Prosecution Insights
Last updated: April 19, 2026
Application No. 18/849,258

EQUIVALENCY MACHINE FOR FACILITATING IN-STORE CHECKOUT PROCESS FOR ITEMS ORDERED ONLINE

Non-Final OA §101§103
Filed
Sep 20, 2024
Examiner
MASUD, ROKIB
Art Unit
3627
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Ethor Ip Corp.
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 1–19 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to patent-ineligible abstract ideas without reciting significantly more than the abstract ideas themselves. Step 1: Statutory Category Claims 1–19 are directed to systems, networks, and methods, which nominally fall within the statutory categories of process and machine. Accordingly, the analysis proceeds to Step 2A of the Alice/Mayo framework. Step 2A, Prong One: Abstract Idea Independent Claims Claim 1 is directed to facilitating in-store checkout for items ordered online, including: • receiving order information, • linking online order data with in-store POS item data using an identifier, • comparing ordered items with picked items, and • approving or denying orders. These limitations collectively recite organizing, comparing, and verifying transactional information, which constitutes fundamental commercial practices and mental processes, i.e., abstract ideas. Such activities are analogous to manual order verification, substitution checking, and checkout reconciliation, which have long been performed by humans without technological innovation. Accordingly, claim 1 recites an abstract idea. Claim 12 is directed to comparing details of items ordered online with items entered into an in-store POS system using rules to determine accuracy or substitution acceptability. This claim similarly recites data comparison, rule-based verification, and validation of commercial transactions, which are abstract ideas involving evaluation and judgment of information and fundamental retail practices. Accordingly, claim 12 recites an abstract idea. Claim 18 is directed to improving fulfillment of an online order by: • receiving an online order, • selecting items in-store, • determining whether selected items match ordered items or acceptable substitutes. These limitations describe human decision-making processes related to order fulfillment and substitution approval, which constitute abstract ideas involving organizing human activity and mental comparisons. Accordingly, claim 18 recites an abstract idea. Step 2A, Prong Two: Integration into a Practical Application These claims do not integrate the abstract ideas into a practical application. The recited components—processors, POS systems, databases, scanners, interfaces, memory, and networks—are generic computing elements performing their ordinary functions, such as receiving data, storing data, transmitting data, and comparing data. These claims merely use conventional computer technology as a tool to implement an abstract commercial process more efficiently, which does not constitute a technological improvement to computer functionality or another technical field. Therefore, the claims are not integrated into a practical application. Step 2B: Inventive Concept These claims do not recite any inventive concept sufficient to transform the abstract ideas into patent-eligible subject matter. • The processors are generic and perform routine functions. • The “Equivalency Machine” applies rule-based comparisons without any disclosed technological improvement. • Unique identifiers, scanning, notifications, and reporting are conventional retail and POS techniques. • The claims lack any specific improvement to POS architecture, scanning technology, databases, or networking. Individually and as an ordered combination, the claim elements merely implement abstract commercial practices on generic computer components, which is insufficient under §101. Dependent Claims Claims 2–11 depend from claim 1 and merely add routine limitations, such as: • separating processors (claim 2), • enabling online order interfaces (claim 3), • data exchange between systems (claims 4–5), • providing identifiers to users (claim 6), • rule-based comparisons and notifications (claims 7–11). These limitations do not add meaningful technical features or inventive concepts and therefore do not render claim 1 patent eligible. Claims 13–17 depend from claim 12 and add routine data types, notifications, reporting, and monitoring functions. These limitations involve generic data handling and business reporting and fail to add any technological improvement. Accordingly, claims 13–17 remain patent-ineligible. Claim 19 depends from claim 18 and adds a weight scale to verify selected products. The use of a weight scale to confirm product identity is a well-known, conventional technique in retail and does not provide a technological improvement or inventive concept. 