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 action is in response to the Request for Continued Examination filed 4/23/2026. Claim(s) 1, 5, 14 has/have been amended. Claim(s) 2, 4, 6, 10-13, 18-19, 23 has/have been canceled. Claims 1, 3, 5, 7, 8, 9, 14, 15, 16, 17, 20, 21, 22 are pending and have been considered below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/23/2026 has been entered.
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.
Claim(s) 1, 5 and 14 is/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.
Does Claim 1 fall within one of the statutory categories of invention? Claim 1 is directed to a computing system comprising one or more processor and one or more memories, which is a statutory category of invention. Claim 5 is directed to a computer implemented method, which is a statutory category of invention. Claim 14 is directed to one or more non-transitory computer-readable storage media, which is a statutory category of invention.
Do the claims include a judicial exception? Claim(s) 1, 5 and 14 is/are directed to an abstract idea. The claim(s) is/are directed in part to generating a first recommendation related to an allocation of a first permission to an identity, indicating the first recommendation, determining a first condition, and attributing the condition to the identity at a future time period.
Does the judicial exception fall within one of the abstract idea groupings? The abstract idea is not meaningful different from determining a future recommended permission for an entity based on a condition. The claims amount to no more than the abstract idea of comparing new and stored information and using rules to identify options and organizing and manipulating information through mathematical correlations (based on speculative execution of a machine learning model). This concept is not meaningfully different from those concepts found by the courts to be abstract (see: Smartgene, Digitech).
Are there additional elements beyond the judicial exception? Claim(s) 1 recite(s) the additional elements of a computing system comprising processors and memories. Claim(s) 5 recite(s) a computer-implemented method. Claim(s) 14 recite(s) one or more non-transitory computer-readable storage media having stored thereon computing instructions that, upon execution by one or more computing devices, cause the one or more computing devices to perform operations.
Applicant amended Claim 1 with the limitation “computer generated hypothetical future condition”.
Do the additional elements individually or in combination with the claim as a whole integrate the judicial exception into a practical application? This judicial exception is not integrated into a practical application. The courts have recognized various implementations as integrating abstract ideas into a practical application. For example, if the overall claim limitations including the judicial exception improve the functioning of a computer or other technology or technical field, or if implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture is integral to the claim, or if the overall claim limitations including the judicial exception effect a transformation or reduction of a particular article to a different state or thing, then the courts have recognized that the judicial exception is integrated into a practical application. However, the judicial exception in the claims merely making a recommendation for a user based on speculation of a condition in a future time period, but does not improve a computer, another technology or other technical field, is not implemented with or in conjunction with a machine or manufacture that is integral to the claim, or does not effect a transformation or reduction of a particular article to a different state or thing. Therefore, the judicial exception is not integrated into a practical application.
Does the claim provide an inventive concept, i.e. does the claim recite additional elements or a combination of elements that amount to significantly more than the judicial exception? The additional elements in the claims include a machine learning model in addition to a computing system comprising one or more processors and memories, and one or more non-transitory computer-readable storage media. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
The additional elements of an identity management system simply identify the system on which the method executes and does not add significantly more to render the claims to more than the judicial exception. The additional limitation of using input that reflects at least one hypothetical future condition among the at least one hypothetical future conditions is nothing more that data that can be input by a user. The limitation, the machine learning model is tested based on one or more speculative executions to confirm that the machine learning model satisfies a selected consistency benchmark, simply identifies a step in the method but provides no description for how the speculative executions are carried out. Therefore, the additional elements do not add significantly more or integrate the judicial exception into a practical application.
The additional element “computer generated hypothetical future condition” does not represent integration into a practical application or significantly more than the judicial exception because the claim does not describe the computer generated hypothetical future condition in such a way that the hypothetical future condition must be generated by a computer and not by the human mind. In order for the claimed hypothetical future condition to represent significantly more than the judicial exception, the claim must describe the computer generation of the hypothetical future condition such that hypothetical future condition could only be generated by a computer.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim(s) 1, 5 and 14 is/are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Dependent Claims 2-4, 6-12, 15-20 do not appear to include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3, 5, 7, 8, 9, 14, 15, 16, 17, 20, 21, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ip et al. (US 2022/0271938 A1) in view of Adderly et al. (US 9,472115 B2) and further in view of Ko (US 2011/0082766 A1).
