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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/21/2026 has been entered.
Claim Status
The claims filed 6/10/2026 are entered.
Claims 1-2, 4-10, and 12-14, and 21-22 are pending.
Claims 1, 13, and 14 are independent.
Claims 1-2, 4-8, and 12-14 are currently amended.
Claims 9-10 and 21-22 are previously presented.
Response to Arguments
Applicant's arguments filed 6/10/2026 have been considered but they are not fully persuasive.
35 U.S.C. 101
Applicant’s arguments regarding the rejection of claims 1-2, 4-10, 12-14, and 21-22 as being directed to an abstract idea without significantly more have been considered but are not persuasive.
Regarding Step 2A Prong 2 of the subject matter eligibility framework, Applicant argues that the additional elements integrate the claimed abstract idea into a practical application. Specifically, Applicant argues “the claims now recite "training a learned model by performing machine learning using teacher data, the teacher data comprising input data including coordinates of a plurality of training word appearance frequency graphs and correct answer output data including identification information of regions within a scatter diagram." Under MPEP 2106.04(d) and current USPTO guidance regarding Artificial Intelligence, claims that specify how a machine learning model is trained utilizing a specific, unconventional data structure (here, mapping raw coordinate graphs to specific regions of a dual-area scatter diagram utilizing a square function area) integrate the exception into a practical application. The claims do not preempt the general use of machine learning; they are limited to a specific AI training regimen that translates temporal frequency coordinates into a highly specific diagnostic output space, thereby improving the technological functioning of the data processing system itself.”
The argument is not persuasive. Regarding Applicant’s argument that the claims do not preempt the general use of machine learning, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B). Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1150, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016); Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379, 115 USPQ2d 1152, 1158 (Fed. Cir. 2015). Here, the recited training step does not integrate the abstract idea into a practical application under Step 2A Prong 2 of the eligibility framework. The specification discusses machine learning features at a level of generality in a manner which is not indicative of a technical improvement to one of ordinary skill in the art. For example, the specification states “Machine learning is a technology for allowing a computer to acquire human-like learning abilities, and refers to a technology in which a computer autonomously generates an algorithm necessary for determination such as data identification, etc., from previously captured learning data, and applies the algorithm to new data to make predictions. The learning method for machine learning may be any one of supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or deep learning, and may be a learning method combining these learning methods, regardless of the learning method for machine learning. Machine learning methods include perceptron, deep learning, support vector machine, logistic regression, naive Bayes, decision tree, random forest, etc., and are not limited to the methods described in the present embodiment.” Furthermore, the claim does not specify how the machine learning model is trained but instead recites only that a model is trained using teacher data comprising inputs and correct-answer outputs. This is also merely a generic description of supervised learning. Here, the claim (1) recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; and (2) invokes computers or other machinery merely as a tool to perform an existing process. As such, the additional element is not indicative of a technical improvement and is instead merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Applicant further argues that the claims provide significantly more under Step 2B. Specifically, Applicant argues “Even if the claims were found to be directed to an abstract idea, the ordered combination of elements provides an inventive concept. Generating a learned model that bridges the gap between raw temporal graph coordinates and a specialized "eye map" scatter diagram (defined by an x-axis of a first area and a y-axis of a square-function area) is a non-conventional arrangement. The claims provide a highly specific technological solution that improves the accuracy of automated trend detection, which is "significantly more" than a generic computer instruction.
The argument is not persuasive. As described above, the additional element of machine learning is claimed and described in the specification in a manner which is not indicative of a technical improvement to one of ordinary skill in the art. The graph coordinates and scatter-diagram region labels are the outputs of the recited mathematical calculations. They are thus part of the abstract idea and cannot supply the inventive concept. Viewed as an ordered combination, calculating features, training a generic classifier on them, and inferring a label is the generic sequence of supervised learning, and applying generic machine learning to a new data set does not amount to significantly more. Here, the improvement is confined to the abstract idea itself. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Thus, the additional element, even considered in ordered combination with the claims as a whole, does not provide integration into a practical application under Step 2A, nor does it provide an inventive concept under Step 2B.
For the above reasons, the rejection of the claims under 35 U.S.C. 101 are maintained herein.
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 1-2, 4-10, 12-14, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-2, 4-10, 12-14, and 21-22 are directed to a product or system and thus fall within the statutory categories of invention. (Step 1: YES).