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 1-19 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) 1-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aurora et al. (US20210256042A1, hereinafter Aurora), in view of High et al. (US20220327427A1, hereinafter High), and further in view of High et al. (US20200302329A1, hereinafter High29). With respect to claim 1, AURORA teaches identifying an item and matching it to compatible items based on compatibility data (see ¶¶ [0009]–[0013], Fig. 1, 3–5) and constructing compatibility-based text for query results where matching items are identified (¶ [0014], [0035]), and HIGH teaches retrieving data associated with an unavailable physical object and matching it to available substitute objects based on dynamically learned matching behavior (¶¶ [0012]–[0015], claim 1), and HIGH29 further teaches dynamically learning matching behavior and replacing unavailable items with substitutes based on learned rules (¶¶ [0012]–[0013]). It would have been obvious to one of ordinary skill in the art to combine these teachings to provide a system that links online orders with POS entered items and identifies acceptable substitutes. With respect to claim 2, it would have been obvious to use separate processors for distinct functions such as item identification, order management, and unique identifier generation. AURORA teaches processing modules operating on separate processing units (¶¶ [0010]–[0013], Fig. 2) and HIGH discloses a central computing system with processors performing matching and substitution tasks (¶¶ [0012]–[0014], claim 1). HIGH29 supports implementation of dynamic logic on separate processors (¶ [0013]). With respect to claim 3, AURORA teaches constructing compatibility-based text and providing it to a user interface to enable user access (¶¶ [0014], [0035], Fig. 4). HIGH suggests presenting substitute match results to a user (¶ [0015], claim 1). With respect to claim 4, AURORA teaches that compatibility and item data are shared between modules, suggesting provision of data from POS agents to an order manager (¶¶ [0011]–[0013], Fig. 2). With respect to claim 5, bidirectional data flow is inherent in AURORA’s item matching system (¶ [0014], Fig. 3), making data provision from the order manager to POS agents obvious. With respect to claim 6, AURORA teaches a “compatibility identifier” associated with item clusters, supporting the unique identifier recited in the claim (¶ [0013], Fig. 5). With respect to claim 7, AURORA’s compatibility identifier and cluster logic inherently link item details and compatible items, supporting comparison of online order items with POS entered items (¶¶ [0010]–[0014], Fig. 2–3). With respect to claim 8, HIGH teaches comparing data associated with unavailable objects to available substitute objects based on dynamically learned matching behavior (¶¶ [0012]–[0015], Fig. 1, claim 1), analogous to applying rules in an Equivalency Machine. With respect to claim 9, HIGH29 teaches dynamically learned matching behavior and replacement of unavailable items with acceptable substitutes, which would include approval or denial (¶¶ [0012]–[0013], Fig. 2). With respect to claim 10, HIGH discloses notification to a user or operator when a substitute item is selected or approved (¶ [0015], Fig. 3). With respect to claim 11, notifying a cashier of approved/denied substitutes would have been obvious using conventional messaging in networked substitution systems (AURORA, ¶¶ [0014]–[0015]). With respect to claim 12, HIGH teaches a networked substitution system with a central computing system in communication with hand-held devices (¶¶ [0012]–[0016], Fig. 1), which inherently suggests comparing substituted items with unavailable items in a POS network. With respect to claim 13, AURORA teaches that item and compatibility data include both static descriptors and dynamically generated matching results (¶¶ [0013]–[0015]). With respect to claim 14, AURORA’s compatibility database and matching routines disclose comparing stored item attributes with query results (¶¶ [0010]–[0014], Fig. 2–3). With respect to claim 15, AURORA teaches notification modules that send updates to devices upon matching or substitution events (¶ [0015], Fig. 3). With respect to claim 16, monitoring orders, recording metrics, and managing rule sets is an obvious extension of HIGH29’s dynamically learned substitution system (¶¶ [0012]–[0013]). With respect to claim 17, generating reports of compared items is routine once matching outcomes are determined, as disclosed in AURORA (¶¶ [0014], [0035]). With respect to claim 18, AURORA teaches querying item attributes (¶¶ [0009]–[0014], Fig. 1–3) and HIGH/HIGH29 teach determining acceptable substitutes based on dynamically learned behavior (¶¶ [0012]–[0015]), making verification of picked items vs. online orders obvious. With respect to claim 19, integrating a weight scale for verification is a conventional enhancement; the dynamic matching and substitution framework of HIGH and AURORA make this an obvious implementation (¶¶ [0012]–[0015], [0014]–[0015]). 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

Sep 20, 2024
Application Filed
Jan 10, 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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