DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 19, 2026, has been entered.
In the response filed March 19, 2026, the Applicant amended claims 1, 2, 4, and 6-8; and canceled claim 5. Claims 1, 2, 4, and 6-8, are pending in the current application.
Notice of 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 Arguments
Applicant’s arguments for claims 1, 2, 4, and 6-8, with respect to the 35 U.S.C. 101 rejection have been considered but are unpersuasive. Applicant argues that the claims are eligible for integrating a judicial exception into a practical application as they reflect an improvement in the functioning of a computer or an improvement to other technology or technical field. Examiner respectfully disagrees.
First, Applicant argues that the claims are tied to a particular technological context such as “demand and supply in the market.” Examiner respectfully disagrees. Here, the alleged improvements are non-technical subjective/abstract improvements, not technical improvements to computers or technological processes, but addresses a business challenge regarding “accurate price predictions due to data volatility.” Providing such accurate price predictions is directed to, if anything, a business “improvement” (e.g., efficient methods and ways to determine prices). The idea of providing such accurate price predictions is not a patent eligible “improvement.” That a computer is used to make these calculations serves merely to implement the abstract idea on a generic computer.
Second, Applicant argues that the claims recite an improvement in computer functionality. Examiner respectfully disagrees. The requirement to execute the claimed steps/functions using “a non-transitory computer-readable recording medium,” “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 1); “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 7); and “an information processing apparatus comprising: a memory; and a processor,” “a machine learning models,” “the trained machine learning model,” (claim 8), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Applicant argues that the amended claims “generating smoothed slope time-series data … by applying a noise removal filter to the first time-series of slopes and the second time-series of slopes to reduce temporal variance, …;” recites a meaningful limitation that amounts to significantly more. Examiner respectfully disagrees. This specific limitation in the claim describe or set-forth training a machine learning model to estimate an intersection point of two curves for a later time period, which amounts to mathematical calculations. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s arguments remain unpersuasive. The 35 U.S.C. 101 rejection is hereby maintained.
Claim Objections
Claims 1, 7, and 8 are objected to because of the following informalities:
Claim 1, lines 9-10, “the demand curve creating” should read --the demand curve; creating--.
Claim 1, lines 16-17, “a machine learning models” should read --a machine learning model--.
Claim 1, lines 24-25, “the intersection point calculating” should read
--the intersection point; calculating--.
Claim 7, lines 8-9, “the demand curve creating” should read --the demand curve; creating--.
Claim 7, lines 15-16, “a machine learning models” should read --a machine learning model--.
Claim 7, lines 23-24, “the intersection point calculating” should read
--the intersection point; calculating--.
Claim 8, lines 9-10, “the demand curve creating” should read --the demand curve; creating--.
Claim 8, lines 16-17, “a machine learning models” should read --a machine learning model--.
Claim 8, lines 24-25, “the intersection point calculating” should read
--the intersection point; calculating--.
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, 2, 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.
Step 1: Claims 1, 2, 4, and 6, are drawn to a product of manufacture, claim 7 is drawn to a process, and claim 8 is drawn to a machine, each of which is within the four statutory categories (e.g., a process, a machine). (Step 1: YES).
Step 2A – Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1 (representative of claims 7 and 8) recites/describes the following steps:
“identifying an intersection point between a demand curve that indicates a relationship between an amount and a price of a buying order and a supply curve that indicates a relationship between an amount and a price of a selling order and has one intersection point with the demand curve in a first period;”
“creating a first linear function passing through the intersection point and a specific point on the demand curve”
“creating a second linear function passing through the intersection point and a specific point on the supply curve;”
“creating a first time-series of slopes from the first linear functions corresponding to the first time periods;”
“creating a second time-series of slopes from the second linear functions corresponding to the first time periods;”
“generating smoothed slope time-series data … by applying a noise removal filter to the first time-series of slopes and the second time-series of slopes to reduce temporal variance, …;”
“setting a first approximation expression as a linear function having the first smoothed slope and passing through the intersection point;”
“calculating a first intercept of the first approximation expression;”
“setting a second approximation expression as a linear function having the smoothed slope and passing through the intersection point;”
“calculating a second intercept of the second approximation expression;”
“generating the machine learning model by respectively performing training by using data of slopes and intercepts of the first approximation expression and the second approximation expression;” and
“estimating an intersection point between the demand curve and the supply curve in a second period that corresponds to a period after the first period ….”
These steps, under broadest reasonable interpretation, describe or set-forth training a machine learning model to estimate an intersection point of two curves for a later time period, which amounts to mathematical calculations. The steps of generating the machine learning model require specific mathematical calculations to perform the training of the machine learning model and therefore encompass mathematical concepts. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Each of the depending claims 2, 4, and 6 likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Any elements recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim.
Step 2A – Prong Two:
The claims recite the additional elements/limitations of: “a non-transitory computer-readable recording medium,” “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 1); “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 7); and “an information processing apparatus comprising: a memory; and a processor,” “a machine learning models,” “the trained machine learning model,” (claim 8).
The requirement to execute the claimed steps/functions using “a non-transitory computer-readable recording medium,” “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 1); “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 7); and “an information processing apparatus comprising: a memory; and a processor,” “a machine learning models,” “the trained machine learning model,” (claim 8), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Remaining dependent claims 2, 4, and 6 either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
As discussed above in “Step 2A – Prong 2,” the requirement to execute the claimed steps/functions using “a non-transitory computer-readable recording medium,” “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 1); “a computer,” “a machine learning models,” “the trained machine learning model,” (claim 7); and “an information processing apparatus comprising: a memory; and a processor,” “a machine learning models,” “the trained machine learning model,” (claim 8), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Remaining dependent claims 2, 4, and 6 either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: NO).
Allowable Subject Matter
Claims 1, 2, 4, and 6-8, would be allowable subject matter if revised and amended to overcome the rejection under 35 U.S.C. 101 as set forth in this Office action.
As per claim 1 (representative of claims 7 and 8), the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest “identifying an intersection point between a demand curve that indicates a relationship between an amount and a price of a buying order and a supply curve that indicates a relationship between an amount and a price of a selling order and has one intersection point with the demand curve in a first period; creating a first linear function passing through the intersection point and a specific point on the demand curve[;] creating a second linear function passing through the intersection point and a specific point on the supply curve; creating a first time-series of slopes from the first linear functions corresponding to the first time periods; creating a second time-series of slopes from the second linear functions corresponding to the first time periods; generating smoothed slope time-series data for training a machine learning models by applying a noise removal filter to the first time-series of slopes and the second time-series of slopes to reduce temporal variance, thereby improving training efficiency of the machine learning models; setting a first approximation expression as a linear function having the first smoothed slope and passing through the intersection point; calculating a first intercept of the first approximation expression; setting a second approximation expression as a linear function having the smoothed slope and passing through the intersection point[;] calculating a second intercept of the second approximation expression; generating the machine learning model by respectively performing training by using data of slopes and intercepts of the first approximation expression and the second approximation expression; and estimating an intersection point between the demand curve and the supply curve in a second period that corresponds to a period after the first period by using the trained machine learning model” This combination of functions/features would not have been obvious to a PHOSITA in view of the prior art.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Ching (US 6,078,901) discloses a system for non-arbitrary price determination and rational decision making. A spreadsheet establishes a deterministic relationship--described by an equal number of equations and unknowns--between the price and all the factors affecting the price in an expected time space extending from now to the infinite future. The infinite spreadsheet expands the current finite spreadsheet to infinity.
Conclusion
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/Patrick Kim/Examiner, Art Unit 3628