Prosecution Insights
Last updated: April 19, 2026
Application No. 17/870,881

SYSTEMS AND METHODS FOR DETERMINING TARGET POPULATIONS FOR STATISTICAL EXPERIMENTS

Final Rejection §101
Filed
Jul 22, 2022
Examiner
HUANG, MIRANDA M
Art Unit
2124
Tech Center
2100 — Computer Architecture & Software
Assignee
Capital One Services LLC
OA Round
2 (Final)
59%
Grant Probability
Moderate
3-4
OA Rounds
4y 7m
To Grant
99%
With Interview

Examiner Intelligence

Grants 59% of resolved cases
59%
Career Allow Rate
149 granted / 253 resolved
+3.9% vs TC avg
Strong +54% interview lift
Without
With
+53.5%
Interview Lift
resolved cases with interview
Typical timeline
4y 7m
Avg Prosecution
22 currently pending
Career history
275
Total Applications
across all art units

Statute-Specific Performance

§101
14.6%
-25.4% vs TC avg
§103
47.9%
+7.9% vs TC avg
§102
23.3%
-16.7% vs TC avg
§112
9.0%
-31.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 253 resolved cases

Office Action

§101
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 . This office action is in response to amendment filed 11/6/2025. Claims 1-20 are pending. Response to Arguments Applicant’s arguments, filed 11/6, with respect to claims have been fully considered and are persuasive. The USC 103 rejection has been withdrawn. Applicant's arguments filed 11/6 have been fully considered but they are not persuasive. USC 101 claim eligibility Regarding the claim amendment “convert, using machine learning models comprising a bidirectional encoder representations from transformers (BERT) model and a SOL query learning model, the natural-language hypothesis into a standardized query by: identifying one or more target parameters associated with the target population, determining whether the hypothesis is associated with a causal impact experiment or an optimization experiment, and the machine learning models are configured to determine whether a Simpson's paradox exists within the statistical experiment” in claim 1 (similarly, claims 10 and 16) In response. Steps, identifying and determining, may be performed in the human mind using observation and evaluation and thus, fall under mental processing. The step “to determine whether a Simpson's paradox exists within the statistical experiment” may comprise computing a Simonson’s paradox index and making a comparison, where the former falls within a math concept category while the latter falls under mental processing. In step 2a prong 2, regarding “using machine learning models comprising a Bidirectional Encoder Representations from Transformers (BERT) model and a SOL query learning model” and “the machine learning models are configured”, the BERT and the SOL query model, as recited at a high level, amount to mere instructions to implement an abstract idea without limiting how those models function. In step 2b, the models are at best the equivalent of merely adding the words “apply it” to the judicial exception. The claim limitation “the stored query comprising a Boolean query” fails to applying the judicia exception beyond generally linking and thus, falls within field of use. Regarding in claim 4, “the instructions, when executed by the one or more processors are configured to cause the system to: generate a graphical user interface indicating the identification of the Simpson's paradox within the statistical experiment, and training the one or more machine learning models” In response. Generating a graphic user interface, as broadly recited, amounts to activating a GUI or displaying results to end users by a generic computer, which is a well understood or WURC activity (e.g., Kaukanen). The step, training, as recited at high level, may improve the performance of the math models, but not the functioning of a computer. Such model training is a well understood or WURC activity (e.g., Kaukanen). 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1: Step 1: The claim recites a system. Step 2a, prong 1. The claim recites an abstract idea. Steps, determine whether the one or more target parameters match a stored query beyond a predetermined threshold, predict a sample size for the statistical experiment based on the target population and the one or more target metrics, falls under mental processing. The claim limitation does not describe how the target population is used (e.g., in training) and may be just represented by a numerical value in calculation. Steps, identifying and determining, as in “identifying one or more target parameters associated with the target population” and “determining whether the hypothesis is associated with a causal impact experiment or an optimization experiment”, may be performed in the human mind using observation and evaluation and thus, fall under mental processing. The step “to determine whether a Simpson's paradox exists within the statistical experiment” may be performed by a processor including computing a Simonson’s paradox index and making a comparison, where the former falls within a math concept category while the latter falls under mental processing. Step 2a, prong 2. This judicial exception is not integrated into a practical application. Addition elements include processors, a memory, a user device, receive, from