate
5-6
OA Rounds
3y 2m
To Grant
95%
With Interview

Examiner Intelligence

Grants 58% of resolved cases
58%
Career Allow Rate
421 granted / 722 resolved
+6.3% vs TC avg
Strong +37% interview lift
Without
With
+37.0%
Interview Lift
resolved cases with interview
Typical timeline
3y 2m
Avg Prosecution
36 currently pending
Career history
758
Total Applications
across all art units

Statute-Specific Performance

§101
37.9%
-2.1% vs TC avg
§103
50.7%
+10.7% vs TC avg
§102
2.0%
-38.0% vs TC avg
§112
7.4%
-32.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 722 resolved cases

Office Action

§101
DETAILED ACTION This Final Office Action is in response to Applicant's amendments and arguments filed on November 12, 2025. Applicant has amended claims 21 and 34. Currently, claims 21-39 are pending. 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 Amendments The 35 U.S.C. 101 rejections of claims 21-39 are maintained in light of applicant’s amendments to claims 21 and 34. Response to Arguments Applicant’s remarks submitted on 11/12/25 have been considered but are not persuasive. Applicant argues on p. 9 of the remarks that the 101 rejection is improper. Examiner disagrees. Applicant argues on p. 13 of the remarks that the claims show modeling improvement. Examiner notes that supply chain modeling is considered abstract so such an improvement is an improvement to the abstract idea. Applicant argues on p. 13-14 that the claims recite significant computation steps. Examiner notes that significant computing processing is still considered using a computer to implement the abstract idea and that this is not considered a technical improvement. Applicant argues on p. 14 that such improvements reduce computational resources. Examiner notes such improvements (computing processing efficiency as a result of reducing the amount of simulations) are not sufficiently tethered to the claims. Examiner further notes that PTAB decisions such as Mirza are not considered precedent and thus not persuasive. Applicant further argues on p. 17 of the remarks that the claims are eligible because of step 2B. Applicant makes comparisons to example 34. Examiner notes that example 34 is not analogous because applicant's claims are not an improvement to the computer technology of filtering but rather an improvement to an abstract idea. Applicant's claims use of metrics from a supply chain is a generic tool for providing data that is used in the modeling. Applicant argues on p. 20 that the ordered combination is a justification for the claims being eligible. Examiner disagrees and notes the ordered combination is well understood in the field of modeling where the data is received and then there is some sort of analysis and output of such analysis. Therefore, the 101 rejections are maintained. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 21-39 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method and system). Claims 21-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 21 and 34 recite the abstract idea of simulating fulfillment of orders within a supply chain network by simulating execution of a set of predicted orders within the supply chain network using a supply chain simulation model representative of a digital fulfillment process within the supply chain network and wherein simulating execution of the set of predicted orders includes: retrieving historical order information from an external demand guidance system and connecting to one or more feeds supplying live supply chain network data and predicting, at a stochastic model, varying lead times corresponding to stages of the supply chain network based on operational disruptions, wherein the stochastic model utilized a nominal value and an error model to generate predicted lead time values and generating, at a fill rate module, a predicted fill rate representative of item location information at each of a plurality of nodes within the supply chain network and generating a capacity module, a predicted capacity for items at each of the plurality of nodes within the supply chain network and generating a serviceability module, a predicted service level, wherein the service level is based on a simulated availability of items and generating a baseline scenario of transaction-level operation of the supply chain network using a default value for a first operational parameter of the supply chain simulation model and using the predicted fill rate, capacity, and service level and dynamically updating the baseline scenario based at least in part on the supply chain network data and generating at least one modified scenario of transaction-level operation of the supply chain network using an experimental value for the first operational parameter of the supply chain simulation model outputting transaction level data for each discrete order in the set of predicted orders from the baseline scenario and the at least one modified scenario and receiving, as an input to an aggregation process, the transaction level data, wherein the aggregation process includes: transforming transaction level data into values for a plurality of predicted metrics for the baseline scenario and the at least one modified scenario, generating a first scenario evaluation by aggregating the values from the transformed transaction level data along a first vector for the plurality of predicted metrics associated with the order fulfillment process and for the baseline scenario and the at least one modified scenario, generating a second scenario evaluation by aggregating the values from the transformed transaction level data along a second vector for the plurality of predicted metrics associated with the digital order fulfillment process and comparing the plurality of predicated metrics between the scenario evaluation for the at least one modified scenario with the first scenario evaluation for the baseline scenario wherein the plurality of predicted metrics are affiliated with a plurality of aspects of the supply chain network and results based on comparing the scenario evaluations, wherein the results include an identified challenge associated with fulfillment of predicted orders and identifying and providing a recommendation for solving the identified challenge. The claims are directed to a type of simulating and analysis of order and transaction data in a supply chain. