Prosecution Insights
Last updated: April 19, 2026
Application No. 18/903,507

SYSTEMS AND METHODS FOR ADVANCED INVENTORY CONTROL AUTOMATION

Non-Final OA §101§103
Filed
Oct 01, 2024
Examiner
MASUD, ROKIB
Art Unit
3627
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
State Farm Mutual Automobile Insurance Company
OA Round
1 (Non-Final)
68%
Grant Probability
Favorable
1-2
OA Rounds
3y 5m
To Grant
69%
With Interview

Examiner Intelligence

Grants 68% — above average
68%
Career Allow Rate
503 granted / 735 resolved
+16.4% vs TC avg
Minimal +0% lift
Without
With
+0.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 5m
Avg Prosecution
34 currently pending
Career history
769
Total Applications
across all art units

Statute-Specific Performance

§101
30.5%
-9.5% vs TC avg
§103
46.8%
+6.8% vs TC avg
§102
14.3%
-25.7% vs TC avg
§112
4.8%
-35.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 735 resolved cases

Office Action

§101 §103
DETAILED ACTION 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 . 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 1–22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (abstract idea) and does not include additional elements that amount to significantly more than the abstract idea. Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05. Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c). Step 1 – Statutory Category Claims 1–22 are directed to statutory categories (systems, methods, and computer-readable media). Accordingly, the analysis proceeds to Step 2A. Step 2A, Prong One – Judicial Exception The claims are directed to organizing human activity, commercial interactions, and mental processes, including: Collecting and analyzing vendor information Ranking and selecting vendors Facilitating negotiations Generating recommendations and documents Monitoring meetings and outcomes These concepts fall within fundamental economic practices and data analysis, which are recognized abstract ideas. Claims 1, 21 and 22 as a whole, recites collecting, analyzing, and acting on business negotiation data, which is a fundamental economic practice long prevalent in commerce. Claims 2–3 recites Receiving vendor attributes and vendor-provided data merely adds additional data sources. These claims remain directed to information collection and evaluation, which are abstract. Claim 4 recites training a model using historical negotiation data is a form of analyzing past outcomes to improve decision making, which constitutes an abstract mental process when claimed at a high level. Claims 5–7 recite monitoring meetings and generating questions represent human negotiation assistance activities, merely automated using a computer. Claims 8–10 recite scheduling meetings and generating invitations are organizing human interactions, a recognized abstract idea. Claim 11 recite ranking vendors based on goals, strategies, or outcomes is evaluative judgment and business decision-making. Claim 12 recite monitoring vendor performance and updating the model reflects performance evaluation and learning, which are abstract analytical processes. Claim 13 recite selecting a vendor based on meeting information is decision-making using analyzed data. Claims 14–15 recite generating NDAs and requesting private information are post-selection administrative steps, ancillary to the abstract negotiation process. Claim 16 recite preparing a contract is a conventional business document generation activity. Claims 17–18 recite generating requests based on inventory levels is business logistics analysis. Storing inventory data in a blockchain does not change the abstract nature of the claim and merely recites a generic data structure. Claims 19–20 recite generating feedback for unselected vendors is evaluation and advisory output, which is abstract. Therefore, claims 1–20 recite abstract ideas involving business negotiation, vendor selection, and data analysis. Step 2A, Prong Two – Integration into a Practical Application The claims do not integrate the abstract idea into a practical application because: The claims merely automate traditional negotiation and procurement practices. No improvement to computer technology itself is recited. The claims do not specify how the negotiation analysis model is technically implemented. The user interface, scheduling, monitoring, and document generation steps are result-oriented. The claimed computer elements are used only as tools to implement the abstract idea. Step 2B – Inventive Concept The claims do not recite significantly more than the abstract idea because: The processor, memory, model, and user interfaces are generic computer components. Machine learning is recited functionally, without technical detail. No unconventional data structures, training techniques, or system architecture are disclosed. The combination of elements reflects routine and conventional business automation. Accordingly, the claims fail to provide an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. Thus, after considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims are not enough to transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea. Accordingly, claims 1-20 are directed to non-statutory subject matter under 35 U.S.C. § 101. 