DETAIL ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice on Prior Art Rejections
2. 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 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.
Status of Claims
3. This Office Action is in response to the Applicant's application filed December 20, 2024. Claims 6-10 are presently pending and are presented for examination.
Objection
4. The disclosure is objected to because of the following informalities: The invention claims foreign priority to DE10 2022 002 334.2. However, the specification lacks cross-reference to this foreign priority. See (b) CROSS-REFERENCES TO RELATED APPLICATIONS: See 37 CFR 1.78 and MPEP § 211 et seq.
Appropriate correction is required.
Judicial Exception Claim Rejections - 35 USC § 101
5. 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.
6. Claims 6-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 6 recites “A method for determining and providing lane routes of streets by a central computing unit communicatively coupled to vehicles of a vehicle fleet, the method comprising: providing a geometric map having geometric lane boundaries; applying a grid with grid cells of a specified size to the geometric map; collecting fleet data from vehicles of the vehicle fleet, wherein the fleet data comprises position sequences covered by the vehicles of the vehicle fleet; determining vehicle orientations of the vehicles at positions of the grid cells from the collected position sequences; discretizing the determined vehicle orientations; generating a histogram for each individual grid cell position for the discretized vehicle orientations determined at the position of a respective grid cell; selecting a map section having a specified amount of grid cells; and determining the lane routes on the map section by a learning-based method from the histograms created for the grid cells of the map section and the geometric lane boundaries on the map section.”.
The limitations of claim 1 presented above, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a central computing unit” nothing in the claims elements precludes the steps from practically being performed as part of human activities. For example, “providing a geometric map having geometric lane boundaries”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind where a person is mentally able to generate a geometric map having geometric lane boundaries. Further, “determining vehicle orientations of the vehicles at positions of the grid cells from the collected position sequences”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind where a person is mentally able to observe vehicles orientation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. For example, “determining the lane routes on the map section by a learning-based method from the histograms created for the grid cells of the map section and the geometric lane boundaries on the map section” is not a practical application because it is a mere instruction to apply the judicial exception using generic elements. In particular, the claim does not recite any additional elements that integrate the abstract idea into a practical application. Accordingly, the claim lack of additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements that integrate the abstract idea into a practical application. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. The independent claims 7-10 are also rejected for their dependency upon claim 6.
Claim Rejections - 35 USC § 103
7. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
8. Claims 6-10 are rejected under 35 U.S.C 103 as being unpatentable over Omari et al, US 2020/0378776, in view of Piao et al. US 2020/0393265, hereinafter referred to as Omari and Piao, respectively.
Regarding claim 6, Omari discloses a method for determining and providing lane routes of streets by a central computing unit communicatively coupled to vehicles of a vehicle fleet, the method comprising:
providing a geometric map having geometric lane boundaries (See at least fig 1-7, ¶ 57, 59, 60, 61, 62, 63, 64, 4, 3, “A score that measures a comfort level associated with the potential route can be determined, wherein the score is determined based on at least one sensor map that segments the geographic region into a grid of cells”);
applying a grid with grid cells of a specified size to the geometric map (See at least fig 1-7, ¶ 37, 39, 40, 41, 43, 51, 52, 55, 10, “determine a set of cells in the acceleration map that represent the potential route; determine respective acceleration fingerprints for the set of cells; and determine the score measuring the comfort level for the potential route”);
collecting fleet data from vehicles of the vehicle fleet, wherein the fleet data comprises position sequences covered by the vehicles of the vehicle fleet (See at least fig 1-7, ¶ 1, 14, 33, 34, 35, 57, “The acoustic map 402 can be generated for a geographic region based on acoustic data collected by a fleet of vehicles ( e.g., the vehicle 640 as shown in FIG. 6) while navigating the geographic region”);
determining vehicle orientations of the vehicles at positions of the grid cells from the collected position sequences (See at least fig 1-7, ¶ 49, 73, “User device 630 may include functionality for determining its location, direction, or orientation, based on integrated sensors such as GPS, compass, gyroscope, or accelerometer.”);