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain methods of organizing human activity including commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claims are simulating and analyzing human and business activity (order information and transaction level operations) and that data is organized by the comparisons and results generated. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as digital orders, live data feeds, live supply chain network data, displaying on a user interface, a computing system including a data store, a processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the processor to perform steps) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as digital orders, live data feeds, displaying on a user interface, a computing system including a data store, a processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the processor to perform steps (as evidenced by para [0030], [0034], [0066]-[0067], [0082]-[0087] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 22-23, 26-32, 35-39 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing storing the scenario evaluation for each of the baseline scenario and the at least one modified scenario in a memory in association with the experimental value for the first operational parameter and wherein the plurality of predicted metrics include a cost, a capacity, and a guest experience for the digital order fulfillment process and wherein the supply chain simulation model includes a simulation layer having an order simulation engine configured to use the historical order information and demand guidance to simulate execution of predicted future orders received within the supply chain network and wherein the supply chain simulation model includes a simulation layer having a promise engine configured to receive a plurality of simulated orders from the order simulation engine, the promise engine defining a service level based on a simulated availability of items within the supply chain network and wherein the supply chain simulation model includes a simulation layer configured to model a fill rate representative of item location information at each of a plurality of nodes within the supply chain network and to model capacity for items at each of the plurality of nodes based on a known node capacity and wherein the supply chain simulation model includes a simulation layer configured to select a node from the plurality of nodes within the supply chain for simulated fulfillment of each of the plurality of simulated orders based on a defined service level for each of the plurality of simulated orders provided from the promise engine, in combination with the modeled fill rate and modeled capacity at each node and wherein the supply chain simulation model includes a simulation layer configured to simulate a carrier allocation for fulfilling each of the plurality of simulated orders from each of the assigned, simulated nodes based at least in part on serviceability of the respective simulated order, the serviceability being based on at least a carrier rate and a carrier assignment for the respective simulated order and wherein the first operational parameter is one of node of origin location, carrier selection, box size, carrier rate, and shipping cost and wherein the plurality of simulations is a plurality of Monte Carlo simulations using randomly selected values for the experimental value for the first operational parameter and wherein the displayed results include a recommendation for a supply chain network decision associated with the first operational parameter. Dependent claims 24-25 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as wherein the results are displayed in one or more of a graphical and tabular format and wherein the results displayed on the user interface are representative of an aggregated comparison of the scenario evaluations across the same set of predicted digital orders (as evidenced by para [0030], [0034], [0066]-[0067], [0082]-[0087] of applicant’s own specification) are well understood, routine and conventional in the field. Allowable Subject Matter Claims 21-39 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Scheer (US 2002/0161674 A1), a method for fulfilling an order in a supply chain by extracting from a customer system information pertaining to the work order that specifies a piece of equipment to be repaired and items expected to be used during the repair procedure, determining, using an equipment knowledge base, a probability that each of the items will be needed to effect the repair procedure, and using the determined probability to stage the items within the supply chain whereby the items are made ready for use in the repair procedure THIS ACTION IS MADE FINAL. 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 extension fee 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 SUJAY KONERU whose telephone number is (571)270-3409. The examiner can normally be reached M-F, 8:30 AM to 5 pm. 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, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SUJAY KONERU/ Primary Examiner, Art Unit 3624
Read full office action

Prosecution Timeline

Apr 19, 2024
Application Filed
Jan 28, 2025
Non-Final Rejection — §101
Mar 18, 2025
Interview Requested
Mar 26, 2025
Examiner Interview Summary
Mar 26, 2025
Applicant Interview (Telephonic)
Apr 29, 2025
Response Filed
May 07, 2025
Final Rejection — §101
Jun 17, 2025
Interview Requested
Jun 30, 2025
Examiner Interview Summary
Jun 30, 2025
Applicant Interview (Telephonic)
Jul 15, 2025
Request for Continued Examination
Jul 21, 2025
Response after Non-Final Action
Sep 08, 2025
Non-Final Rejection — §101
Oct 01, 2025
Interview Requested
Oct 09, 2025
Examiner Interview Summary
Oct 09, 2025
Applicant Interview (Telephonic)
Nov 12, 2025
Response Filed
Nov 24, 2025
Final Rejection — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

5-6
Expected OA Rounds
58%
Grant Probability
95%
With Interview (+37.0%)
3y 2m
Median Time to Grant
High
PTA Risk
Based on 722 resolved cases by this examiner. Grant probability derived from career allow rate.

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