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 communications filed on 12/23/2025.
Claims 1, 6-11, and 16- 18 have been amended and are hereby entered.
Claim 12 has been canceled.
Claims 1, 4-11, 13-18, and 20 are currently pending and have been examined.
This action is made Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Claim Objections
Claims 1, 17, and 18 are objected to because of the following informalities:
Claim 1: lines 31-35; Claim 17: lines 23-27; and Claim 18: lines 20-24 recite the limitations “estimate [estimating] a causality score indicating an effect that a predetermined operation has on whether or not the user executes the action for users having risk index at or above a risk threshold value” and “omit [omitting] estimation of the causality score for users having risk index below the risk threshold value.”
Firstly, “a causality score;” “an effect;” and “a predetermined operation” are previously recited in Claim 1: lines 5-6; Claim 17: lines 7-8; and Claim 18: lines 3-4. Is the “causality score;” “effect;” and “predetermined operation” recited in Claim 1: lines 31-32; Claim 17: lines 23-24; and Claim 18: lines 20-21 different than a causality score; an effect; and a predetermined operation recited in Claim 1: lines 5-6; Claim 17: lines 7-8; and Claim 18: lines 3-4, respectively? It appears there are typographical mistakes since the specification only points to a single causality score; effect; and predetermined operation. For compact examination purposes, Examiner interpreted the instances recited in Claim 1: lines 31-32; Claim 17: lines 23-24; and Claim 18: lines 20-21 as “estimate [estimating] the causality score indicating the effect that the predetermined operation has on whether or not the user executes the action for users.”
Secondly, Claim 1: lines 32-35; Claim 17: lines 24-27; and Claim 18: lines 21-24 recite the limitations “users having risk index at or above a risk threshold value….. users having risk index below the risk threshold value.” The indefinite article, “a” is missing before “risk index at or above a risk threshold value” and “risk index below the risk threshold value.” It appears there are typographical mistakes because the indefinite article “a” should have been used for each type of risk index. For compact examination purposes, Examiner interpreted the instances recited in Claim 1: lines 32-35; Claim 17: lines 24-27; and Claim 18: lines 21-24 as “users having a risk index at or above a risk threshold value….. users having a risk index below the risk threshold value.” Appropriate correction is required.
Thirdly, Claim 1: line 33 recites the limitation “risk threshold value, omit estimation” The comma, “,” after the word “value” should have been a semicolon (;). It appears this is a typographical mistake. For compact examination purposes, Examiner interpreted the instance recited in Claim 1: line 33 as “…..risk threshold value; omit estimation…..” Appropriate correction is required.
Lastly, Claim 1: line 34; Claim 17: line 26; and Claim 18: line 23 recite the limitation “omit [omitting] estimation of the causality score for users.” The indefinite article, “an” is missing before “estimation of the causality score.” It appears this is a typographical mistake because the indefinite article “an” should have been used before “estimation of the causality score.” For compact examination purposes, Examiner interpreted the instances recited in Claim 1: line 34; Claim 17: line 26; and Claim 18: line 23 as “omit [omitting] an estimation of the causality score for users.” Appropriate correction is required.
In summary, for compact examination purposes, Examiner interpreted the instances recited in Claim 1: lines 31-35; Claim 17: lines 23-27; and Claim 18: lines 20-24 as “estimate [estimating] the causality score indicating the effect that the predetermined operation has on whether or not the user executes the action for users having a risk index at or above a risk threshold value; and “omit [omitting] an estimation of the causality score for users having a risk index below the risk threshold value.” Hence, appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 16 is rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. For instance, in In re Hayes Microcomputer Products, the written description requirement was satisfied because the specification disclosed the specific type of microcomputer used in the claimed invention as well as the necessary steps for implementing the claimed function. The disclosure was in sufficient detail such that one skilled in the art would know how to program the microprocessor to perform the necessary steps described in the specification. In re Hayes Microcomputer Prods., Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, ___ (Fed. Cir. 1992). In the present applicant, claim 16 discloses “transmitting control signals to an operation center management system” where transmitting control signals is not supported in the specification as to how the applicant is “…transmitting control signals …” in order to show possession of the invention at the time of filing. While one skilled in the art could have devised a way to accomplish this aspect of the invention, Applicant’s original disclosure lacks sufficient detail to explain how Applicant envisioned achieving the goal of “transmitting control signals to an operation center management system.” Simply stating or re-stating the claim limitation does not provide enough support to show possession. Since these important details about how the invention operates are not disclosed, it is not readily evident that Applicant has full possession of the invention at the time of filing (i.e., the original disclosure fails to provide adequate written description to support the claimed invention as a whole). Neither the specification nor the drawings disclose in detail the specific steps or algorithm needed to perform the operation. If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a), for lack of written description must be made. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b). 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 1, 4-11, 13-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation directed to the user without significantly more.