Claim 1. Ip discloses a computing system comprising: one or more processors; and one or more memories having stored therein instructions that, upon execution by the one or more processors, cause the computing system to perform operations comprising:
generating, by a machine learning model, a permission controller uses machine learning analysis to determine if a permission should be suspended or maintained (P 0068),
a first recommendation relating to allocation of a first permission to an identity in an identity management system that manages access right permissions to computing resources, a user associated with a permission and/or application, etc. may be a registered user (P 0016) permissions for an application may be assigned/edited/revoked by the administrator (e.g., during deployment of an application and/or after an application is deployed), a credential (e.g., a username/password, an API key, an authentication key, a certificate, etc.) may be assigned/associated with one or more permissions that authorize a user of the credential to take an action (e.g., to retrieve data using an interface) (P 0061),
wherein the first recommendation is for the identity to retain the first permission, an initial permission is assigned to a user (P 0013, 0017) a user registers with an application (P 0016) a user is identified with an entity (P 0022), or
for the first permission to be deallocated from the identity, a permission monitor determines if a user’s permission should be maintained or suspended (P 0031);
providing, to one or more users, a first indication of the first recommendation, a notification is provided to a user (P 0017) or an administrator that a permission has been selected for deactivation (P 0073) a user may be presented with a notification to approve or reject a suspension (P 0105) and a decision may be made prior to suspension of a permission (P 0107);
determining, based on a speculative execution of the machine learning model, the speculative execution comprising automatically executing the machine learning model … to output a recommendation, a first condition … that, when attributed to the identity, causes changing of the first recommendation to a second recommendation relating to the allocation of the first permission to the identity, wherein the second recommendation differs from the first recommendation, the threshold period of time may be determined based on historical statistical and/or patterns of access (P 0016) determining that one or more permissions should be suspended or maintained using weights determined using machine learning analysis of the available information associated with applications and permissions in the e-commerce platform (P 0068) a criteria is evaluated to determine if the permission is maintained or suspended (P 0088) a user may be presented with a notification to approve or reject a suspension (P 0105) each determination may be associated with a score and that total score may be compared with a threshold utilizing a machine learning algorithm to determine such weighting and/or thresholds based on historical data (P 0107), Applicant’s specification discloses that speculative execution refers to an evaluation made based on a condition that has not actually occurred (i.e. is merely theoretical) at the time the evaluation is made; the threshold period in Ip is determined based on historical statistical and/or patterns of access to determine if a permission is maintained or suspended, that is, the evaluation for maintaining or suspending a permission is not based on currently evaluated data, but on historical data,
wherein the first condition is attributed to the identity as occurring in a future time period, if an application is expected to access an API within 24 hours of a new order transaction, but the site is not busy and may not see an order transaction for 90 days, the event-based threshold ensures that the application's permission is not suspended after, for example, more than 30 days passes between accesses (even though the application would access the API within 24 hours of each new order transaction) (P 0070) an application that facilitates preparation of annual tax information may be expected to access an interface only once a year and may be associated with a 13 month threshold whereas an application that analyzes customer details to detect fraud may be expected to access data every day and/or access data within a period of time from a last transaction and will be associated with a threshold of one day or a threshold that is equal the time since a last transaction plus one day (P 0102). An application is registered to a user, and it is clear that an expected (future) behavior of an application is determined and a condition is set based on the expected behavior.
Ip does not disclose determining, based on a speculative execution of the machine learning model … using input data indicative of at least one computer generated hypothetical future conditions, a first condition among the at least one computer generated hypothetical future conditions, as disclosed in the claims. However, in the same field of invention, Adderly discloses hypothetical ontological relationships between information concepts are automatically identified based on automated analysis of evidential support for these hypothetical ontological relationships found in association with existing actual ontological relationships and information concepts (C 4 L 5-10) a set of questions for investigating the hypothetical ontological relationship may be chosen based on the information concept type(s), ontological relationship type (link type), and the like. The questions may be selected by an automated engine that has been trained using machine learning techniques to identify the type of questions to be asked when the ontological relationship being investigated involves certain information concept types and link types (C 5 L 23-30) one or more corpora of evidential data are used to generate candidate answers to a question and identifies evidence supporting and/or refuting the hypothetical ontological relationship, or links, between information concepts corresponding to a question being answered (C 6 L 36-42) evaluating future hypothetical ontological links where scoring of the hypothetical ontological links is influenced by reliability and credibility of sources of information in the corpora (C 38 L 2-14). Therefore, considering the teachings of Ip and Adderly, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine determining, based on a speculative execution of the machine learning model … using input data indicative of at least one computer generated hypothetical future conditions, a first condition among the at least one computer generated hypothetical future conditions with the teachings of Ip with the motivation to provide a more efficient system for processing large amounts of data with information gaps to find relevant relationships (Adderly: C 1 L 11-17).
