Prosecution Insights
Last updated: April 19, 2026
Application No. 17/986,886

AI-Based Real-Time Prediction Engine Apparatuses, Methods and Systems

Final Rejection §101
Filed
Nov 14, 2022
Examiner
DUCK, BRANDON M
Art Unit
3693
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Fmr LLC
OA Round
5 (Final)
64%
Grant Probability
Moderate
6-7
OA Rounds
2y 7m
To Grant
83%
With Interview

Examiner Intelligence

Grants 64% of resolved cases
64%
Career Allow Rate
214 granted / 332 resolved
+12.5% vs TC avg
Strong +19% interview lift
Without
With
+18.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 7m
Avg Prosecution
47 currently pending
Career history
379
Total Applications
across all art units

Statute-Specific Performance

§101
47.9%
+7.9% vs TC avg
§103
21.9%
-18.1% vs TC avg
§102
9.6%
-30.4% vs TC avg
§112
13.3%
-26.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 332 resolved cases

Office Action

§101
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 . Status of Claims This action is in reply to the Response filed on 1/26/2026. Claims 19-36 are currently presented. This action is made Final. Claim Objections Claim 28 is objected to because of the following informalities: “order placement datastructure” should be written as “order placement request datastructure." Appropriate correction is required. 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 19-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more. Under the broadest reasonable interpretation, the following claim terms are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. MPEP § 2111. The claims recite “inferred labels.” “Inferred labels” are used in real-time prediction logic to predict classification or category assigned to a data point by a machine learning model. The claims recite “datastructure.” Applicant has not acted as their own lexicographer in defining the claim term “datastructure”. For Applicant to be their own lexicographer, “the patentee's lexicography must, of course, appear ‘with reasonable clarity, deliberateness, and precision’ before it can affect the claim.” Renishaw PLC v. Marposs Societa' per Azioni, 158 F.3d 1243, 1249 (Fed.Cir.1998) (quoting In re Paulsen, 30 F.3d 1475, 1480 (Fed.Cir.1994)) (emphasis added). Under BRI, examiner determines datastructure as a way of formatting data so that it can be used by a computer program or other system. The “order placement request” are interpreted as intended use, because it is a mere label to describe the intended use of the claimed datastructure MPEP § 2103(I)(C). Claims 19-22 recite the term “component collection.” In machine learning, a "component" typically refers to a self-contained, reusable unit of code or a distinct part of the machine learning process or system, often used to build more complex pipelines or models. Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03) Claim 19 recites a product (apparatus), which is a statutory category of invention (Step 1: YES). Claim 20 recites a product (apparatus), which is a statutory category of invention (Step 1: YES). Claim 21 recites a system, which is a statutory category of invention (Step 1: YES). Claim 22 recites a process, which is a statutory category of invention (Step 1: YES). Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a)). Yes. The claims are analyzed to determine whether it is directed to a judicial exception. Claim 19, 20, 21, and 22 recite comprising: a set of trade tick data messages, in which the set of trade tick data messages pertains to a security identifier; determine, a set of inferred labels for each obtained trade tick data message using a real-time prediction logic generated using a machine learning technique; compute, a real-time liquidity model for the security identifier using the determined inferred labels; assess, counterparty landscape for the security identifier using the real- time liquidity model; detect, information leakage associated with the security identifier based on the assessment of the counterparty landscape; determine, an order placement allocation for the security identifier based on the detected information leakage; and send, an order placement request datastructure for a first order specified by the order placement allocation to a server associated with a first venue specified by the order placement allocation for the first order. These limitations, as drafted, under its broadest reasonable interpretation, covers performance via certain methods of organizing human activity, but for the recitation of generic computer components. Under human activity, the limitations are commercial interactions, specifically sales activities and business relations. Also, the limitations are managing interactions between people, specifically following instructions. Accordingly, the claim recites an abstract idea. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: Yes. The claims recite an abstract idea). Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d)). No. This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of an artificial intelligence-based information leakage order generating apparatus (non-transient medium and system), component collection, memory, and processor. The additional elements of an artificial intelligence-based information leakage order generating apparatus (non-transient medium and system), component collection, memory, and processor, are just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)). The computer components are recited at such a high-level of generality (i.e. as a generic computer components) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. (Step 2A-Prong 2: NO. The judicial exception is not integrated into a practical application). Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05). No. The claims are next analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements of (an artificial intelligence-based information leakage order generating apparatus (non-transient medium and system), component collection, memory, and processor) in the claims amount to no more than mere instructions to apply the exception using a generic computer component and generally linking the use of GUI’s to judicial exception. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claims do not amount to significantly more than the recited abstract idea (Step 2B: NO; The claims do not provide significantly more, and are not patent eligible). Claim 23 recites in which the order placement allocation specifies a plurality of orders for placing in a plurality of venues. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 24 recites in which the inferred label is any one of: predicted trading algorithm type, predicted client type, predicted tier, predicted state, predicted venue, predicted limit price. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 25 recites The apparatus of claim 19, in which the real-time liquidity model includes specified buckets having time buckets having a specified size. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 26 recites in which the information leakage is an allocation information leakage alert generated based on determining that the predicted trading algorithm type corresponds to the trading algorithm type associated with the trading algorithm identifier. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 27 recites further, comprising: modify, the order placement allocation for the security identifier using a different trading algorithm; and send, an order placement request datastructure for a second order specified by the modified order placement allocation. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 28 recites in which the order placement datastructure further includes a limit price, and in which the specified buckets are price buckets. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 29 recites in which the information leakage alert is a limit price information leakage alert generated based on determining that the predicted limit price is within a threshold of the limit price. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 30 recites further, comprising modify, the order placement allocation for the security identifier for use of a different limit price; and send, an order placement request datastructure for a second order having the different limit price. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 31 recites further, comprising: compute, a real-time liquidity model for the security identifier using the determined inferred labels; cassess, via counterparty landscape for the first order using the real-time liquidity model; and modify, an internal state of the trading algorithm associated with the trading algorithm identifier based on the counterparty landscape. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 32 recites in which the order placement allocation for the security identifier is further determined based on the modified internal state of the trading algorithm. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 33 recites further, comprising: modify, the order placement allocation for the security identifier based on the modified internal state of the trading algorithm; and send, an order placement request datastructure for a second order specified by the modified order placement allocation. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 34 recites in which the real-time liquidity model for the security identifier indicates at least one of: percentages of orders that are executed in different markets, percentages of orders that are executed using different trading algorithms. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 35 recites, in which the machine learning technique used to for generating the real-time prediction logic is one of: random forest, gradient boosting, decision tree, logistic regression. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Claim 36 recites further, comprising: retrieve, historical trade tick data; retrieve, historical quote data; augment, the historical trade tick data with the historical quote data, in which an as of join of the historical quote data and the historical trade tick data is performed; retrieve, historical order execution data; determine, venues for the historical order execution data; determine, time buffers for the historical order execution data, in which each venue is associated with a separate time buffer; generate, labeled trade tick data, in which an as of join of the historical order execution data and the augmented historical trade tick data is performed; select, a subset of the labeled trade tick data that avoids overfitting using sampling stratified over a set of buckets; and -train, the real-time prediction logic using the machine learning technique and the selected subset of the labeled trade tick data. These limitations are also part of the abstract idea identified in claim 19, and are similarly rejected under the same rationale as claim 19, supra. Response to Arguments Applicant's arguments filed 1/26/2025 have been fully considered but they are not persuasive. Applicant also argues a recent Director Squires policy September 26, 2025 memo (Arguments, pg. 12). Examiner notes, that the memo did not change USPTO Examiner guidance regarding patent eligibility, and did not say that all AI patent applications are eligible. Examiner notes that the Ex Parte Desjardins decision (as well as the Director Squires’ memo) stressed the specification of application 16/319040, in that the specification was curing a deficiency in the way that normal AI functions, and their specific way of training the AI had an improvement in the AI; the decision had nothing to do with the data itself. In the currently recited claims, the “invention” is in the data itself, and it is using AI at a “high level” which amounts to “apply it,” where there is no curing any technical problem with the way AI functions, and is just using a different set of data. Thus, the application (and currently recited claims) are more like Recentive, than Ex Parte Desjardins. Applicant argues that software in and of itself is not abstract (Pg. 22, Para. 31). The Courts ruling are true and define to what is or is not abstract. Even still, the currently recited claims are abstract as noted in the above rejection. As noted in the above rejection, all of the claim limitations, including structuring “the leakage order apparatus,” “machine learning technique,” and “counterparty landscape”, are certain methods of organizing human activity, such as commercial interactions and managing interactions between people. