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 .
This is the first office action on the merits and is responsive to the papers filed on 10/29/2024. Claims 1-20 are currently pending.
Priority
1. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
2. The Information Disclosure Statement (IDS) submitted on 10/29/2024 has been considered by the Examiner.
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.
3. Subject Matter Eligibility Analysis of Claim 1 (see MPEP §2106.03):
As an apparatus, the claim is directed to a statutory category (Step 1).
Claim 1 is 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. Claim 1 is directed to acquiring data about a visit history of a requester (user) and to recommend a place the requestor is expected to visit based on the big data. This limitation akin to a mental process as a human mind can observe someone who follows a routine for a particular day or for an entire week, and based on the routine, can make a recommendation. For example, the human mind can observe someone who goes out to dinner every Saturday. Based on the data, restaurant can be recommended to the user before going out (Step 2A, Prong 1).
The applicant does not recite additional elements that integrate the judicial exception into a practical application (ex: controlling the vehicle based on the place the requestor is expected to visit). The applicant has recited a claim in which observe data and makes a recommendation based on the data (Step 2A, Prong 2).
The claim does not provide an inventive concept and the claim recites no additional elements. Accordingly, the lack of additional elements does not integrate the abstract idea into a practical application because there are no meaningful limits imposed on practicing the abstract idea (see MPEP §2106.05((I)(a)) (Step 2B).
In conclusion, Claim 1 is directed toward non-subject matter eligible material and is thus rejected under 35 U.S.C 101 as being patent ineligible.
4. Subject Matter Eligibility Analysis of Claim 11 (see MPEP §2106.03):
As a method, the claim is directed to a statutory category (Step 1).
Claim 11 is 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. Claim 1 is directed to acquiring data about a visit history of a requester (user) and to recommend a place the requestor is expected to visit based on the big data. This limitation akin to a mental process as a human mind can observe someone who follows a routine for a particular day or for an entire week, and based on the routine, can make a recommendation. For example, the human mind can observe someone who goes out to dinner every Saturday. Based on the data, restaurant can be recommended to the user before going out (Step 2A, Prong 1).
The applicant does not recite additional elements that integrate the judicial exception into a practical application (ex: controlling the vehicle based on the place the requestor is expected to visit). The applicant has recited a claim in which observe data and makes a recommendation based on the data (Step 2A, Prong 2).
The claim does not provide an inventive concept and the claim recites no additional elements. Accordingly, the lack of additional elements does not integrate the abstract idea into a practical application because there are no meaningful limits imposed on practicing the abstract idea (see MPEP §2106.05((I)(a)) (Step 2B).
In conclusion, Claim 11 is directed toward non-subject matter eligible material and is thus rejected under 35 U.S.C 101 as being patent ineligible.
Claim Rejections - 35 USC § 102
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
7. Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tjokro (US 20240401961 A1).
8. Regarding Claim 1, Tjorko teaches a device for controlling a vehicle, the device comprising (Tjokro: [0019]):
A navigation system configured to acquire big data including a point of interest (POI) with a visit history of a requestor who has requested a place recommendation (Tjokro: [0023] and [0035]);
And a processor configured to: perform preprocessing on the big data for learning acquired in advance to generate input data (Tjokro: [0031] and [0039]);
Train a POI recommendation model based on the input data (Tjokro: [0026] and [0035]);
And input the big data into the POI recommendation model that has been trained to generate at least one place the requestor is expected to visit (Tjokro: [0026] and [0035]).
9. Regarding Claim 2, Tjokro remains as applied above in Claim 1, and further, teaches to perform the preprocessing to extract user information and POI information from the big data for the learning and generate first preprocessing data (Tjokro: [0031] and [0032]);
Extract a POI with a history of being set as a destination from the first preprocessing data and generate second preprocessing data; extract only a POI category to be learned from the second preprocessing data and generate third preprocessing data (Tjokro: [0035]);
And remove a user and a POI with a visit frequency smaller than a predetermined number of times from the third preprocessing data to generate the input data (Tjokro: [0039]).
10. Regarding Claim 3, Tjokro remains as applied above in Claim 1, and further, teaches to train the POI recommendation model based on: a first model configured to learn a movement pattern of a user over time based on the input data (Tjokro: [0035]);
A second model configured to score a distance between the user and a POI and personalize the scored distance for each user (Tjokro: [0017] and [0051]);
And a third model configured to receive an age and a gender of the user, a POI category, and an output value output from the first model (Tjokro: [0035] and [0113]).
11. Regarding Claim 4, Tjokro remains as applied above in Claim 3, and further, teaches to train the POI recommendation model to dot-product an output value output from the second model and an output value output from the third model and output a place the user is expected to visit (Tjokro: [0035], [0049], and [0050]).