Ip does not explicitly disclose providing, to the one or more users, a second indication that attribution of the first condition to the identity causes the changing of the recommendation from the first recommendation to the second recommendation, as disclosed in the claims. However, Ip discloses update events may be implemented in a subscription model, such customer creation, product changes, or order cancelation (P 0049) an access monitor may register for event notifications that a permission is accessed (P 0085) a user may be presented with a notification to approve or reject a suspension (P 0105) and a decision may be made prior to suspension of a permission (P 0107). In the same field of invention, Ko discloses if a user’s terminal satisfies requirements for account access, a notification of permission of access is issued (P 0040). Therefore, considering the teachings of Ip, Adderly and Ko, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine providing, to the one or more users, a second indication that attribution of the first condition to the identity causes the changing of the recommendation from the first recommendation to the second recommendation with the teachings of Ip and Adderly with the motivation to make Ip more flexible by providing a well-known method for notification messages for not only when permissions are suspended, but also for permission access and for when permissions are granted and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Ip does not disclose wherein the machine learning model is tested based on one or more speculative executions to confirm that the machine learning model satisfies a selected consistency benchmark, as disclosed in the claims. However, Ip discloses the application permission monitor monitors permissions to identify opportunities to suspend permissions that are inconsistent with a type of the application, etc. (P 0061). Adderly discloses a set of questions for investigating the hypothetical ontological relationship may be chosen based on the information concept type(s), ontological relationship type (link type), the questions may be selected by an automated engine that has been trained using machine learning techniques to identify the type of questions to be asked when the ontological relationship being investigated involves certain information concept types and link types (C 5 L 23-30) this process involves applying weights to the various scores, where the weights have been determined through training of the statistical model (C 19 L 66 – C 20 L 2) a weighted function of the confidence scores of the answers to the various questions, as well as the scores of the sub-links, may be utilized in which the weights for the various components of the calculation may be learned through training and machine learning (C 55 L 14-21). Therefore, considering the teachings of Ip, Adderly and Ko, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the machine learning model is tested based on one or more speculative executions to confirm that the machine learning model satisfies a selected consistency benchmark with the teachings of Ip, Adderly and Ko with the motivation to ensure the accuracy of the statistical data or patterns of Ip (Ip: P 0016).
Claim 2. Claim.
Claim 3. Ip, Adderly and Ko disclose the computing system of claim, and the combination Ip in view of Adderly further discloses wherein the operations further comprise: detecting occurrence of the at least one hypothetical future conditions; and reevaluating, by the machine learning model, based at least in part on the detecting, the first recommendation, a threshold period of time is set for a permission to be suspended if the permission has not been utilized (P 0031) or has an expired password (P 0071) the permission controller determines that it is time to analyze permissions based on a timer expiring or any other event trigger (P 0086) The expiration of a password is a future event. Adderly has been combined to reject hypothetical future conditions as in Claim 1
Claim 4. canceled.
