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 .
Status of Claims
This action is in reply to the Application Number 19/015,900 filed on 01/10/2025.
Claims 6-11 have been amended and are hereby entered.
Claims 1-11 are currently pending and have been examined.
This action is made FINAL in response to the “Amendment” and “Remarks” filed on 06/05/2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 30th, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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.
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.
Claims 1-6 are rejected under 35 U.S.C. 102 as being unpatentable over Ganjineh (U.S. Pub. No. 2020/0098135 A1).
Regarding Claim 1:
Ganjineh teaches:
An information processing apparatus comprising a controller, the controller being configured to execute: acquiring first map data including position information of roads; acquiring probe data including a set of position information of a first mobile body positioned on a road;, (See (Ganjineh: Summary of the Invention – 20th-27th, 44th-52nd, and 133rd paragraphs))
training a machine learning model using global positioning system (GPS) data of the probe data as input data and the first map data as ground truth data;, (See (Ganjineh: Summary of the Invention – 58th and 68th paragraphs and Detailed Description of the Preferred Embodiments – 158th-166th, 172nd-183rd, 189th, 209th, and 245th paragraphs, FIG. 2-5))
and converting second probe data including a set of position information of a second mobile body, into a road graph including position information of the roads, using the trained machine learning model., (See (Ganjineh: Summary of the Invention – 28th, 67th, and 122nd-127th paragraphs and Detailed Description of the Preferred Embodiments – 169th-181st and 214th paragraphs))
Regarding Claim 2:
Ganjineh, as shown in the rejection above, discloses the limitations of claim 1. Ganjineh further teaches:
The information processing apparatus according to claim 1, wherein the controller causes the machine learning model to, (See (Ganjineh: Summary of the Invention – 58th, 68th, and 133rd paragraphs and Detailed Description of the Preferred Embodiments – 209th paragraph))
[…] learn a relative positional relationship between the set of position information of the first mobile body and an actual road., (See (Ganjineh: Summary of the Invention – 20th-27th and 44th-52nd paragraphs))
Regarding Claim 3:
Ganjineh, as shown in the rejection above, discloses the limitations of claim 1. Ganjineh further teaches:
The information processing apparatus according to claim 1, wherein the first map data includes position information of centerline of the roads,, (See (Ganjineh: Summary of the Invention – 45th-47th paragraphs and Detailed Description of the Preferred Embodiments – 154th paragraph))
[…] and the controller trains the machine learning model using the position information of the centerline of the roads included in the first map data, as the ground truth data., (See (Ganjineh: Summary of the Invention – 26th, 58th, 68th, and 133rd paragraphs and Detailed Description of the Preferred Embodiments – 209th paragraph))
Regarding Claim 4:
Ganjineh, as shown in the rejection above, discloses the limitations of claim 3. Ganjineh further teaches:
The information processing apparatus according to claim 3, wherein the controller inputs set of position information included in the second probe data into, (See (Ganjineh: Summary of the Invention – 28th, 67th, 122nd-127th, and 133rd paragraphs and Detailed Description of the Preferred Embodiments – 169th-181st and 214th paragraphs))
[…] trained machine learning model and obtains a set of position information of the centerline of corresponding road, as an estimation result., (See (Ganjineh: Summary of the Invention – 45th-47th, 58th, and 68th-70th paragraphs and Detailed Description of the Preferred Embodiments – 154th, 209th, 227th, and 254th paragraphs))
Regarding Claim 5:
Ganjineh, as shown in the rejection above, discloses the limitations of claim 1. Ganjineh further teaches:
The information processing apparatus according to claim 1, wherein the probe data further includes data regarding position of road boundaries and/or lane boundaries,, (See (Ganjineh: Summary of the Invention – 45th and 98th-115th paragraphs and Detailed Description of the Preferred Embodiments – 234th-236th paragraphs))
[…] and the controller further includes the data in the input data to train the machine learning model., (See (Ganjineh: Summary of the Invention – 58th, 68th, and 133rd paragraphs and Detailed Description of the Preferred Embodiments – 209th paragraph))
Regarding Claim 6:
Ganjineh, as shown in the rejection above, discloses the limitations of claim 1. Ganjineh further teaches:
The information processing apparatus according to claim 1, wherein the set of position information of the first mobile body represents the GPS data, and the set of position information of the second mobile body represents second GPS data., (See (Ganjineh: Detailed Description of the Preferred Embodiments – 158th-166th, 172nd-183rd, 189th, and 245th paragraphs, FIG. 2-5))
Comment on the Closest Prior Art References
Claims 7-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The allowable subject matter in claim 7 includes converting second probe data into a road graph including: plotting points of GPS data, represented by a set of position information of a second mobile body, onto the road graph, implementing a gap interpolation process comprising connecting, as edges, ones of the points of the GPS data to the road graph, and implementing an edge deletion process comprising determining whether to delete any of the edges based on a number of vehicles predetermined to have passed through ones of the edges during a period of time.
The allowable subject matter in claim 8 includes converting second probe data into a road graph including: implementing a skeletonization process comprising reducing a line width of the road graph from two or more pixels to one pixel.
The allowable subject matter in claim 9 includes converting second probe data into a road graph including: determining whether to merge a plurality of intersections represented by the road graph into, in the road graph, a single intersection based on a distance between the plurality of intersections in the road graph.
The allowable subject matter in claim 10 includes converting second probe data into a road graph including: determining whether an intersection represented by the road graph is arranged at a multi-level crossing, comprising any of an overpass and an underpass, based on whether any one or more traffic flows, through an area represented by the intersection in the road graph, change direction at the area.
The allowable subject matter in claim 11 includes training a machine learning model using positional information of road centerlines included in the first map data as ground truth data and using the probe data as input data, such that the machine learning model learns a relative positional relationship between (i) a set of positional information of the first mobile body included in the probe data and (ii) positional information of the road centerlines included in the ground truth data and converting second probe data, including a set of positional information of a second mobile body, into a road graph including positional information of road centerlines by using the trained machine learning model.
Response to Arguments
Applicant’s arguments filed on June 5th, 2026 with regard to the 35 U.S.C. 102 rejection have been fully considered but are not persuasive.
With regard to the 35 U.S.C. 102 rejection, the limitations are taught in Ganjineh as has been set forth above, contrary to the Applicant’s assertions. Therefore, the Applicant’s amendments and arguments are insufficient to overcome these prior art rejections.
More specifically, See (Ganjineh: Summary of the Invention – 58th and 68th paragraphs and Detailed Description of the Preferred Embodiments – 158th-166th, 172nd-183rd, 189th, 209th, and 245th paragraphs, FIG. 2-5) In doing so, Ganjineh addresses the Applicant’s limitation of “training a machine learning model using global positioning system (GPS) data of the probe data as input data and the first map data as ground truth data” as set forth by the Applicant in claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jeffrey Chalhoub whose telephone number is (571) 272-9754. The examiner can normally be reached Mon-Fri 8:30-5:30. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on (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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/J.R.C./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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