a user device, a natural-language hypothesis associated with a statistical experiment and a target population, the hypothesis comprising one or more target metrics, receive, from the user device, one or more target parameters associated with the target population, responsive to the one or more target parameters matching the stored query, query, using the stored query, a user database to determine the target population that satisfies the one or more target parameters, the stored query comprising a Boolean query, and transmit, to the user device, a graphical user interface comprising the predicted sample size for the statistical experiment. The generic processors and memory are used to perform computing and storage, the user interface perform generic data display and the user device is only used to implement the data input and output. They amount to no more than mere instructions to apply the exception using a generic computer. Similarly, “using machine learning models comprising a bidirectional encoder-representations from transformers (BERT) model and a SOL query learning model” and “the machine learning models are configured” provide nothing more than mere instructions to implement an abstract idea. The BERT and SOL query model are used to generally apply the abstract idea without limiting how these models are functioning. The claim limitation “the stored query comprising a Boolean query” merely recites a data type and does not apply the judicia exception beyond generally linking. Receiving data and transmitting data to a graphic user interface are mere data gathering and passing recited at a high level of generality and thus, are insignificant extra solution activity. Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The processors, memory, user device and the interface are merely used as a tool to facilitate implementation of the claimed steps (MPEP 2106.05(f)). The BERT and SOL query model are at best equivalent of merely adding the words “apply it” to the judicial exception. Querying, as broadly recited, is a well under stood activity (e.g., Kaukanen). A query comprising a Boolean query as recited at high level falls under field of use. Receiving and transmitting amount to receiving or transmitting data over a network, which data are a well understood or WURC activity (Berkheimer). Even when considered in combination, the additional elements represent mere instructions to apply an exception, insignificant extra-solution activity, field of use and a WURC activity, which do not provide an inventive concept. Therefore, claim 1 is not eligible. Claim 10: Step 1: The claim recites a system. Step 2a, prong 1. The claim recites an abstract idea. Steps, determine whether the one or more target parameters match a stored query beyond a predetermined threshold, predict a sample size for the statistical experiment based on the target population and the one or more target metrics, iteratively determine whether the degradation metric has been exceeded while the statistical experiment is active, and in response to the degradation metric being exceeded or that a Simpson’s paradox exits, amount to observation and evaluation in human mind and falls under mental processing. The claim limitation does not describe how the target population is used (e.g., in training) and it may be just represented by a numerical value in calculation. The iterative operation falls under a math concept. Steps, identifying and determining, as in “identifying one or more target parameters associated with the target population” and “determining whether the hypothesis is associated with a causal impact experiment or an optimization experiment”, may be performed in the human mind using observation and evaluation and thus, fall under mental processing. The step “to determine whether a Simpson's paradox exists within the statistical experiment” may be performed by a processor including computing a Simonson’s paradox index and making a comparison, where the former falls within a math concept category while the latter falls under mental processing. Step 2a, prong 2. This judicial exception is not integrated into a practical application. Addition elements include processors, a memory, a user device, receive, from a user device, a natural-language hypothesis associated with a statistical experiment and a target population, the hypothesis comprising one or more target metrics, receive, from the user device, one or more target parameters associated with the target population, responsive to the one or more target parameters matching the stored query, query, using the stored query, a user database to determine the target population that satisfies the one or more target parameters, the stored query comprising a Boolean query, transmit, to the user device, a graphical user interface comprising the predicted sample size for the statistical experiment, initialize the statistical experiment, and receive, from the user device, a selection of a degradation metric stored on a degradation metric database, the selected degradation metric associated with the statistical experiment, end the statistical experiment, and update the graphical user interface to indicate that the