DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 11/7/2025, Applicant, on 1/22/2026, amended Claims 1, 3, 5, and 7. Claims 1-3 and 5-7 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §112(a)/(b) – The rejections are hereby removed in light of Applicant’s amendments. A new rejection under §112(a) and §112(b) are below as necessitated by the amendments.
Arguments regarding 35 USC §101 Alice – Applicant recites the amended limitations of the claims, specifically the performing reinforcement learning using a first dataset, applying a validation dataset that is excluded from the reinforcement learning used to generate the trained KPI prediction model, and calculating predicted KPI values using that excluded validation dataset, reciting the other amended limitations, and that the claims cannot be performed in the mind nor are they a method of organizing human activity as they focus on how machine learning models are trained, validated, and optimized to reduce overtraining, which is a technical problem in machine learning system.. Examiner disagrees as using trained machine learning is not a technical operation but rather is a utilization of current technologies such as an apparatus, storage device, and CPU, to perform the abstract limitations of the Claims. As per the rejection below, the claims recite limitations which describe abstract processes of both a “Mental Process” and a “Certain Method of Organizing Human Activity”, as the claims recite limitations for the purposes of managing policies, a Fundamental Economic process, which is inherently an abstract idea. There is no improvement to any technological feature, and excluding information from the machine learning does not change the fact that there are identified abstract ideas.
Applicant asserts the claims are integrated into a practical application and recites the amended limitations of the claims, stating that the dataset is excluded in the machine learning is a meaningful constraint and a concrete improvement to machine learning model, thus improves model generalization and reduced overtraining, and stating that this is a specific and well-known technical problem in the machine learning domain. Examiner disagrees as the claims are not practically integrated, as the claim limitations merely utilize current technologies such as a trained machine learning model to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It” for making recommendations. The use of a trained KPI prediction model to display AI-generated results is use of a computer, with no improvement to any of the additional elements alone or in combination, and these have not been meaningfully integrated nor is there any type of technological improvement. Further the excluding of information in the machine learning is another implementation of the machine learning model, and there can’t be any improvement to the machine learning as, at best, this is a utilization of machine learning to perform the abstract limitations of the claims.
Applicant asserts the claims recite significantly more as the claims execute machine learning models using dataset partitioning, validation-based weighting, and training-time biasing to reduce overtraining, the ordered combination of claim 1 improves the functioning of a machine learning system, and states that these uses form a specific, non-conventional solution to a known problem in predictive modeling and sales activity recommendation systems. Examiner disagrees as there is no improvement to the machine learning mode, a technology or any technological process, as any inventive concept would be contained wholly within the abstraction, that of predictive modeling for sales activities recommendations. There is no improvement to the learning model (if it were taken as an additional element), apparatus, CPU, storage device, GUI, or any other additional element, as per Applicant’s Specification shown in the rejection below. This is “Applying It”, similar to Alice, on a generic computing system.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
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.
Claims 1-3 and 5-7 are 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(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 5 recite a limitation to “calculate a predicted KPI value about the organization by applying to the trained KPI prediction model, a validation dataset comprising sales activity records that are excluded from the reinforcement learning used to generate the trained KPI prediction model, the validation dataset being temporally separated from the first dataset and configured to evaluate generalization performance of the trained KPI prediction model”. The validation dataset being temporally separated from the first dataset and configured to evaluate generalization performance of the trained KPI prediction model” are not defined in the specification as there is nothing in the specification about anything being temporally separated from another data element. The words valid, validation, temporally, separated, and generalization, and other forms of those word aren’t even in the specification. The Applicant’s specification states this about performance:
“[0019] <Background and Concept as Premises>
First, a description is given of an overview of problems and solutions that the Applicants have found in regards to the present disclosure. As already described, a sales representative's sales performance is greatly affected by the suitability of an activity policy for conducting a sales activity, such as which criteria to follow to determine where to pay a visit or which product to propose. [0020] Thus, ideas already exist of creating a model for exploring a sales policy through reinforcement learning performed by feeding attributes of past sales activities and target companies and sales track records to a learning engine. However, a sales policy presented by such a model based on reinforcement learning is not always the best measure. This is because such a policy also includes exploratory activities for maximizing future value. [0021] When such exploratory sales activities increase, sales track records do not improve in a short run, and value (a key performance indicator (KPI) may become lower than at present. Thus, the degree of such exploratory activities may be a matter to be adjusted based on a business decision. Thus, it is necessary to know how much of the sales policies presented to the sales representative as recommended measures based on the results of reinforcement learning are exploratory measures, but this is difficult with the existing techniques. [0022] Also, when results of reinforcement learning are used for activities like sales activities that involve human actions (such as decision making and paying visits), it is unlikely that a sales representative feels right about, accepts, and conducts the policy adjusted in priority by reinforcement learning. [0023] Conducting such a policy tends to affect sales performance, and therefore, the sales”
Which is the only description of performance in the Specification, and this is only in the background and a KPI model, and this could be entirely new matter. Again, there are no details of this limitations in the Specification. To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that a patent must describe the technology; the requirement serves both to satisfy the inventor’s obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). The dependent Claims inherit the deficiencies of the independent claims and thus are similarly rejected.
Therefore, the claims and their dependent claims are rejected under 35 U.S.C. 112(a), written description, as being directed to non-statutory subject matter.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-3 and 5-7 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor.
Claims 1 and 5 recite limitations to “calculate a predicted KPI value about the organization by applying to the trained KPI prediction model, a validation dataset comprising sales activity records that are excluded from the reinforcement learning used to generate the trained KPI prediction model, the validation dataset being temporally separated from the first dataset and configured to evaluate generalization performance of the trained KPI prediction model”. Applicant’s specification is silent to this validation dataset and them being temporally separated from the first dataset, and as best taken from above, this can be any dataset which is used in the training and learning of models. For Examination purposes this will be taken as any dataset.
