DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a non-final action in response to the amendments filed on 3/17/2026. Claims 1, 4-13 and 15-20 are currently pending and have been considered below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/17/2026 has been entered.
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, 4-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1, 4-13 and 15-20 are determined to be directed to an abstract idea.
The claims 1, 4-13 and 15-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), without providing a practical application integration and without providing significantly more.
As per Step 1 of the subject matter eligibility analysis, Claims 1, 4-13 and 15-20 are directed to a method (i.e., process), a system (i.e., machine/apparatus), and/or a non-transitory computer-readable medium (i.e., product), which are statutory categories of invention.
As per Step 2A-Prong 1 of the subject matter eligibility analysis, Claim 1, 15 and 16 are directed specifically to the abstract idea of an estimation which uses a learning model which has learned a relationship between first position information which is based on a position of a first location visited by a first user in past and second position information which is based on a position of a second location visited by the first user after the first location and a usage count which indicates a number of times the first user has used a service; the estimation comprising: acquiring third position information which is based on a position of a third location being a location visited by a second user in the past; and acquiring output of the learning model corresponding to the third position information as an estimation result of fourth position information which is based on a position of a fourth location being a location that is likely to be visited by the second user in future; and provide information determined based on the fourth position information to the second user; wherein the first position information includes a first central place being a central place which is based on a plurality of first locations visited by the first user in the past, wherein the second position information includes a second central place being a central place which is based on a plurality of second locations visited by the first user after the plurality of first locations, wherein the learning model has learned a relationship between the first central place of the first user and the second central place of the first user, wherein the third position information includes a third central place being a central place which is based on a plurality of third locations visited by the second user in the past, wherein the fourth position information includes a fourth central place being a central place which is based on a plurality of fourth locations that are likely to be visited by the second user in the future, and to acquire the output of the learning model corresponding to the third central place as an estimation result of the fourth central place of the second user; wherein the first position information further includes a first degree of variation being a degree of variation in a distance between the first central place and the position of each of the plurality of first locations, wherein the learning model has learned a relationship between the first central place and the first degree of variation of the first user and the second central place of the first user, wherein the third position information further includes a second degree of variation being degree of variation in a distance between the third central place and the position of each of the plurality of third locations, and to acquire output of the learning model corresponding to the third central place and the second degree of variation as the estimation result of the fourth central place of the second user; which include mental processes (observing and evaluating data regarding locations visited by users to make a judgement and opinion on future locations likely to be visited), and certain methods of organizing human activity based on fundamental economic practice (estimating location of future visits from customers/users), and based on managing personal behavior and interactions between people (following rules and instructions to estimating future locations likely to be visited by customers/users). Claims 2-13, 17-19 are directed to the abstract idea of claims 1, 15 and 16 with further details on the parameters/attributes of the abstract idea which includes mental processes and certain methods of organizing human activity for similar reasons as provided above for claim 1, 15 or 16. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
As per Step 2A-Prong 2 of the subject matter eligibility analysis, while the claims 1, 4-13 and 15-20 recite additional limitations which are hardware or software elements, such as a storage, at least one processor, a non-transitory computer-readable information storage medium for storing a program for causing a computer…, real-time, these limitations are not enough to qualify as a practical application being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
As per Step 2B of the subject matter eligibility analysis, while the claims 1, 4-13 and 15-20 recite additional limitations which are hardware or software elements, such as a storage, at least one processor, a non-transitory computer-readable information storage medium for storing a program for causing a computer…, real-time, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do provide significantly more to an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, since there are no limitations in the claims 1, 4-13 and 15-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments have been fully considered and would not overcome all of the rejections.
Arguments on rejections under 35 U.S.C. 101:
Applicant’s amendments merely add to the abstract idea. Further, applicant’s claimed invention does not pertain to the fact pattern of example 47. There is no improvement to the additional elements/technological elements beyond abstract idea. These limitations merely apply and/or generally link the abstract idea in a particular technological environment.
Arguments on rejections under 35 U.S.C. 102/103:
Rejections are withdrawn based on applicant’s amendments to the claims. Closest prior art to these claims include:
Grimes et al. (US-20150161665-A1) as applied as prior art in the rejection sections of previous Office actions,
Weiss et al. (US-20110099048-A1),
Weiss et al. (US-20170308908-A1),
Weiss et al. (US-20170316426-A1).
None of the prior art alone or in combination teaches the claimed invention; wherein the novelty is in combination of all the limitations and not in a single limitation.
Conclusion
Additional relevant prior art not relied upon includes:
Chang et al. (CN-107679661-A), regarding “calculating the user going to a probability of a next position based on the current position and returning the result are added to the route generating model based on a hidden Markov model, finally generating a route planning result, the route planning result as candidate routes into the candidate path set.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHMET YESILDAG whose telephone number is (571)272-3257. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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Sincerely,
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624