Claim 5. Ip discloses a computer-implemented method comprising:
generating, by a machine learning model, a permission controller uses machine learning analysis to determine if a permission should be suspended or maintained (P 0068),
a first recommendation relating to allocation of a first permission to an identity in an identity management system that manages access right permissions to computing resources, a user associated with a permission and/or application, etc. may be a registered user (P 0016) permissions for an application may be assigned/edited/revoked by the administrator (e.g., during deployment of an application and/or after an application is deployed), a credential (e.g., a username/password, an API key, an authentication key, a certificate, etc.) may be assigned/associated with one or more permissions that authorize a user of the credential to take an action (e.g., to retrieve data using an interface) (P 0061),
wherein the first recommendation is for the identity to retain the first permission, an initial permission is assigned to a user (P 0013, 0017) a user registers with an application (P 0016) a user is identified with an entity (P 0022), or
for the first permission to be deallocated from the identity, a permission monitor determines if a user’s permission should be maintained or suspended (P 0031);
providing, to one or more users, a first indication of the first recommendation, a notification is provided to a user (P 0017) or an administrator that a permission has been selected for deactivation (P 0073) a user may be presented with a notification to approve or reject a suspension (P 0105) and a decision may be made prior to suspension of a permission (P 0107);
determining, based on a speculative execution of the machine learning model, the speculative execution comprising automatically executing the machine learning model … to output a recommendation, a first condition … that, when attributed to the identity, causes changing of the recommendation from the first recommendation to a second recommendation relating to the allocation of the first permission to the identity, wherein the second recommendation differs from the first recommendation, the threshold period of time may be determined based on historical statistical and/or patterns of access (P 0016) determining that one or more permissions should be suspended or maintained using weights determined using machine learning analysis of the available information associated with applications and permissions in the e-commerce platform (P 0068) a criteria is evaluated to determine if the permission is maintained or suspended (P 0088) a user may be presented with a notification to approve or reject a suspension (P 0105) each determination may be associated with a score and that total score may be compared with a threshold utilizing a machine learning algorithm to determine such weighting and/or thresholds based on historical data (P 0107),
wherein the first condition is attributed to the identity as occurring in a future time period, if an application is expected to access an API within 24 hours of a new order transaction, but the site is not busy and may not see an order transaction for 90 days, the event-based threshold ensures that the application's permission is not suspended after, for example, more than 30 days passes between accesses (even though the application would access the API within 24 hours of each new order transaction) (P 0070) an application that facilitates preparation of annual tax information may be expected to access an interface only once a year and may be associated with a 13 month threshold whereas an application that analyzes customer details to detect fraud may be expected to access data every day and/or access data within a period of time from a last transaction and will be associated with a threshold of one day or a threshold that is equal the time since a last transaction plus one day (P 0102). It is clear that an expected (future) behavior of an application is determined and a condition is set based on the expected behavior.
Ip does not disclose determining, based on a speculative execution of the machine learning model … using input data indicative of at least one computer generated hypothetical future condition … among the at least one computer generated hypothetical future conditions, as disclosed in the claims. However, in the same field of invention, Adderly discloses hypothetical ontological relationships between information concepts are automatically identified based on automated analysis of evidential support for these hypothetical ontological relationships found in association with existing actual ontological relationships and information concepts (C 4 L 5-10) a set of questions for investigating the hypothetical ontological relationship may be chosen based on the information concept type(s), ontological relationship type (link type), and the like. The questions may be selected by an automated engine that has been trained using machine learning techniques to identify the type of questions to be asked when the ontological relationship being investigated involves certain information concept types and link types (C 5 L 23-30) one or more corpora of evidential data are used to generate candidate answers to a question and identifies evidence supporting and/or refuting the hypothetical ontological relationship, or links, between information concepts corresponding to a question being answered (C 6 L 36-42) evaluating future hypothetical ontological links where scoring of the hypothetical ontological links is influenced by reliability and credibility of sources of information in the corpora (C 38 L 2-14). Therefore, considering the teachings of Ip and Adderly, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine determining, based on a speculative execution of the machine learning model … using input data indicative of at least one computer generated hypothetical future condition … among the at least one computer generated hypothetical future conditions with the teachings of Ip with the motivation to provide a more efficient system for processing large amounts of data with information gaps to find relevant relationships (Adderly: C 1 L 11-17).
Ip does not explicitly disclose providing, to the one or more users, a second indication that attribution of the first condition to the identity causes the changing of the first recommendation to the second recommendation, as disclosed in the claims. However, Ip discloses update events may be implemented in a subscription model, such customer creation, product changes, or order cancelation (P 0049) an access monitor may register for event notifications that a permission is accessed (P 0085) a user may be presented with a notification to approve or reject a suspension (P 0105) and a decision may be made prior to suspension of a permission (P 0107). However, in the same field of invention, Ko discloses if a user’s terminal satisfies requirements for account access, a notification of permission of access is issued (P 0040). Therefore, considering the teachings of Ip, Adderly and Ko, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine providing, to the one or more users, a second indication that attribution of the first condition to the identity causes the changing of the first recommendation to the second recommendation with the teachings of Ip and Adderly with the motivation to make Ip more flexible by providing a well-known method for notification messages for not only when permissions are suspended, but also for permission access and for when permissions are granted and the Supreme Court in KSR International Co. v. Teleflex Inc. identified applying a known technique to a known device (method, or product) ready for improvement to yield predictable results as a rationale to support a conclusion of obviousness which is consistent with the proper “functional approach” to the determination of obviousness as laid down in Graham.