The dependent claims inherit the deficiencies of the independent, and thus the dependents are similarly rejected.
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.
Alice – Claims 1-3 and 5-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 5 are directed at limitations to perform reinforcement learning by using, as learning data, part of the pieces of information on the organization and the sales activity and generate the trained KPI prediction model (Analyzing Information, an evaluation, a Mental Process; Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), calculate a predicted KPI value about the organization by applying to the trained KPI prediction model, a validation dataset comprising sales activity records that are excluded from the reinforcement learning used to generate the trained KPI prediction model, the validation dataset being temporally separated from the first dataset and configured to evaluate generalization performance of the trained KPI prediction model (Collecting and Analyzing the Information, an observation and evaluation, a Mental Process; Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), retrieve a track record KPI value associated with the sales activity records included in the validation dataset from the storage device (Collecting Information, an observation, a Mental Process; a Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), calculate, based on the validation dataset, a weight representing an upward deviation between the predicted KPI and the track record KPI value using a predefined deviation function that applies time-decay to historical sales activity records, the weight being configured to suppress overtraining of the trained KPI prediction model (Analyzing the Information, an evaluation, a Mental Process; a Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), generate a first policy determination model by applying machine learning to a training dataset, the training dataset comprising: organizational explanatory variables, associated KPI values as target values, and the weight as a training parameter that biases training toward validation-consistent sales activity outcomes (Analyzing the Information, an evaluation, a Mental Process; a Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), determine a policy value indicating a recommended activity by applying the information on the organization to the determination model (Analyzing the Information, an evaluation, a Mental Process; a Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), determine an unweighted policy value by applying the information on the organization to a second policy determination model wherein the second policy determination model is trained without applying the weight (Analyzing the Information, an evaluation, a Mental Process; a Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), and output the policy value and the unweighted policy value as part of a computer-generated output, the computer-generated output including displaying a comparative visualization of the policy value and the unweighted policy value to perform model validation and policy optimization that reduces overtraining relative to a policy determination model trained without the validation dataset (Transmitting the Analyzed Information, an evaluation and judgment, a Mental Process; a Fundamental Economic Process, i.e. managing policies; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of managing and making a policy decision, but for the recitation of generic computer components. That is, other than reciting a sales activity support apparatus, storage device configured to hold pieces of information on a sales target organization and a sales activity for the organization, and a CPU, nothing in the claim element precludes the step from practically being performed or read into the mind for a Fundamental Economic Process, i.e. managing policies. For example, calculating a weight corresponding to a degree by which the predicted value exceeds a track record value related to the sale activity for the organization encompasses any manager or supervisor looking at sales activity and determining a weight, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for a Fundamental Economic Process, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The apparatus, storage device, and CPU are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmission steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0030] Meanwhile, the user terminal 200 is a terminal that a sales representative as described above uses to use a service provided by the sales activity support apparatus 100. Specific possible examples include a smartphone, a tablet terminal, and a personal computer.
[0031] Also, the administrator terminal 300 is a terminal that a director or the like as described above uses to use a service provided by the sales activity support apparatus 100, and is a terminal used to provide the sales activity support apparatus 100 with various kinds of data for model creation (data in a company DB 125, a product DB 126, and a track record DB 127). Specific possible examples include a smartphone, a tablet terminal, and a personal computer.”
Which states that any type of general purpose computer system can be used, such as any personal computer, laptop, mobile phone, tablet, etc., to perform the abstract limitations, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmission steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the medium, computing device, processors, etc., nor the receiving and transmission steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-3 and 6-7 contain the identified abstract ideas, further narrowing them, with no additional elements to be considered as part of a practical application or under prong 2 of the Alice Analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claim 1-3 and 5-7 are objected to as being dependent upon a rejected base claim, but would be allowable if the independent claims were amended in such a way as to overcome the 35 USC 101 rejection and any other rejections.
The closest prior art of record are Fukuda (U.S. Publication No. 2009/028,1845), Sinha (U.S. Publication No. 2022/038,3224), and Cmielowski (U.S. Publication No. 2017/012,6740}. Fukuda, a method and apparatus of constructing and exploring KPI networks, teaches defining KPI in goal models, defining a set of KPIs, calculation of KPIs using policies of access control, using different goal models to determine strategy and goals, modeling and monitoring of the modeling by adding a context modeling, determining a KPI catalog, use of dependency algorithms which are weighted to determine a policy value, and considering a weighted directed KPI graph using an algorithm which considers edges weight, but not the unweighted policy value obtained about the organization or the explanatory variable based on the determination model generated through machine learning. Sinha, a system and method for determining leading indicators and monitor business KPIs and metrics for preemptive action, teaches a machine learning model being used to learn weightings of KPIs, identifying one or more leading indicators that forecast target time in series data, determination of attention weights for the machine learning model, using the weights to modify the model and train the model, and determining a model output of importance of a KPI, but not the determination and calculation of a weight corresponding to a degree by which the predicted value exceeds a track record value related to the sales activity of the organization, determination of a policy value, or determination of an unweighted policy value. Cmielowski, a system and method for predicting performance of machine learning models, teaches performance prediction between machine learning model metric values and overall process indicator values, such as business KPIs, using and training a regression model which utilizes the values, and using behavioral learning models, deep learning models, to measure KPIs, but not the determination of a policy value or an unweighted policy value. None of the prior art explicitly teaches calculating a weight corresponding to a degree by which the predicted value exceeds a track record value related to the sales activity, determining a policy value using machine learning and KPIs, and determination and output of a policy value with an unweighted policy value, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-3 and 5-7 are allowable over the prior art of record, and are objected to as provided above.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 2/23/2026