DETAILED ACTION
This Office Action is in response to Applicants response after non-final rejection received on November 12, 2025. Claim(s) 1-4, 6-8 is/are currently pending in the instant application. The Application is a 371 of PCT/JP2022/007550 filed on February 24, 2022.
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 .
Response to Amendment
The Examiner acknowledges the Applicants amendments to claims 1, 4, 6 and 7 in the response on November 12, 2025. Claim 5 is canceled at this time.
The replacement abstract is also acknowledged and the prior objection has been vacated
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 1, 4, and 6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claims include limitations of estimation processing executed with reduced computational load and improved convergence stability. The specification is silent with respect to these limitations. There is not discussion or detail regarding how the claimed apparatus is achieving the reduction in computational load with the current arrangement. Further there is no detail or explanation regarding the assertion that the stability is improved.
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, and 6-8 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.
Claims 1-4, and 6-8 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a system or “apparatus”, method or “process”, or product or “article of manufacture” and is a method and apparatus for time discount rate estimation and analysis which is a process (e.g. process/machine) (Step 1: YES).
The Examiner has identified independent method Claim 6 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 1 and 4 and method Claim 7. Claim 6 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
calculating a transition time from a behavior of a predetermined user recorded at each date and time to all types of behaviors of the predetermined user and outputting behavior transition time feature data for each behavior recorded at each date and time;
distributing the behavior transition time feature data to the processor and executing matrix operations for deep learning in parallel to train the time discount rate estimation model:
calculating an error between a value of a time discount rate obtained by inputting the behavior transition time feature data to a time discount rate estimation model obtained by deep learning and a time discount rate serving as correct answer data based on an answer by the predetermined user and performing machine learning on the time discount rate estimation model by updating parameters of multiple layers of the time discount rate estimation model by performing backpropagation on the GPGPU, thereby controlling the parameters based on the calculated error so as to reduce the error,
calculating, by using a self-attention mechanism included in the time discount rate estimation model, a weight for each transition time,
outputting visualization data indicating importance of each behavior based on the calculated weight, wherein the visualization data include a horizontal axis representing date and time information indicating when each behavior occurred, and a vertical axis representing values of the importance, and
storing the updated parameters in a model database so that subsequent estimation processing is executed with reduced computation load and improved convergence stability.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. Calculating time and date based on recorded behaviors and calculating error between time discount rate obtained from the data and an estimation model and using machine learning to reduce error, calculating a weight, outputting a visualization, and storing in a database recites managing personal behavior. Accordingly, the claim recites an abstract idea. The time discount rate estimation apparatus in Claims 1 and 4 is just applying generic computer components to the recited abstract limitations. The method applied to non-transitory computer-readable recording medium having computer readable instructions stored thereon in Claim 7/8 appears to be just software. Claims 1, 4, and 7 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mental Processes”. Calculating time and date based on recorded behaviors, calculating error between time discount rate obtained from the data, calculating a weight, and an estimation model to reduce error recites concepts performed in the human mind. But for the “deep learning model” and “machine leaning” language, the claims encompass collecting the behavior data of a predetermined user for analysis of transition time and calculating the error between the discount rate from the data and the estimation of the discount rate from a model and working to reduce the error between the two using his/her mind and/or pen and paper. The mere nominal recitation of deep learning and machine learning is application of the technological field and does not take the claim out of the mental processes grouping. Accordingly, the claim recites an abstract idea. The time discount rate estimation apparatus in Claims 1 and 4 is just applying generic computer components to the recited abstract limitations. The method applied to non-transitory computer-readable recording medium having computer readable instructions stored thereon in Claim 7/8 appears to be just software. Claims 1, 4, and 7 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a processor, a memory including instructions, and machine learning (Claims 1 and 4) a machine learning method (claim 6) and/or a discount rate estimation using machine learning stored on a non-transitory computer-readable recording medium (Claim 7/8). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1,4, 6, and 7 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0016] and [0075] about implantation using general purpose or special purpose computing devices ([0016] The processor 101 and the memory 102 form a so- called computer, and, when the processor 101 executes the various programs read on the memory 102, the computer implements various functions. [0075] machine learning is performed on the time discount rate estimation model (model parameters) by using a known technique such as backpropagation) and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 4, 6, 7 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2, 3, and 8 further define the abstract idea that is present in their respective independent claims 1, 4, 6, 7 and thus correspond to Certain Methods of Organizing Human Activity and/or Mental Processes and hence are abstract for the reasons presented above. The dependent claims 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. The dependent claims include steps or processes which are similar to that disclosed in MPEP 2106.05(d), (f), (g), and/or (h) which include activities and functions the courts have determined to be well-understood, routine, and conventional when claimed in a generic manner, or as insignificant extra solution activity, or as merely indicating a field of use or technological environment in which to apply the judicial exception. Therefore, the claims 2, 3, 5, 8 are directed to an abstract idea. Thus, the claims 1-8 are not patent-eligible.