Ip does not disclose wherein the machine learning model is tested based on one or more speculative executions to confirm that the machine learning model satisfies a selected consistency benchmark, as disclosed in the claims. However, Ip discloses the application permission monitor monitors permissions to identify opportunities to suspend permissions that are inconsistent with a type of the application, etc. (P 0061). Adderly discloses a set of questions for investigating the hypothetical ontological relationship may be chosen based on the information concept type(s), ontological relationship type (link type), the questions may be selected by an automated engine that has been trained using machine learning techniques to identify the type of questions to be asked when the ontological relationship being investigated involves certain information concept types and link types (C 5 L 23-30) this process involves applying weights to the various scores, where the weights have been determined through training of the statistical model (C 19 L 66 – C 20 L 2) a weighted function of the confidence scores of the answers to the various questions, as well as the scores of the sub-links, may be utilized in which the weights for the various components of the calculation may be learned through training and machine learning (C 55 L 14-21). Therefore, considering the teachings of Ip, Adderly and Ko, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the machine learning model is tested based on one or more speculative executions to confirm that the machine learning model satisfies a selected consistency benchmark with the teachings of Ip, Adderly and Ko with the motivation to ensure the accuracy of the statistical data or patterns of Ip (Ip: P 0016).
Claim(s) 6 canceled.
Claim 7. Ip, Adderly and Ko disclose the computer-implemented method of claim 6, and Ip discloses wherein the first condition corresponds to accessing of a service, by the identity, within a future time period, a threshold period of time is set for a permission to be suspended if the permission has not been utilized (P 0031) if an application is expected to access an API within 24 hours of a new order transaction, but the site is not busy and may not see an order transaction for 90 days, the event-based threshold ensures that the application's permission is not suspended after, for example, more than 30 days passes between accesses (even though the application would access the API within 24 hours of each new order transaction) (P 0070) or has an expired password (P 0071) the permission controller determines that it is time to analyze permissions based on a timer expiring or any other event trigger (P 0086) an application that facilitates preparation of annual tax information may be expected to access an interface only once a year and may be associated with a 13 month threshold whereas an application that analyzes customer details to detect fraud may be expected to access data every day and/or access data within a period of time from a last transaction and will be associated with a threshold of one day or a threshold that is equal the time since a last transaction plus one day (P 0102).
Claim 8. Ip, Adderly and Ko disclose the computer-implemented method of claim 5, and Ip discloses wherein the first condition is included in a set of one or more future conditions that, when attributed to the identity, each cause changing of the first recommendation to the second recommendation, a threshold period of time is set for a permission to be suspended if the permission has not been utilized (P 0031) or has an expired password (P 0071) the permission controller determines that it is time to analyze permissions based on a timer expiring or any other event trigger (P 0086) The expiration of a password is a future event.
Claim 9. Ip, Adderly and Ko disclose the computer-implemented method of claim 8, and Ip discloses detecting occurrence of one of the set of one or more future conditions; and reevaluating, by the machine learning model, based at least in part on the detecting, the first recommendation, a threshold period of time is set for a permission to be suspended if the permission has not been utilized (P 0031) or has an expired password (P 0071) the permission controller determines that it is time to analyze permissions based on a timer expiring or any other event trigger (P 0086) determined by a machine learning algorithm (P 0107) The expiration of a password is a future event.
Claim 10-13. Canceled.
Claim(s) 14 is/are directed to non-transitory computer-readable storage media claim(s) similar to the computer system claim(s) of Claim(s) 1 and is/are rejected with the same rationale.
Claim 15. Ip, Adderly and Ko disclose the one or more non-transitory computer-readable storage media of claim 14, and Ip disclose wherein the first condition is a future condition, a threshold period of time is set for a permission to be suspended if the permission has not been utilized (P 0031) or has an expired password (P 0071) the permission controller determines that it is time to analyze permissions based on a timer expiring or any other event trigger (P 0086) The expiration of a password is a future event.