Step 2A - Prong 1
The Examiner has identified independent product non-transitory computer-readable recording medium claim 1 as the claim that represents the claimed invention for analysis and is similar to independent system claims 13 and 14. Claim 1 recites the limitations of:
1. A non-transitory computer-readable recording medium storing a program that causes a computer to execute a process performed in an information processing system, the process comprising:
acquiring data with which at least one of a year, a month, a date, an hour, a minute, or a second is associated;
extracting at least one analysis target from the data acquired at the acquiring;
creating a graph of a cumulative value of a value relating to the analysis target with respect to a period;
calculating a first area formed by the graph and an x-axis, and a second area formed by a square function of the graph and the x-axis;
training a learned model by performing a machine learning using teacher data, the teacher data comprising input data including coordinates of a plurality of training word appearance frequency graphs and correct answer output data including identification information of regions within a scatter diagram in which the first area is used as an x-axis and the second area is used as a y-axi
inputting coordinates of the created graph into the trained learned model;
inferring, using the trained learned model, a pattern of a graph shape as an identification of a specific region with the scatter diagram, the specific region indicating a trend of transition tendencies of an appearance frequency of the extracted analysis target;
storing, in a memory, data points representing the first area and the second area for the extracted analysis target, wherein the stored data points are associated with the inferred pattern;
generating screen information by accessing the stored data points for displaying the scatter diagram, the scatter diagram including the data points in correspondence with the graph shape patterns which have been inferred; and
transmitting the screen information of the screen to a terminal via a network and
in response to a user selection of one of the plurality of data points on the terminal, causing display of information corresponding to a selected analysis target, the information including at least one of a keyword, an appearance frequency, and a first appearance year.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mathematical Concepts”. The claim limitations delineated in bold above recite mathematical relationships, mathematical formulas or equations, and/or mathematical calculations, as they set forth or describe, for example, analysis of time series data, creating a graph, performing calculations, and inferring a pattern of a graph shape. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as mathematical relationships, mathematical formulas or equations, and/or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
These limitations, under their broadest reasonable interpretation, also cover performance of the limitation as mental processes. The claim limitations delineated in bold above recite observations, evaluations, and judgments as they set forth or describe, for example, analysis of time series data, creating a graph, performing calculations, and inferring a pattern of a graph shape.
Accordingly, the claim recites an abstract idea. The non-transitory computer-readable recording medium and information processing system in which the process is performed in claim 1 is just (1) applying generic computer components to the recited abstract limitations; and (2) generally linking the use of a judicial exception to a particular technological environment or field of use. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 13 and 14 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
The limitations together as a single abstract idea for Step 2A Prong Two and Step 2B rather than as a plurality of separate abstract ideas to be analyzed individually (see MPEP 2106.04(II)(B)).
Step 2A - Prong 2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
Claim 1: non-transitory computer-readable recording medium (preamble); information processing system in which the process is performed (preamble)
Claim 13: circuitry; memory
Claim 14: circuitry; memory
The computer hardware/software is/are 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.
The limitations drawn to training and using a “learned model”. The limitations are recited at a high level and described in the specification in a manner that does not convey a technological improvement to one of ordinary skill in the art. Even considering these limitations, the computer is still merely used in its ordinary capacity as a tool to perform the abstract idea. Here, the additional element of using a computer hardware amounts to 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 and are at a high level of generality. Therefore, claims 1, 13, and 14 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)
Step 2B
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 pg. 57 about implementation using general purpose or special purpose computing devices and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 13, and 14 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent Claims
Dependent claims 2, 4-10, 12, 21, and 22 further define the abstract idea that is present in independent claims 1, 13, and 14 and thus correspond to “Mathematical Concepts” 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 computer hardware/software is similarly 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. Therefore, the dependent claims are directed to an abstract idea without significantly more.
Thus, claims 1-2, 4-10, 12-14, and 21-22 are not patent-eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jones (US 2006/0248073 A1) discloses a system and/or method for providing search results in response to a search query. In this implementation a temporal profile of the search query is built from temporal data associated with documents retrieved in response to the search query. From features of the temporal profile it may determined whether the search query would benefit from relevance feedback from a user. If there is a determination that the search query will benefit from the relevance feedback, relevance feedback is sought from the user. Search results are provided based on the relevance feedback.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC T WONG whose telephone number is (571)270-3405. The examiner can normally be reached 9am-5pm M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC T WONG/Primary Examiner, Art Unit 3693
ERIC WONG
Primary Examiner
Art Unit 3693
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