Claim 1 is directed to an apparatus, which is one of the statutory categories of invention; Claim 16 is directed to a method, which is one of the statutory categories of invention; Claim 17 is directed to a non-transitory computer-readable recording medium, which is one of the statutory categories of invention; and Claim 18 is directed to a method, which is one of the statutory categories of invention. (Step 1: YES).
Independent Claim 1 is directed to an apparatus comprising: at least one memory configured to store program code; at least one processor configured to operate as instructed by the program code, the program code including: estimation code configured to cause at least one of the at least one processor to estimate a causality score indicating an effect that a predetermined operation, directed to a user to prompt the user to execute a predetermined action, has on whether or not the user executes the action; score output code configured to cause at least one of the at least one processor to use a machine learning model that outputs, in response to an input of one or a plurality of attributes regarding the user, the causality score indicating the effect of the operation directed to the user, wherein the causality score is based on a difference between statistics relating to an execution rate of the action by users who received the operation and statistics relating to the execution rate of the action by users who did not receive the operation, within a plurality of users having a predetermined attribute; operation condition output code configured to cause at least one of the at least one processor to output a condition relating to the operation directed to the user, on the basis of the effect that is estimated; priority condition output code configured to cause at least one of the at least one processor to output the condition that yields higher priority, with regard to the operation directed to the user of which the effect that is estimated by the estimation code is higher, wherein the operation condition output code is further configured to cause at least one of the at least one processor to output the condition to an operation center management system that manages operations regarding the user following the condition, wherein the estimation code is further configured to cause at least one of the at least one processor to: estimate a risk index for each user indicating probability the user will not execute the action, estimate a causality score indicating an effect that a predetermined operation has on whether or not the user executes the action for users having risk index at or above a risk threshold value, omit estimation of the causality score for users having risk index below the risk threshold value. These series of steps describe the abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation (with the exception of the italicized and bolded terms above), which is mitigating risk of incorrectly estimating priority conditions that yields higher priority; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The system limitations, e.g., at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Dependent claims 4-11, 13-15, and 20 are directed to an apparatus, which perform the steps that describe the abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation directed to the user. Furthermore, dependent claims 4-5 and 20 are directed to an apparatus, which perform the steps: “wherein the estimation code is further configured to cause at least one of the at least one processor to estimate the effect of the operation using the machine learning model generated using a machine learning framework based on ensemble learning; wherein the estimation code is further configured to cause at least one of the at least one processor to estimate the effect of the operation using the machine learning model generated using the machine learning framework based on a gradient boosting decision tree; wherein the machine learning model is created on the basis of training data, in which a causality score based on statistics relating to an execution rate of the action by users that received the operation, out of a plurality of users having a predetermined attribute, and statistics relating to the execution rate of the action by users that did not receive the operation, out of the plurality of users, is defined as a causality score indicating the effect of the operation directed to users having the attribute; and wherein the operation condition output code is further configured to cause at least one of the at least one processor to control a telecommunications equipment to initiate telephone calls or message transmissions to the user based on the priority determined from the effect that is estimated, wherein the telecommunications equipment executes different contact protocols based on the output condition, respectively, (with the exception of the italicized and bolded terms above), which is mitigating risk of incorrectly estimating priority conditions that yields higher priority; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. Thus, claims 4-11, 13-15, and 20 recite an abstract idea. The additional elements of at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, operation center management system, a machine learning framework based on ensemble learning, machine learning framework based on a gradient boosting decision tree, and telecommunications equipment are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, operation center management system, a machine learning framework based on ensemble learning, machine learning framework based on a gradient boosting decision tree, and telecommunications equipment do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Therefore, dependent claims 4-11, 13-15 and 20 have further defined the abstract idea that is present in their respective independent claim: Claim 1; and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract in nature for the reason presented above. The dependent claims 4-11, 13-15 and 20 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, claims 4-11, 13-15 and 20 are directed to an abstract idea without significantly more.