Response to Arguments
The Applicants remarks begin on page 9 of the response on November 12, 2025. The Applicant begins with a summary of the claims and the amendment to the specification for the amended abstract.
The arguments begin with the rejection under 35 U.S.C § 101 (remarks pages 9-12) with Step 2A, Prong One (remarks page 10) where the Applicant does not agree the Office assertion that the claims are an abstract idea under certain methods of organizing human activity or as mental processes. The Applicant argues that claim 1 recites hardware components and concrete computational operations that cannot be performed in the human mind or with pen and paper. Applicant specifically cites the updating parameters of multiple layers of the model using backpropagation on the GPGPU (Remarks page 10-11). The arguments claim the steps define a specialized computer architecture and parallel computation process which humans cannot perform.
The Examiner does not agree. The Office action clearly stated that aspects of the claim such as calculating transition times based on user behaviors and calculating error values are aspects which can be performed in the mind. Also, the rejection clearly indicates that other than claiming the calculations and visual outputs are performed by machine learning on the models through the use of a computer the claims are simply applying the computer and known machine learning techniques, specifically neural networks or deep learning, and a backpropagation technique and the judicial exception. For this reason the claims are not successfully integrated into a practical application rather simply applying a computer and known techniques to the recited judicial exception.
Further, the Applicant admits in par. 0075 that the backpropagation technique related to the deep learning is a known technique and is being used in it’s ordinary and known capacity.
The arguments move on to Step 2A, Prong 2 (remarks page 11) where the Applicant argues that the claims are integrated into a practical application. The arguments cite technical specific data-handling operation which the Applicant claims are a tangible improvement. The operations are parallelized training of the deep learning model, parameter control through backpropagation to improve the model, storage of updated parameters in a database, and generation of visualization data. The Applicants position is that the limitations are a technical solution to a technical problem of improving speed, stability, and interpretation of machine learning model training.
The arguments are not persuasive. Data handling operations do not immediately make the claim limitations eligible. Reviewing the cited limitations the training limitation is you generic and necessary training of any kind of machine learning model. Without the first training and validation sequence it would not be possible to confirm that any model is providing correct and useful data. Simply including the first step training is not indicative of practical application. Second, the idea that parallel computing will reduce computation time is flawed as the time it takes to achieve a solution may be less however multiple cycles are executed in the parallel architecture meaning the same amount of computation may happen.
The limitation of backpropagation is simply claiming the known technique related to deep learning or neural networks. Further the specification is silent to the improvement to model stability and performance. The limitation of storage of updated parameters for the purpose of reducing computation load is similar to MEPE 2106.05(d)II. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Theses are functions the courts have determined to be well-understood, routine, and conventional behavior when claimed in a merely generic manner or as insignificant extra solution activity.
Finally, discussing the generation of structured visualization is not more than displaying the data as a graph with X and Y axes. Since there is not change, manipulation, or interaction with the display or the data beyond presenting it for the use to read or otherwise interpret, the limitation would be similar to MPEP 2106.05 (g)(3) iii. Selecting information, based on types of information and availability of information [in a power-grid environment], for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);.
In this instance, the Examiner does not find a technical solution to a technical problem, rather a technical solution to a time management problem. The application of technology to the problem does not guarantee eligibility. It’s more of the apply it standard where the computer or technical steps are used as a tool to perform the otherwise abstract idea. Also, the specification is silent to the claim of improving speed, stability, and performance of the model training environment.
Next the arguments present Step 2B where it is argued that the additional limitations are not well understood, routine, or conventional computer functions. The Applicant cites the use of GPGPU based architecture for distributing behavior features, performing matrix based deep learning in parallel, and dynamic updating model parameters as non-conventional configuration that improves operation.
The Examiner disagrees. Many of the functions are listed in section 2106.05(d) which cite well-understood, routine, and conventional functions as established by the courts. This includes but is not limited to MPEP 2106.05(d)II. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
ii. Performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Other limitations are similar to additional elements as mere instructions to apply the exception because the do no more than merely invoke computers or machinery as a tool to perform the existing process MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Or as examples of activities the courts have established as insignificant extra solution activities MPEP 2106.05(g)(3) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);.
In summary, the arguments and amendments have not overcome the rejection under 35 U.S.C § 101. The claims are not in condition for allowance at this time.
Conclusion
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 DYLAN C WHITE whose telephone number is (571)272-1406. The examiner can normally be reached M-F 7:30-4:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DYLAN C WHITE/Primary Examiner, Art Unit 3625 March 4, 2026.