12. Regarding Claim 5, Tjokro remains as applied above in Claim 3, and further, teaches to generate the first model based on a TimelyRec model (Tjokro: [0035]).
13. Regarding Claim 6, Tjokro remains as applied above in Claim 5, and further, teaches the TimelyRec model includes a first learning device configured to learn a periodic behavior pattern of the user over the time and a second learning device configured to learn a sequential behavior pattern of the user over the time (Tjokro: [0035] and [0039]).
14. Regarding Claim 7, Tjokro remains as applied above in Claim 3, and further, teaches to score the distance between the user and the POI based on a radial basis function (RBF) kernel (Tjokro: [0051] and [0053]).
15. Regarding Claim 8, Tjokro remains as applied above in Claim 3, and further, teaches to personalize the scored distance value for each user based on a distance score model (Tjokro: [0051] and [0053]).
16. Regarding Claim 9, Tjokro remains as applied above in Claim 3, and further, teaches to generate the third model based on a multi-layer perceptron (MLP) neural network (Tjokro: [0090]).
17. Regarding Claim 10, Tjokro remains as applied above in Claim 1, and further, teaches to output the at least one place the requestor is expected to visit via an output device (Tjokro: [0030] and [0035]).
18. Regarding Claim 11, Tjokro teaches a method for controlling a vehicle, the method comprising (Tjokro: [0019]):
Acquiring, by a navigation system, big data including a point of interest (POI) with a visit history of a requestor who has requested a place recommendation (Tjokro: [0023] and [0035]);
Performing, by a processor, preprocessing on the big data for learning acquired in advance to generate input data (Tjokro: [0031] and [0039]);
Training, by the processor, a POI recommendation model based on the input data (Tjokro: [0026] and [0035]);
And inputting, by the processor, the big data into the POI recommendation model that has been trained to generate at least one place the requestor is expected to visit (Tjokro: [0026] and [0035]).
19. Regarding Claim 12, Tjokro remains as applied above in Claim 11, and further, teaches extracting user information and POI information from the big data for the learning and generating first preprocessing data (Tjokro: [0031] and [0032]);
Extracting a POI with a history of being set as a destination from the first preprocessing data and generating second preprocessing data; extracting only a POI category to be learned from the second preprocessing data and generating third preprocessing data (Tjokro: [0035]);
And removing a user and a POI with a visit frequency smaller than a predetermined number of times from the third preprocessing data to generate the input data (Tjokro: [0039]).
20. Regarding Claim 13, Tjokro remains as applied above in Claim 11, and further, teaches training the POI recommendation model based on a first model configured to learn a movement pattern of a user over time based on the input data (Tjokro: [0035]),
A second model configured to score a distance between the user and a POI and personalize the scored distance for each user (Tjokro: [0017] and [0051]),
And a third model configured to receive an age and a gender of the user, a POI category, and an output value output from the first model (Tjokro: [0035] and [0113]).
21. Regarding Claim 14, Tjokro remains as applied above in Claim 13, and further, teaches training the POI recommendation model to dot-product an output value output from the second model and an output value output from the third model and outputting a place the user is expected to visit (Tjokro: [0035], [0049], and [0050]).
22. Regarding Claim 15, Tjokro remains as applied above in Claim 13, and further, teaches generating the first model based on a TimelyRec model (Tjokro: [0035]).
23. Regarding Claim 16, Tjokro remains as applied above in Claim 13, and further, teaches the TimelyRec model includes a first learning device configured to learn a periodic behavior pattern of the user over the time and a second learning device configured to learn a sequential behavior pattern of the user over the time (Tjokro: [0035] and [0039]).
24. Regarding Claim 17, Tjokro remains as applied above in Claim 13, and further, teaches scoring the distance between the user and the POI based on a radial basis function (RBF) kernel (Tjokro: [0051] and [0053]).
25. Regarding Claim 18, Tjokro remains as applied above in Claim 13, and further, teaches personalizing the scored distance value for each user based on a distance score model (Tjokro: [0051] and [0053]).
26. Regarding Claim 19, Tjokro remains as applied above in Claim 13, and further, teaches generating the third model based on a multi-layer perceptron (MLP) neural network (Tjokro: [0090]).
27. Regarding Claim 20, Tjokro remains as applied above in Claim 11, and further, teaches outputting the at least one place the requestor is expected to visit via an output device (Tjokro: [0030] and [0035]).
Conclusion
28. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bortolussi (US 20180112995 A1)
St. Gray (US 20240210195 A1)
Zhou (US 20210302185 A1)
29. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL T SILVA whose telephone number is (571)272-6506. The examiner can normally be reached Mon-Tues: 7AM - 4:30PM ET; Wed-Thurs: 7AM-6PM ET; Fri: OFF.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached at 571-272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL T SILVA/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663