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) 1-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zagorin et al. (US 2019/0244268, hereinafter Zagorin), in view of Ohayon (US 12,156,089), and further in view of Ozonat et al. (US 2012/0290485, hereinafter Ozonat). With respect to claims 1, 21 and 22 Zagorin discloses a computer-implemented system for automating vendor negotiations and analysis using one or more processors and memory (¶¶ [0012], [0031], [0040]). storing and executing a negotiation analysis model that evaluates vendors based on multiple data inputs to facilitate negotiations (¶¶ [0035], [0046], [0062]); receiving a buyer request for goods or services that initiates automated negotiation and vendor evaluation (¶¶ [0022], [0043]); analyzing plurality of vendor data to generate vendor scores and rankings for responding to the request (¶¶ [0048], [0064]); selecting and contacting vendors based on their ranking or scoring outputs (¶¶ [0066], [0069]); automatically scheduling negotiation meetings between buyers and selected vendors (¶¶ [0073], [0075]); monitoring vendor meetings and negotiation sessions to capture interaction data (¶¶ [0078], [0081]); generating vendor recommendations using both historical vendor data and monitored negotiation interactions (¶¶ [0084], [0087]); generating and transmitting contracts and negotiation documents to an approved vendor (¶¶ [0091], [0094]). Zagorin does not disclose the feature of monitoring negotiation exchanges between parties in an automated negotiation environment, generating predictive vendor rankings using learned models and generating final vendor recommendations based on predictive outcomes from learned models. However, Ozonat further teaches monitoring negotiation exchanges between parties in an automated negotiation environment (¶¶ [0029], [0033]), and Ohayon teaches generating predictive vendor rankings using learned models (column 5 lines 37-48), and generating final vendor recommendations based on predictive outcomes from learned models (column 6 lines 8-24). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine Zagorin’s automated negotiation system with Ohayon’s machine-learning vendor recommendation techniques and Ozonat’ automated negotiation monitoring, because such combination, improves accuracy of vendor selection, enhances automated negotiation efficiency, predictably improves results using known ML techniques. With respect to claim 2, Zagorin futher discloses the feature of vendor attributes (¶¶ [0046], [0061]). With respect to claim 3, Zagorin futher discloses the feature of receiving vendor data from vendor-associated devices (¶¶ [0052], [0056]). With respect to claim 4, Zagorin futher discloses the feature of training the negotiation model using historical negotiation data is taught by Zagorin (¶¶ [0060], [0062]) and Ohayon (¶¶ [0039], [0043]). With respect to claims 5-7, Zagorin futher discloses the feature of monitoring meetings and generating questions during negotiations are taught by Zagorin (¶¶ [0078], [0082], [0086]) (¶¶ [0031], [0034]). With respect to claims 8-10, Zagorin futher discloses the feature of scheduling meetings and generating meeting invites based on requests (¶¶ [0073]–[0076]). With respect to claim 11, Zagorin futher discloses the feature of ranking vendors based on goals, strategies, and outcomes (¶¶ [0049], [0065]). With respect to claim 12, Zagorin futher discloses the feature of monitoring vendor performance and updating the model based on outcomes (¶¶ [0052], [0057]). With respect to claim 13, Zagorin futher discloses the feature of selecting vendors based on meetings and vendor information (¶¶ [0084], [0088]). With respect to claims 14-15, Zagorin futher discloses the feature of generating NDAs and requesting private vendor information (¶¶ [0096], [0099]). With respect to claim 16, Zagorin futher discloses the feature of preparing contracts for approved vendors (¶¶ [0091], [0094]). With respect to claims 17-18, Zagorin futher discloses the feature of generating vendor requests based on inventory levels (¶¶ [0058], [0060]). Use of distributed or secure data structures is an obvious design choice. With respect to claims 19-20, Zagorin futher discloses the feature of generating feedback reports for unselected vendors (¶¶ [0101], [0104]). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROKIB MASUD whose telephone number is (571)270-5390. The examiner can normally be reached Mon-Fri 8:00-5:00. 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 Obeid can be reached at 571-270-3324. 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. /ROKIB MASUD/Primary Examiner, Art Unit 3627
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Prosecution Timeline

Oct 01, 2024
Application Filed
Jan 24, 2026
Non-Final Rejection — §101, §103 (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

1-2
Expected OA Rounds
68%
Grant Probability
69%
With Interview (+0.2%)
3y 5m
Median Time to Grant
Low
PTA Risk
Based on 735 resolved cases by this examiner. Grant probability derived from career allow rate.

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