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea. Further, as noted above, an artificial intelligence-based information leakage order generating apparatus, is just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)). Applicant's argument that the rejection lacks Berkheimer evidence is not persuasive (Para. 33). Such evidence is only required to support a conclusion that an additional element is well-understood, routine, conventional activity. Here, the rejection does not assert well-understood, routine, conventional activity and instead identifies the additional elements drawn to the database as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. MPEP 2106.05(f). Because the evaluation in Step 2B is not a weighing test, it is not important how the elements are characterized or how many considerations apply from the list of considerations set forth in MPEP 2106.05. It is important to evaluate the significance of the additional elements relative to the invention, and to keep in mind the ultimate question of whether the additional elements encompass an inventive concept. Applicant also argues (Para. 34) that Examiner must consider the machine-or-transformation test of Bilski as evidence of “significantly more.” While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. MPEP 2106 (I). Applicant argues various claim limitations in view of the “broadest reasonable claim interpretation (Applicant arguments, para. 35). Examiner notes that “the leakage order apparatus,” “machine learning technique,” and “counterparty landscape” are all addressed in the above rejection. Applicant argues that the currently recited claims are a practical application (Applicant arguments, Para. 35). Examiner disagrees. As noted in the above rejection, all of the claim limitations, including structuring “the leakage order apparatus,” “machine learning technique,” and “counterparty landscape”, are certain methods of organizing human activity, such as commercial interactions and managing interactions between people. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea. Further, as noted above, an artificial intelligence-based information leakage order generating apparatus, is just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)). The currently recited claims recite how a typical machine learning model works, using specific attributes and parameters. However, the claims do not describe any particular improvement in the manner of computer functions. Although a machine learning model is used for the purposes of determining liquidity for trading order placements, such uses is both generic and conventional. The object of the claims is to determine liquidity for trading order placements, not to produce technology enabling a machine learning model to operate. The claims call for generic use of such a machine learning model in the manner such models conventionally operate. Simply reciting a particular technological module or piece of equipment in a claim does not confer eligibility. The MPEP notes this distinction. The MPEP notes this distinction (For example, in MPEP 2106.05(f)(I), it states: Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743). In the instant application, the currently recited claims use machine learning as generic data processing. Applicant argues that the claims amount to “significantly more” than the abstract idea (Applicant arguments, Para. 32). Examiner disagrees. Applicant argues that the currently recited claim limitations (such as, “artificial intelligence-based information leakage order generating apparatus,” “using a real-time prediction logic generated using a machine learning technique,” and [to] “assess counterparty landscape for the security identifier using the real-time liquidity model, detect information leakage associated with the security identifier based on the assessment of the counterparty landscape,” are even more technical in nature than Berkenheimer, Amdocs, etc. (Applicant arguments, Para. 45). Examiner disagrees. The steps in the current claims are purely business interaction that can be achieve on a face-to-face level or over the telephone or over generic network environment. This is purely a commercial interaction under the certain methods of organizing human activity. Applicant argues that the Examiner must accept Applicants evidence of “significantly more” by failing to provide a factual determination to the contrary (Applicant arguments, Para. 52). Examiner disagrees. Applicant's argument that the rejection lacks Berkheimer evidence is not persuasive. Such evidence is only required to support a conclusion that an additional element is well-understood, routine, conventional activity. Here, the rejection does not assert well-understood, routine, conventional activity and instead identifies the additional elements drawn to the database as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. MPEP 2106.05(f). Because the evaluation in Step 2B is not a weighing test, it is not important how the elements are characterized or how many considerations apply from the list of considerations set forth in MPEP 2106.05. It is important to evaluate the significance of the additional elements relative to the invention, and to keep in mind the ultimate question of whether the additional elements encompass an inventive concept. Applicant also argues (Para. 57), that Examiner must consider the machine-or-transformation test of Bilski as evidence of “significantly more.” While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. MPEP 2106 (I). Step 2B does not require Bilski per se, rather, it considers the limitations individually and as an ordered combination to determine if the claimed invention amount to “significantly more” than the abstract idea. In this case, it does not. MPEP 2106.05(b) clearly says “("[I]n Mayo, the Supreme Court emphasized that satisfying the machine-or-transformation test, by itself, is not sufficient to render a claim patent-eligible, as not all transformations or machine implementations infuse an otherwise ineligible claim with an 'inventive concept.'"). Conclusion 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 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 BRANDON M DUCK whose telephone number is (469)295-9049. The examiner can normally be reached 8am - 5pm. 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, Michael Anderson can be reached at 571-270-0508. 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. /BRANDON M DUCK/Examiner, Art Unit 3693 /Mike Anderson/Supervisory Patent Examiner, Art Unit 3693
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Prosecution Timeline

Nov 14, 2022
Application Filed
Nov 14, 2022
Response after Non-Final Action
May 08, 2023
Non-Final Rejection — §101
Nov 13, 2023
Response Filed
Feb 14, 2024
Non-Final Rejection — §101
Aug 20, 2024
Response Filed
Nov 05, 2024
Final Rejection — §101
May 15, 2025
Request for Continued Examination
May 21, 2025
Response after Non-Final Action
Jul 22, 2025
Non-Final Rejection — §101
Jan 26, 2026
Response Filed
Mar 26, 2026
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

6-7
Expected OA Rounds
64%
Grant Probability
83%
With Interview (+18.9%)
2y 7m
Median Time to Grant
High
PTA Risk
Based on 332 resolved cases by this examiner. Grant probability derived from career allow rate.

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