Claim 16. Ip, Adderly and Ko disclose the one or more non-transitory computer-readable storage media of claim 15, and Ip discloses wherein the first condition is included in a set of one or more future conditions that result in the changing of the first decision to the second decision, a threshold period of time is set for a permission to be suspended if the permission has not been utilized (P 0031) or has an expired password (P 0071) the permission controller determines that it is time to analyze permissions based on a timer expiring or any other event trigger (P 0086) The expiration of a password is a future event.
Claim 17. Ip, Adderly and Ko disclose the one or more non-transitory computer-readable storage media of claim 16, but Ip does not disclose wherein the operations further comprise: detecting occurrence of one of the set of one or more future conditions; and reevaluating, by the machine learning model, based at least in part on the detecting, the first decision, as disclosed in the claims. However, Adderly discloses a set of questions for investigating the hypothetical ontological relationship may be chosen based on the information concept type(s), ontological relationship type (link type), the questions may be selected by an automated engine that has been trained using machine learning techniques to identify the type of questions to be asked when the ontological relationship being investigated involves certain information concept types and link types (C 5 L 23-30). Therefore, considering the teachings of Ip, Adderly and Ko, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the operations further comprise: detecting occurrence of one of the set of one or more future conditions; and reevaluating, by the machine learning model, based at least in part on the detecting, the first decision with the teachings of Ip, Adderly and Ko with the motivation to ensure the accuracy of the statistical data or patterns of Ip (Ip: P 0016).
Claim 18-19. canceled.
Claim 20. Ip, Adderly and Ko disclose the one or more non-transitory computer-readable storage media of claim 14, and Ip discloses wherein the entity is an identity, and wherein the first decision and the second decision are permissions recommendations relating to allocation of a first permission to the identity, a notification is provided to a user (P 0017) a permission controller uses machine learning analysis to determine if a permission should be suspended or maintained (P 0068) or an administrator that a permission has been selected for deactivation (P 0073) a user may be presented with a notification to approve or reject a suspension (P 0105) and a decision may be made prior to suspension of a permission (P 0107).
Claim 21. Ip, Adderly and Ko disclose the computing system of claim 1, and Ip discloses wherein the first condition corresponds to accessing of a service, by the identity, within the future time period, if an application is expected to access an API within 24 hours of a new order transaction, but the site is not busy and may not see an order transaction for 90 days, the event-based threshold ensures that the application's permission is not suspended after, for example, more than 30 days passes between accesses (even though the application would access the API within 24 hours of each new order transaction) (P 0070) an application that facilitates preparation of annual tax information may be expected to access an interface only once a year and may be associated with a 13 month threshold whereas an application that analyzes customer details to detect fraud may be expected to access data every day and/or access data within a period of time from a last transaction and will be associated with a threshold of one day or a threshold that is equal the time since a last transaction plus one day (P 0102).
Claim 22. Ip, Adderly and Ko disclose the one or more non-transitory computer-readable storage media of claim 14, and Ip discloses wherein the first condition corresponds to accessing of a service, by the entity, within the future time period, if an application is expected to access an API within 24 hours of a new order transaction, but the site is not busy and may not see an order transaction for 90 days, the event-based threshold ensures that the application's permission is not suspended after, for example, more than 30 days passes between accesses (even though the application would access the API within 24 hours of each new order transaction) (P 0070) an application that facilitates preparation of annual tax information may be expected to access an interface only once a year and may be associated with a 13 month threshold whereas an application that analyzes customer details to detect fraud may be expected to access data every day and/or access data within a period of time from a last transaction and will be associated with a threshold of one day or a threshold that is equal the time since a last transaction plus one day (P 0102).
Response to Arguments
Applicant's arguments filed 4/23/2026 have been fully considered but they are not persuasive.
The applicant argues:
Claims 1, 5 and 14 stand 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. Office Action, at 2.
The Examiner concludes that the independent claims are "directed to an abstract idea," asserting that they are "not meaningful different from determining a future recommended permission for an entity based on a condition." Office Action at 2. The Examiner further characterizes the claims as amounting to "no more than the abstract idea of comparing new and stored information and using rules to identify options and organizing and manipulating information through mathematical correlations (based on speculative execution of a machine learning model)." Id. The Examiner also states that the judicial exception is "not integrated into a practical application" because the claim "merely mak[es] a recommendation based on speculation of a condition in a future time period," and "does not improve a computer, another technology or other technical field." Id. at 3. In responding to Applicant's prior arguments, the Examiner adds that "inputting data into a machine learning model does not add significantly more," and that multiple limitations are "not described in such a way to add significantly more," concluding that "each of the above identified limitations must provide sufficient description as to how the machine learning model performs" the limitations. Id. at 25.