Independent Claim 16 is directed to a method, which recites a series of steps, e.g., performed by at least one processor and comprising: estimating an effect that a predetermined operation, directed to a user to prompt the user to execute a predetermined action, has on whether or not the user executes the action; using a machine learning model to output, in response to an input of one or a plurality of attributes regarding the user, a causality score indicating the effect of the operation directed to the user; wherein the causality score is based on a difference between statistics relating to an execution rate of the action by users who received the operation and statistics relating to the execution rate of the action by users who did not receive the operation, within a plurality of users having a predetermined attribute; outputting a condition relating to the operation directed to the user, on the basis of the effect that is estimated; outputting a condition with higher priority with regard to the operation directed to the user, of which the effect estimated is higher; transmitting control signals to an operation center management system and directing the operation center management system to execute the operation directed to the user according to the output condition and priority. These series of steps describe the abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation directed to the user (with the exception of the italicized and bolded terms above), which is mitigating risk of incorrectly estimating priority conditions that yields higher priority; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The system limitations, e.g., at least one processor, machine learning model, control signals, and operation center management system do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 16 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of at least one processor, machine learning model, control signals, and operation center management system, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 16 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 16 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of at least one processor, machine learning model, control signals, and operation center management system are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 16 is not patent eligible.
Independent Claim 17 is directed to a non-transitory computer-readable recording medium for storing a program that when executed by at least one processor, causes the at least one processor to: estimate an effect that a predetermined operation, directed to a user to prompt the user to execute a predetermined action, has on whether or not the user executes the action; use a machine learning model that outputs, in response to an input of one or a plurality of attributes regarding the user, a causality score indicating the effect of the operation directed to the user; wherein the causality score is based on a difference between statistics relating to an execution rate of the action by users who received the operation and statistics relating to the execution rate of the action by users who did not receive the operation, within a plurality of users having a predetermined attribute; output a condition relating to the operation directed to the user, on the basis of the effect that is estimated; output a condition that yields higher priority with regard to the operation directed to the user, of which the effect estimated is higher; output the condition to an operation center management system that manages operations regarding the user following the condition; estimate a risk index for each user indicating probability the user will not execute the action; estimate a causality score indicating an effect that a predetermined operation has on whether or not the user executes the action for users having risk index at or above a risk threshold value; and omit estimation of the causality score for users having risk index below the risk threshold value. These series of steps describe the abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation (with the exception of the italicized and bolded terms above), which is mitigating risk of incorrectly estimating priority conditions that yields higher priority; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The system limitations, e.g., a non-transitory computer-readable recording medium, program, at least one processor, machine learning model, and operation center management system do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 17 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a non-transitory computer-readable recording medium, program, at least one processor, machine learning model, and operation center management system, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 17 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 17 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a non-transitory computer-readable recording medium, program, at least one processor, machine learning model, and operation center management system, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 17 is not patent eligible.
