DETAILED ACTION
This office action is a non-final. Claims 1-4 and 7-9 are pending. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/9/2026 has been entered.
Status of Claims
Applicant’s amendment date 4/9/2026, amended claims 1 and 9; and canceled claims 5-6.
Response to Amendment
The previously pending rejection to claims 1-9, under 35 USC 101 (Alice), will be maintained.
Response to Arguments
Applicant’s arguments received on date 4/9/2026 have been fully considered, but they are not persuasive.
Response to Arguments under 35 USC 103:
With respect to the 35 USC 103 rejections, none of the prior art of record, taken individually or in any combination, teach, inter alia,
The prior art references most closely resembling the Applicant’s claimed invention Gregory (2018/0144351) in view of ICN Merger Guidelines Workbook, Prepared for the Fifth Annual ICN Conference in Cape Town. April 2006, hereinafter “ICN” in view of Lee (2016/0063526).
Neither Gregory et al., ICN et al., nor Lee et al. disclose when the combined market share rate of the own-company service and the other-company service is unknown, the processing circuitry is further configured to calculate a reference relative index indicating a relative ratio of the own-company service to the other-company service for all areas based on the number of service provision results of the own-company service and the number of service provision results of the other company service in all areas, and select, as the dominant area, an area where the relative index for each area exceeds a multiplication result of the reference relative index and a predetermined coefficient for threshold adjustment.
Response to Arguments under 35 USC 101:
Applicant asserts that “these features represent a clear improvement to the machine learning technology used to achieve the benefits of the claimed invention.” Examiner respectively disagrees.
The additional elements do not reflect an improvement in the functioning of a computer, or an improvement to another technology or technical field. For example, as recited to the claims 1 and 9, the limitations of “machine learning model” are not indicative of integration into a practical application, rather, it is generally linking the use of the judicial exception to a particular technological environment or field of use. (See MPEP 2106.05(h)).
Applicant asserts that "the present claims as a whole provide an improvement to this technological environment and also integrate any interpreted judicial exception into a clear practical application.” Examiner respectively disagrees.
As discussed, under the second prong of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55.
Here, under the second prong of Step 2A, the only additional elements beyond the recited abstract idea of claim 1, and similarly claim 9, are the recitation of “processing circuitry of a service demand potential prediction device, comprising: acquiring a number of service provision results for each of a target own-company service and an other company service used for calculating a relative index indicating a relative ratio of the own-company service with respect to the number of service provision results, for each predetermined area; calculating the relative index of the own-company service to the other-company service for each area based on the acquired number of service provision results for each of the target own-company service and the other-company service, for each area; selecting a dominant area in which the number of service provision results of the own-company service is already relatively dominant in comparison to the other company service, based on information including the calculated relative index for each area; performing machine learning using a characteristic amount representing characteristics of the selected dominant area as an explanatory variable and the number of service provision results of the own-company service in the selected dominant area as an objective variable, and constructs a machine learning model for predicting a demand for service provision in the dominant area, the characteristics including at least one of daytime and nighttime population by sex age in the dominant area aggregated on a time basis based on at least one of (a) the location information of terminals used by users, (b) a number of member stores corresponding to payment located in the dominant area, or (c) a characteristic amount representing the characteristics of the dominant area, the characteristics of areas including at least one of commercial areas, residential areas, industrial areas, urban centers, or suburban areas; predicting a service provision prediction number of the own-company service in a case where a non-dominant area is assumed to be a dominant area by inputting a characteristic amount representing the characteristics of the non-dominant area which is not the dominant area into the constructed machine learning model, and sets the obtained service provision prediction number as a service demand potential in the non-dominant area: and outputting, on a display, the service demand potential in the non-dominant area, wherein when a combined market share rate of the own-company service and the other-company service is known, the method further comprising selecting, as the dominant area, an area where a multiplication result of: the calculated relative index for each area, and the combined market share rate of the own-company service and the other-company service exceeds a predetermined threshold, and when the combined market share rate of the own-company service and the other-company service is unknown, the method further comprising calculating a reference relative index indicating a relative ratio of the own-company service to the other-company service for all areas based on the number of service provision results of the own-company service and the number of service provision results of the other-company service in all areas, and selecting, as the dominant area, an area where the relative index for each area exceeds a multiplication result of the reference relative index and a predetermined coefficient for threshold adjustment..,” and these additional elements, individually and in combination, are nothing more than computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Accordingly, contrary to Applicant’s assertions, the judicial exception is not integrated into a practical application under the second prong of Step 2A.