Applicant respectfully submits that the Examiner's abstraction omits the operative claim language and thereby misstates what claim 1, as previously presented, actually requires. The Examiner summarizes claim 1 as "generating a first recommendation," "determining a first condition," and "attributing the condition at a future time period." Office Action at 2. That summary removes the central technical requirement: the "first condition" is not selected by a human rule-set, nor is it an unbounded informational association. Rather, claim 1 requires determining the condition "based on a speculative execution of the machine learning model" using input data reflecting hypothetical future conditions, and further requires identifying a condition that, when attributed to the identity, "causes changing" of the recommendation to a different recommendation. Id. Put differently, the claim recites a machine-performed process of executing a trained model under hypothetical inputs to determine how system behavior would change, and then presenting that change-triggering condition to users.
The Examiner's response asserts that the claims "simply state that a machine learning model generates a first and second recommendation" and that the speculative execution is "not described in such a way" to provide significantly more. Office Action at 26. Applicant respectfully disagrees. The question under §101 is not whether the claim contains some unstated implementation detail, but whether the claim as a whole is directed to an abstract idea or instead integrates any judicial exception into a practical application. MPEP § 2106.05(a)-(c). The claim language requires an identity-management permission recommendation systems and methods that use speculative execution to identify conditions that trigger recommendation changes and requires model testing by speculative executions to confirm satisfaction of a consistency benchmark. Office Action at 2-4. Those limitations constrain the claim to a particular machine- implemented technique for improving the reliability and transparency of automated permission management, rather than "comparing" information in the abstract.
The Examiner states that "using input that reflects at least one hypothetical future condition is nothing more that data that can be input by a user." Office Action at 4. That statement does not track the claim's operation. Claim 1, for example, does not merely present hypothetical data for human contemplation; it requires that the system determine, based on speculative execution of the model using such input, which condition causes a change in the model's recommendation and then provides an indication that attribution of that condition causes the change. Id. at 3. The claim therefore requires the computing system to execute the trained model under hypothetical conditions and to identify and communicate a condition-dependent transition in system output. Labeling such model execution as "user input" collapses the claimed system behavior into a non-operative description and is inconsistent with the claim language the Examiner has already identified.
The Examiner asserts that to "add significantly more," "each of the above identified limitations must provide sufficient description as to how the machine learning model performs one or more of the limitations," and that absent such description "the limitations do not add significantly more." Office Action at 25. That is legal error. Applicant respectfully submits that this is not the controlling inquiry under §101. Section 101 is not satisfied by imposing a requirement that the claim disclose implementation detail beyond the claimed functional constraints. Here, the claim requires speculative execution of a trained model to identify recommendation-changing conditions and requires speculative-execution-based consistency testing. These are concrete constraints on a computing system's operation, and the Examiner has not shown that these constraints are merely conventional or that the claim is directed to a judicial exception.
Nevertheless, and without waiver or disclaimer, Applicant has amended claim 1 to further clarify the mechanics of the speculative execution already recited in the claims as previously presented. This clarification directly addresses the Examiner's stated rationale that the hypothetical-condition input "is nothing more that data that can be input by a user," and that "inputting data into a machine learning model does not add significantly more or integrate the judicial exception into a practical application." Office Action at 5, 26. The amended language makes explicit that the speculative execution is not a mere presentation of hypothetical information, but comprises automatically executing the trained machine learning model using hypothetical-condition input data to output a recommendation, while the computing system determines a change-causing condition among those hypothetical future conditions. By clarifying that the speculative execution comprises execution of the trained model to produce an output recommendation under the hypothetical-condition inputs, the amendment reinforces that the claim recites concrete computing operations that cannot be performed mentally and that constrain the claim to a particular machine-implemented technique for generating and explaining condition-dependent recommendation changes in an identity-management permission environment.
The examiner respectfully disagrees. As noted in the rejection, merely including a step or steps in a claim wherein a machine learning model, or a computing device, processes some data of the claim, does not add significantly more unless limitations are included in the claim describing processing of the data by the machine learning, or computing device, in such a way that is beyond the capabilities of the human mind.