Independent Claim 18 is directed to a method, which recites a series of steps, e.g., performed by at least one processor and comprising: acquiring training data, in which a causality score based on a difference between statistics relating to an execution rate of a predetermined action by a user that received a predetermined operation, out of a plurality of users having a predetermined attribute, and statistics relating to the execution rate of the action by a user that did not receive the operation, out of the plurality of users, is defined as the causality score indicating the effect of the operation directed to users having the attribute; creating a machine learning model on the basis of the training data; using the machine learning model and estimating an effect that the operation, directed to a user to prompt the user to execute the action, has on whether or not the user executes the action; outputting the condition to an operation center management system that manages operations regarding the user following the condition; estimating a risk index for each user indicating probability the user will not execute the action; estimating a causality score indicating an effect that a predetermined operation has on whether or not the user executes the action for users having risk index at or above a risk threshold value; and omitting estimation of the causality score for users having risk index below the risk threshold value. These series of steps describe the abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation directed to the user (with the exception of the italicized and bolded terms above), which is mitigating risk of incorrectly estimating priority conditions that yields higher priority; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The system limitations, e.g., at least one processor, machine learning model, and operation center management system do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 18 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of at least one processor, machine learning model, and operation center management system, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 18 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 18 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of at least one processor, machine learning model, and operation center management system are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 18 is not patent eligible.
Thus, claims 1, 4-11, 13-18, and 20 are not patent-eligible.
Response to Arguments
With respect to the objection of claims 1, 6, and 16-18, the objections are withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 12/23/2025. However, new claim objections have been given with regards to Claims 1, 17, and 18. In view of the grounds for the claim objection presented above in this office action, appropriate correction is required.
With respect to the 35 U.S.C. 112(a) rejection of claims 1, 4-15, 17-18, and 20, the rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 12/23/2025. However, the 35 U.S.C. 112(a) rejection of claim 16 is not withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 12/23/2025.
With respect to the rejection of independent claim 16 under 35 U.S.C. 112(a), Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Examiner respectfully notes that independent claim 16 discloses “transmitting control signals to an operation center management system” where transmitting control signals is not supported in the specification as to how the applicant is “…transmitting control signals …” in order to show possession of the invention at the time of filing. While one skilled in the art could have devised a way to accomplish this aspect of the invention, Applicant’s original disclosure lacks sufficient detail to explain how Applicant envisioned achieving the goal of “transmitting control signals to an operation center management system.” Neither the specification nor the drawings disclose in detail the specific steps or algorithm needed to perform the operation of transmitting control signals.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 112(a) rejection of claim 16.
Applicant's arguments filed on 12/23/2025 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of 1, 4-18, and 20 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “Claim 1 now recites a specific conditional processing architecture that addresses technical challenges in large-scale data processing systems. The specification explains at paragraph [0039] that "the effect estimating unit 21 may estimate effects of the operation with regard to users of which the risk index is estimated to be the threshold value R3 (third threshold value) or higher, and not estimate effects of the operation (omit estimation processing) with regard to users of which the risk index is estimated to be lower than the threshold value R3 (third threshold value)." This conditional omission of processing provides a technical solution to computational resource constraints encountered when processing large user populations with machine learning models…… The claimed architecture improves computer operations through a specific processing structure that reduces computational resource consumption. The selective execution of the machine learning model based on risk thresholds is not merely a business optimization but rather a technical design choice that affects how the computer system allocates processing resources…… the technical problem addressed by the claimed invention is the computational burden of applying resource-intensive machine learning models to large-scale user populations. Processing millions of user records through a gradient boosting decision tree model, as described in paragraphs [0032-0033], requires substantial computational resources. The technical solution recited in amended Claim 1 is the conditional processing architecture that selectively executes the causality score estimation only for users whose risk index meets or exceeds a threshold value, thereby reducing the total computational workload….. the limitation that the estimation code "omit estimation of the causality score for users having risk index below the risk threshold value" is not a generic instruction to process data but rather a concrete limitation on when and how the machine learning model is invoked, directly impacting the computational workload of the system. … The claimed system differs by implementing a hierarchical processing flow where a first model's output determines whether a second model executes at all. This two-stage conditional architecture, where model execution is gated by threshold comparison of another model's output, is not a routine or conventional arrangement of machine learning components…..When combined with the conditional processing architecture now recited in Claim 1, the system implements a specific technical approach where one machine learning model gates the execution of another, creating an ordered combination that addresses computational efficiency in a manner not routine in the field…… The amendments to Claim 1 integrate the abstract idea into a practical application by reciting a specific conditional processing architecture that improves computer operations through reduced computational resource consumption. The claimed system addresses the technical problem of processing large user populations with resource-intensive machine learning models by implementing a two-stage architecture where risk assessment gates the execution of causality estimation, thereby reducing the total number of model invocations required. This conditional processing approach represents an unconventional arrangement of machine learning components that provides technical improvements to computer system operations beyond merely applying an abstract idea using generic computer elements. For at least these reasons, the rejection of Claims 1, 4-18, and 20 under 35 U.S.C. § 101 is respectfully traversed, and withdrawal of the rejection is respectfully requested.”