degradation metric has been exceeded or that a Simpson’s paradox exists. The generic processors and memory are used to perform computing and storage, the user interface perform generic data display and the user device is only used to implement the data input and output. Similarly, “using machine learning models comprising a bidirectional encoder-representations from transformers (BERT) model and a SOL query learning model” and “the machine learning models are configured” provide nothing more than mere instructions to implement an abstract idea. The BERT and SOL query model are used to generally apply the abstract idea without limiting how these models are functioning. The conditions, to initialize the statistical experiment and end the statistical experiment, as broadly recited, amount to passing values or instructions. The claim limitation “the stored query comprising a Boolean query” merely recites a data type and does not apply the judicia exception beyond generally linking. Receiving the selected degradation metric and transmitting data to a graphic user interface are mere data gathering and passing recited at a high level of generality and thus, are insignificant extra solution activity. Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The processors, memory, user device and the interface are merely used as a tool to facilitate implementation of the claimed steps (MPEP 2106.05(f)). The BERT and SOL query model are at best equivalent of merely adding the words “apply it” to the judicial exception. Querying is a well understood activity (e.g., Kaukanen). A query comprising a Boolean query as recited at high level falls under field of use. Initializing the statical experiment and ending the statistical experiment amount to receiving and passing values, which is a well understood or WURC activity (e.g., Fabijanr). Updating data on a use interface as recited at high level is a well understood activity (Lenth, Fabijan). Receiving and transmitting amount to receiving or transmitting data over a network, which data are a well understood or WURC activity (Berkheimer). When considered in combination, the additional elements represent mere instructions to apply an exception, insignificant extra-solution activity, field of use and a WURC activity, which do not provide an inventive concept. Therefore, claim 10 is not eligible. Claim 16: Step 1: The claim recites a system. Step 2a, prong 1. The claim recites an abstract idea. Steps, determine whether the one or more target parameters match a stored query beyond a predetermined threshold, predict a sample size for the statistical experiment based on the target population and the one or more target metrics, determine whether the hypothesis is associated with an optimization experiment or a causal impact experiment, determining the hypothesis is associated with the causal impact experiment, and determining the hypothesis is associated with the optimization experiment, falls under mental processing. The claim limitation does not describe how the target population is used (e.g., in training) and may be just be represented by a numerical value in calculation. Steps, identifying and determining, as in “identifying one or more target parameters associated with the target population” and “determining whether the hypothesis is associated with a causal impact experiment or an optimization experiment”, may be performed in the human mind using observation and evaluation and thus, fall under mental processing. The step “to determine whether a Simpson's paradox exists within the statistical experiment” may be performed by a processor including computing a Simonson’s paradox index and making a comparison, where the former falls within a math concept category while the latter falls under mental processing. Step 2a, prong 2. This judicial exception is not integrated into a practical application. Addition elements include processors, a memory, a user device, receive, from a user device, a natural-language hypothesis associated with a statistical experiment and a target population, the hypothesis comprising one or more target metrics, receive, from the user device, one or more target parameters associated with the target population, responsive to the one or more target parameters matching the stored query, query, using the stored query, a user database to determine the target population that satisfies the one or more target parameters, and transmit, to the user device, the stored query comprising a Boolean query, a graphical user interface comprising the predicted sample size for the statistical experiment, perform a first statistical experiment type selected from an A/B statistical test and a sequential statistical test in response to determining, and perform a second statistical experiment type comprising a multi-arm bandit statistical test in response to determining. The generic processors and memory are used to perform computing and storage, the user interface perform generic data display and the user device is only used to implement the data input and output. Similarly, “using machine learning models comprising a bidirectional encoder-representations from transformers (BERT) model and a SOL query