The additional element “computer generated hypothetical future condition” does not represent integration into a practical application or significantly more than the judicial exception because the claim does not describe the computer generated hypothetical future condition in such a way that the hypothetical future condition must be generated by a computer and not by the human mind. In order for the claimed hypothetical future condition to represent significantly more than the judicial exception, the claim must describe the computer generation of the hypothetical future condition such that hypothetical future condition could only be generated by a computer.
Regarding the amendments to the claims directed to the limitations: determining, based on a speculative execution of the machine learning model, the speculative execution comprising automatically executing the machine learning model … to output a recommendation, a first condition, Ip discloses the threshold period of time may be determined based on historical statistical and/or patterns of access (P 0016) determining that one or more permissions should be suspended or maintained using weights determined using machine learning analysis of the available information associated with applications and permissions in the e-commerce platform (P 0068) a criteria is evaluated to determine if the permission is maintained or suspended (P 0088) a user may be presented with a notification to approve or reject a suspension (P 0105) each determination may be associated with a score and that total score may be compared with a threshold utilizing a machine learning algorithm to determine such weighting and/or thresholds based on historical data (P 0107), Applicant’s specification discloses that speculative execution refers to an evaluation made based on a condition that has not actually occurred (i.e. is merely theoretical) at the time the evaluation is made; the threshold period in Ip is determined based on historical statistical and/or patterns of access to determine if a permission is maintained or suspended, that is, the evaluation for maintaining or suspending a permission is not based on currently evaluated data, but on historical data.
Applicant’s arguments with respect to claim(s) 1, 5, 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
New prior art reference Adderly has been combined with Ip to reject the amended limitations: determining, based on a speculative execution of the machine learning model … using input data indicative of at least one computer generated hypothetical future conditions, a first condition among the at least one computer generated hypothetical future conditions, as disclosed in the claims. However, in the same field of invention, Adderly discloses hypothetical ontological relationships between information concepts are automatically identified based on automated analysis of evidential support for these hypothetical ontological relationships found in association with existing actual ontological relationships and information concepts (C 4 L 5-10) a set of questions for investigating the hypothetical ontological relationship may be chosen based on the information concept type(s), ontological relationship type (link type), and the like. The questions may be selected by an automated engine that has been trained using machine learning techniques to identify the type of questions to be asked when the ontological relationship being investigated involves certain information concept types and link types (C 5 L 23-30) one or more corpora of evidential data are used to generate candidate answers to a question and identifies evidence supporting and/or refuting the hypothetical ontological relationship, or links, between information concepts corresponding to a question being answered (C 6 L 36-42) evaluating future hypothetical ontological links where scoring of the hypothetical ontological links is influenced by reliability and credibility of sources of information in the corpora (C 38 L 2-14).
Regarding the limitations: wherein the machine learning model is tested based on one or more speculative executions to confirm that the machine learning model satisfies a selected consistency benchmark, Ip discloses the application permission monitor monitors permissions to identify opportunities to suspend permissions that are inconsistent with a type of the application, etc. (P 0061). Adderly discloses a set of questions for investigating the hypothetical ontological relationship may be chosen based on the information concept type(s), ontological relationship type (link type), the questions may be selected by an automated engine that has been trained using machine learning techniques to identify the type of questions to be asked when the ontological relationship being investigated involves certain information concept types and link types (C 5 L 23-30) this process involves applying weights to the various scores, where the weights have been determined through training of the statistical model (C 19 L 66 – C 20 L 2) a weighted function of the confidence scores of the answers to the various questions, as well as the scores of the sub-links, may be utilized in which the weights for the various components of the calculation may be learned through training and machine learning (C 55 L 14-21).
It is clear that the machine learning model is adjusted by training to ensure that the correct hypothetical ontological relationships are calculated.
Conclusion
Any inquiry concerning this communication should be directed to JOHN M HEFFINGTON at telephone number (571)270-1696.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN M HEFFINGTON whose telephone number is (571)270-1696. The examiner can normally be reached on Monday through Friday from 9:30 am to 5:30 pm Eastern.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar B Paula, can be reached at telephone number 571-272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/J.M.H/Examiner, Art Unit 2145
6/26/2026
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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