Examiner respectfully disagrees.
Under Step 2A: Prong 1, Examiner respectfully notes that claims, as amended, are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea; without significantly more. The series of steps recited in claims 1, 4-11, 13-18 and 20, as amended, describe the abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation directed to the user, which is mitigating risk of incorrectly estimating priority conditions that yields higher priority; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. Furthermore, the system limitations (amended claim 1), e.g., at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system do not necessarily restrict the claim from reciting an abstract idea. Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, as previously discussed in the Final Office Action dated 04/01/2025 and Non-Final Office Action dated 09/25/2025, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea. Furthermore, Examiner respectfully notes that the recited features in the limitations: “an apparatus comprising: at least one memory configured to store program code; at least one processor configured to operate as instructed by the program code, the program code including: estimation code configured to cause at least one of the at least one processor to estimate a causality score indicating an effect that a predetermined operation, directed to a user to prompt the user to execute a predetermined action, has on whether or not the user executes the action; score output code configured to cause at least one of the at least one processor to use a machine learning model that outputs, in response to an input of one or a plurality of attributes regarding the user, the causality score indicating the effect of the operation directed to the user, wherein the causality score is based on a difference between statistics relating to an execution rate of the action by users who received the operation and statistics relating to the execution rate of the action by users who did not receive the operation, within a plurality of users having a predetermined attribute; operation condition output code configured to cause at least one of the at least one processor to output a condition relating to the operation directed to the user, on the basis of the effect that is estimated; priority condition output code configured to cause at least one of the at least one processor to output the condition that yields higher priority, with regard to the operation directed to the user of which the effect that is estimated by the estimation code is higher, wherein the operation condition output code is further configured to cause at least one of the at least one processor to output the condition to an operation center management system that manages operations regarding the user following the condition, wherein the estimation code is further configured to cause at least one of the at least one processor to: estimate a risk index for each user indicating probability the user will not execute the action, estimate a causality score indicating an effect that a predetermined operation has on whether or not the user executes the action for users having risk index at or above a risk threshold value, omit estimation of the causality score for users having risk index below the risk threshold value” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong II.