discretizing the determined vehicle orientations (See at least fig 1-7, ¶ 43, 51, 57, 49, 59, “The acceleration map 432 can be discretized into a grid of cells. The cells are demarcated by grid lines, such as grid lines 442, 444. In various embodiments, a cell in the grid can be associated with one or more acceleration fingerprints. The acceleration fingerprints for the cell can be determined based on instances of acceleration data that were captured by the fleet of vehicles while navigating a geographic location represented by the cell”);
generating a histogram for each individual grid cell position for the discretized vehicle orientations determined at the position of a respective grid cell (See at least fig 1-7, ¶ 38, 40, 57, 50, 59, “the acceleration fingerprints 436, 440 can be represented as histograms that measure acceleration over various frequencies. In some embodiments, cells can be associated with multiple acceleration fingerprints. For example, a cell may be associated with multiple layers of acceleration data that were captured by different acceleration sensors of the fleet of vehicles.”);
selecting a map section having a specified amount of grid cells (See at least fig 1-7, ¶ 43, 44, 51, 40, 39, “The map grid module 306 can then discretize the map into a grid of cells. Each cell in the grid can represent some portion of the geographic region. In some embodiments, the cells are uniform in size. In other embodiments, the cells can vary in size”); and
determining the lane routes on the map section by a learning-based method from the histograms created for the grid cells of the map section and the geometric lane boundaries on the map section (See at least fig 1-7, ¶ 54, 55, 56, 61, 65, 53, “The vehicle routing module 342 can be configured to utilize sensor maps generated for geographic regions (e.g., acoustic maps, acceleration maps) to route a vehicle. As shown in the example of FIG. 3E, the vehicle routing module 342 can include a route module 344, a sensor map module 346, and a scoring module 348”).
Omari fails to explicitly disclose geometric lane boundaries.
however, Piao teaches geometric lane boundaries (See at least fig 1-31, ¶ 110, 131, 143, 147, 148, 164, 178, 191, 123, “The HD map system 100 may store objects or data structures that may represents lane elements that may comprise information representing geometric boundaries of the lanes”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Omari and include geometric lane boundaries as taught by Piao because it would allow an autonomous vehicle to safely navigate to various destinations without human input or with limited human input (Piao ¶ 54).
Regarding claim 7, Omari discloses the method of claim 6, wherein the determined lane routes are provided to the vehicles of the vehicle fleet for retrieval (See at least fig 1-7, ¶ 1, 14, 33, 34, 35, 57, 77, “The cameras may be used for, e.g., recognizing roads, lane markings, street signs, traffic lights, police, other vehicles, and any other visible objects of interest. To enable the vehicle 640 to "see" at night, infrared cameras may be installed. In particular embodiments, the vehicle may be equipped with stereo vision for, e.g., spotting hazards such as pedestrians or tree branches on the road.”).
Regarding claim 8, Omari discloses the method of claim 6, wherein the determined lane routes are provided to the vehicles as data in a digital map (See at least fig 1-7, ¶ 34, 35, 57, 59, 60, 61, 62, 63, 64, 4, 3, 10, “determine a set of cells in the acceleration map that represent the potential route; determine respective acceleration fingerprints for the set of cells; and determine the score measuring the comfort level for the potential route based at least in part on accelerative properties reflected in the acceleration fingerprints”).
Regarding claim 9, Omari discloses the method of claim 6, wherein the determined lane routes are supplied to a trajectory planning module of autonomously driving vehicles of the vehicle fleet (See at least fig 1-7, ¶ 43, 51, 57, 49, 56, 59, 49, “The vehicle model module 334 can provide trajectory information for a vehicle. For example, at any given time interval, the vehicle model module 334 can determine vehicle information”).
Regarding claim 10, Omari discloses the method of claim 6, wherein training the learning-based method takes place the generated histograms and by ground truth maps created for specified regions (See at least fig 1-7, ¶ 37, 39, 40, 41, 43, 51, 52, 55, 10, 56, “the machine learning model can be trained using examples of sensor data captured by a fleet of vehicles. The examples can train the machine learning model to recognize road features that are typically associated with passenger discomfort or unsafe driving conditions, such as speed bumps, unpaved sections, potholes, debris, bumpy road segments, stop signs, and traffic control devices”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS A MARTINEZ BORRERO whose email is luis.martinezborrero@uspto.gov and telephone number is (571)272-4577. The examiner can normally be reached on M-F 8:00-5:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HUNTER LONSBERRY can be reached on (571)272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LUIS A MARTINEZ BORRERO/Primary Examiner, Art Unit 3665