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 7-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-4 and 7-9 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1 and 9 recite an abstract idea. Claims 1 and 9 include “acquire a number of service provision results for each of a target own-company service and an other-company service used for calculating a relative index indicating a relative ratio of the own-company service with respect to the number of service provision results, for each predetermined area, calculate the relative index of the own-company service to the other-company service for each area based on the acquired number of service provision results for each of the target own-company service and the other-company service, for each area, select a dominant area in which the number of service provision results of the own-company service is already relatively dominant in comparison to the other-company service, based on information including the calculated relative index for each area, perform learning using a characteristic amount representing characteristics of the selected dominant area as an explanatory variable and the number of service provision results of the own-company service in the selected dominant area as an objective variable, and constructs a model for predicting a demand for service provision in the dominant area, the characteristics including at least one of daytime and nighttime population by sex age in the dominant area aggregated on a time basis based on at least one of (a) the location information of terminals used by users, (b) a number of member stores corresponding to payment located in the dominant area, or (c) a characteristic amount representing the characteristics of the dominant area, the characteristics of areas including at least one of commercial areas, residential areas, industrial areas, urban centers, or suburban areas, and predict a service provision prediction number of the own-company service in a case where a non-dominant area is assumed to be a dominant area by inputting a characteristic amount representing the characteristics of the non-dominant area which is not the dominant area into the constructed model, and sets the obtained service provision prediction number as a service demand potential in the non-dominant area; and output the service demand potential in the non-dominant area, wherein when a combined market share rate of the own-company service and the other-company service is known, the processing circuitry is further configured to select, as the dominant area, an area where a multiplication result of: the calculated relative index for each area, and the combined market share rate of the own-company service and the other-company service exceeds a predetermined threshold, and when the combined market share rate of the own-company service and the other-company service is unknown, the processing circuitry is further configured to calculate a reference relative index indicating a relative ratio of the own-company service to the other-company service for all areas based on the number of service provision results of the own-company service and the number of service provision results of the other company service in all areas, and select, as the dominant area, an area where the relative index for each area exceeds a multiplication result of the reference relative index and a predetermined coefficient for threshold adjustment”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and mathematical calculations because the elements describe a process for demand potential prediction. As a result, claims 1 and 9 recite an abstract idea under Step 2A Prong One.
Claims 2-4 and 7-8 further describe the process for demand potential prediction. As a result, claims 2-4 and 7-8 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1 and 9.
With respect to Step 2A Prong Two of the framework, claims 1 and 9 do not include additional elements that integrate the abstract idea into a practical application. Claims 1 and 9 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1 and 9 include a machine learning model, processing circuitry, and a display device. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1 and 9 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 3-4 and 7-8 do not include any additional elements beyond those recited with respect to claims 1 and 9. As a result, claims 3-4 and 7-8 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1 and 9.
Claim 2 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claim 2 includes a machine learning model. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 2 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1 and 9 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1 and 9 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1 and 9 include a machine learning model, processing circuitry, and display device. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1 and 9 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 3-4 and 7-8 do not include any additional elements beyond those recited with respect to claims 1 and 9. As a result, claims 3-4 and 7-8 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1 and 9.
Claim 2 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claim 2 includes a machine learning model. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 2 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-4 and 7-9 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2010/0106561; US Pat No. 9,691,046; US Pub No. 2010/0057525; US Pub No. 2021/0065091; US Pub No. 2020/0160237; US Pub No. 2012/0303411; US Pub No. 2004/0249696; and A Mottaghi, TT Nguyen (Strategic Performance Management & Optimization of Financial Decision Making in Micro-Enterprise (Service Sector in Construction Industry), 2019, theseus.fi.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ A KASSIM whose telephone number is (571)272-8534. The examiner can normally be reached 9:00 - 5:00 PM.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 5/19/2026
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