Under Step 2A: Prong II, Examiner respectfully notes that there is no improved technology in simply estimating, inputting, using, scoring, processing, and outputting data (i.e., user data, user attributes, priority condition, statistical data, and etc.). Unlike the 2019 and 2024 Updated USPTO's Subject Matter Eligibility Examples and Ex Parte Desjardins, the disclosed invention simply cannot be equated to improvement to technological practices or computers. As previously discussed in the Final Office Action dated 04/01/2025 and Non-Final Office Action dated 09/25/2025, there is no technical improvement at all. Instead, Applicant recites “an apparatus comprising: at least one memory configured to store program code; at least one processor configured to operate as instructed by the program code, the program code including: estimation code configured to cause at least one of the at least one processor to estimate a causality score indicating an effect that a predetermined operation, directed to a user to prompt the user to execute a predetermined action, has on whether or not the user executes the action; score output code configured to cause at least one of the at least one processor to use a machine learning model that outputs, in response to an input of one or a plurality of attributes regarding the user, the causality score indicating the effect of the operation directed to the user, wherein the causality score is based on a difference between statistics relating to an execution rate of the action by users who received the operation and statistics relating to the execution rate of the action by users who did not receive the operation, within a plurality of users having a predetermined attribute; operation condition output code configured to cause at least one of the at least one processor to output a condition relating to the operation directed to the user, on the basis of the effect that is estimated; priority condition output code configured to cause at least one of the at least one processor to output the condition that yields higher priority, with regard to the operation directed to the user of which the effect that is estimated by the estimation code is higher, wherein the operation condition output code is further configured to cause at least one of the at least one processor to output the condition to an operation center management system that manages operations regarding the user following the condition, wherein the estimation code is further configured to cause at least one of the at least one processor to: estimate a risk index for each user indicating probability the user will not execute the action, estimate a causality score indicating an effect that a predetermined operation has on whether or not the user executes the action for users having risk index at or above a risk threshold value, omit estimation of the causality score for users having risk index below the risk threshold value.” Unlike Ex Parte Desjardins, the recited features in the limitations do not result in computer functionality or technical improvement. Specifically, Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. The recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing data (user attribute and priority condition data), and no technical solution or improvement has been disclosed.
Additionally, as previously discussed, there is no technology/technical improvement as a result of implementing the abstract idea. The recited limitations in the pending claims simply amount to the abstract idea of estimating an effect that a predetermined operation has on whether or not the user executes an action and outputting a condition relating to the operation directed to the user. There is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Furthermore, unlike the 2019 and 2024 Updated USPTO's Subject Matter Eligibility Examples and Ex Parte Desjardins, the amended claims recite steps at a high level of generality. See MPEP 2106.05(g). In addition, all uses of the recited judicial exceptions require such data gathering, inputting, and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, inputting, and outputting. See MPEP 2106.05. The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Additionally, these steps, as amended, are recited as being performed by at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system, which are used as tools to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). The claims as amended, recites at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system, which are simply used to perform an abstract idea, as discussed above in Step 2A, Prong 1, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system” in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. Additionally, the ‘automated’ features simply amounts to mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). Thus, the automation feature is not sufficient to show an improvement in computer-functionality or technology/technical improvements (see MPEP 2106.05(a)(1)). The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. The claims, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.. Hence , the claims as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Lastly, under Step 2B, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. As previously discussed in the Final Office Action dated 04/01/2025 and Non-Final Office Action dated 09/25/2025, the improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea; and hence, these arguments are not persuasive. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. As noted above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. As noted above, the recitation of “at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, control signals, and operation center management system” in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. Thus, the automation feature is not sufficient to show an improvement in computer-functionality or technology/technical improvements (see MPEP 2106.05(a)(1)). The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. The claims, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Applying the 2019 and 2024 Updated Guidance on Patent Subject Matter Eligibility here and as discussed above with respect to Step 2A, Prong II, the additional elements: at least one memory, program code, at least one processor, estimation code, score output code, machine learning model, operation condition output code, priority condition output code, and operation center management system, are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). As discussed in Step 2A, Prong II above, the claims’ limitations are recited at a high level of generality. These elements simply amount to receiving, inputting, and outputting data and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II). As discussed in Step 2A, Prong Two above, the recitation of a computer/processor to perform recited limitations, as amended, amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. (Step 2B: NO).
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1, 4-11, 13-18, and 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are the following:
Shah (U.S. Patent Publication No. US 2020/0265443 A1) “Event prediction using artificial intelligence”
Burgess (U.S. Patent Publication No. US 2012/0005053 A1) “Behavioral-based customer segmentation application”
Fang (U.S. Patent Publication No. US 2019/0251612 A1) “Generating user-customized items using a visually-aware image generation network”
Seshan (U.S. Patent Application Publication No. US 2020/0327552 A1) “Optimized dunning using machine-learned model”
Cochran (U.S. Patent Application Publication No. US 2023/0360778 A1) “Machine learning models for automated request processing”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/CHO YIU KWONG/Primary Examiner, Art Unit 3693