learning model” and “the machine learning models are configured” provide nothing more than mere instructions to implement an abstract idea. The BERT and SOL query model are used to generally apply the abstract idea without limiting how these models are functioning. The claim limitation “the stored query comprising a Boolean query” merely recites a data type and does not apply the judicia exception beyond generally linking. Receiving a natural-language hypothesis and transmitting data to a graphic user interface amount to data gathering and passing, an extra solution activity. The A/B test and multi-arm bandit are math concepts. Adding an abstract idea (i.e., the math concept) to another abstract idea (e.g., operations under Step 2a, prong 1) does not make the claim non-abstract. (RecogniCorp) Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The processors, memory, user device and the interface are merely used as a tool to facilitate implementation of the claimed steps (MPEP 2106.05(f)). The BERT and SOL query model are at best equivalent of merely adding the words “apply it” to the judicial exception. A query comprising a Boolean query as recited at high level falls under field of use. Querying is a well under stood activity (e.g., Kaukanen). Performing A/B test or multi-arm bandit is a WURC activity (Kaukanen). Receiving and transmitting data amount to receiving and transmitting data over a network, which is a well understood or WURC activity (Berkheimer). When considered in combination, the additional elements represent mere instructions to apply an exception, insignificant extra-solution activity and a WURC activity, which do not provide an inventive concept. Therefore, claim 16 is not eligible. Dependent claims recite further claim limitation, in claims 2 and 14, providing as election (field of use), in claim 3, determining, preselecting A/B and multi-arm bandit (math concept), in claim 4, “the instructions, when executed by the one or more processors are configured to cause the system to: generate a graphical user interface indicating the identification of the Simpson's paradox within the statistical experiment (as broadly recited, generating may be only activating the interface process or displaying results to end users by a generic computer, a WURC activity (e.g., Kaukanen)), and training the one or more machine learning models” (training, as recited at high level, may improve the performance of the math models, but not the functioning of a computer and model training is a well understood or WURC activity (e.g., Kaukanen)), in claim 5, types of target metrics (field of use), in claims 6, 13 and 17, request, receiving and storing queries (WURC, Kaukanen), in claims 7 and 18, receive, from the user device, a selection of a degradation metric stored on a degradation metric database, the selected degradation metric associated with the statistical experiment (receiving data is extra solution data gathering activity and receiving as recited at high level, amounts to receiving and transmitting data in Berkheimer); initialize the statistical experiment (initializing is a WURC, Fabijan)); iteratively determine whether the degradation metric has been exceeded while the statistical experiment is active (determining falls under mental processing, where iteration as broadly recited falls under a math concept); and in response to the degradation metric being exceeded, end the statistical experiment (ending is a WURC, Fabijan) and update the graphical user interface to indicate that the degradation metric has been exceeded (Updating data on a use interface as recited at high level is a well understood activity (Lenth, Fabijan)), in claims 8, 11 and 19, receiving and providing (WURC, Berkheimer) and estimating a time period (mental processing), in claims 9 and 12 and 20, identify subpopulation to achieve greater effect size (as broadly recited, mental processing), in claim 15, determining A/B or bandit test (mental processing), initializing either test (WURC, Fabijan). Dependent claims are not eligible. 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 LiWu Chang whose telephone number is (571)270-3809, email: li-wu.chang@uspto.gov. The examiner can normally be reached on M-F. 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, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. /LI WU CHANG/Primary Examiner, Art Unit 2124 January 6, 2026
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Prosecution Timeline

Jul 22, 2022
Application Filed
Aug 04, 2025
Non-Final Rejection — §101
Oct 27, 2025
Interview Requested
Nov 04, 2025
Examiner Interview Summary
Nov 04, 2025
Applicant Interview (Telephonic)
Nov 06, 2025
Response Filed
Jan 13, 2026
Final Rejection — §101
Mar 02, 2026
Interview Requested
Mar 16, 2026
Interview Requested

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

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Prosecution Projections

3-4
Expected OA Rounds
59%
Grant Probability
99%
With Interview (+53.5%)
4y 7m
Median Time to Grant
Moderate
PTA Risk
Based on 253 resolved cases by this examiner. Grant probability derived from career allow rate.

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