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 .
Response to Amendment
1. Claims 1-2, 4-9, 11-12, and 14-19 are currently pending.
2. Claims 3, 10, 13, and 20 are canceled.
3. Claims 1, 4-5, 7-9, 11, 14-15, and 17-19 are currently amended.
4. The 101 rejections to Claims 1 and 11 have been overcome.
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 1-2, 4-9, 11-12, and 14-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tjokro (US 20240401961 A1).
8. Regarding Claim 1, Tjokro 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]),
Wherein the processor is further configured 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], [0051], and [0053] Note that under the broadest reasonable interpretation, optimizing the K clusters of recommendations in the dataset based on the closest distance to the respective centroid is equivalent to scoring a distance between the user and POI.);
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]);
Wherein the processor is further configured to output the at least one place the requestor is expected to visit via an output device (Tjokro: [0030] 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 4, Tjokro remains as applied above in Claim 1, 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]).
11. Regarding Claim 5, Tjokro remains as applied above in Claim 1, and further, teaches to generate the first model based on a TimelyRec model (Tjokro: [0035]).
12. 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]).
13. Regarding Claim 7, Tjokro remains as applied above in Claim 1, 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]).
14. Regarding Claim 8, Tjokro remains as applied above in Claim 1, and further, teaches to personalize the scored distance value for each user based on a distance score model (Tjokro: [0051] and [0053]).
15. Regarding Claim 9, Tjokro remains as applied above in Claim 1, and further, teaches to generate the third model based on a multi-layer perceptron (MLP) neural network (Tjokro: [0090]).
16. 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]),
Training, by the processor, 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], [0051], and [0053] Note that under the broadest reasonable interpretation, optimizing the K clusters of recommendations in the dataset based on the closest distance to the respective centroid is equivalent to scoring a distance between the user and POI.),
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]);
And outputting, by an output device, the at least one place the requestor is expected to visit (Tjokro: [0030] and [0035]).
17. 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]).
18. Regarding Claim 14, Tjokro remains as applied above in Claim 11, 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]).
19. Regarding Claim 15, Tjokro remains as applied above in Claim 11, and further, teaches generating the first model based on a TimelyRec model (Tjokro: [0035]).
20. Regarding Claim 16, Tjokro remains as applied above in Claim 15, 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]).
21. Regarding Claim 17, Tjokro remains as applied above in Claim 11, and further, teaches scoring the distance between the user and the POI based on a radial basis function (RBF) kernel (Tjokro: [0051] and [0053]).
22. Regarding Claim 18, Tjokro remains as applied above in Claim 11, and further, teaches personalizing the scored distance value for each user based on a distance score model (Tjokro: [0051] and [0053]).
23. Regarding Claim 19, Tjokro remains as applied above in Claim 11, and further, teaches generating the third model based on a multi-layer perceptron (MLP) neural network (Tjokro: [0090]).
Response to Arguments
24. Applicant's arguments filed 5/5/2026 have been fully considered but they are not persuasive.
25. The Applicant has alleged "Tjokro fails to disclose at least the claimed second model" and that "Tjokro's similarity-based recommendation using histories of other users does not disclose generating or using such a personalized user-to-POI distance scoring model." The Examiner disagrees.
Tjokro teaches in [0017] that a drop off location may be determined based on the closest distance to the places of interest. Additionally, [0053] explains that the K clustering of POI recommendations is optimized by the closest distance to the respective centroid (drop-off location). This is equivalent to score a distance between the user and POI. The user's future location is at the drop-off point and the clustered recommendations are the POIs.
Therefore, under the broadest reasonable interpretation of the claims, optimizing based on the closest distance is equivalent to a distance score between the user and POI. The score is personalized because it is based on the user's future location and based on the POIs that are determined from the user preferences/interests. Tjokro teaches to filter points of interest using other users closest to the user, and based on the identified locations from the other users’ history, a K cluster is determined that is optimized for POIs closest in distance to the centroid where the user is dropped off.
26. Tjokro (US 20240401961 A1) teaches all aspects of the invention. The rejection is modified according to the newly amended language but still maintained with the current prior art of record.
27. Claims 1-2, 4-9, 11-12, and 14-19 remain rejected under their respective grounds and rational as cited above, and as stated in the prior office action which is incorporated herein. Also, although not specifically argued, all remaining claims remain rejected under their respective grounds, rationales, and applicable prior art for these reasons cited above, and those mentioned in the prior office action which is incorporated herein.
Conclusion
28. 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.
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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/MICHAEL T SILVA/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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