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 . Claims 1-20 are pending and examined below. This action is in response to the claims filed 11/8/24.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 11/8/24, 1/17/25, 2/5/25, 5/2/25, and 8/28/25 have been received and considered to the extent possible within the time granted to for the reviewal of IDS submissions.
It is desirable to avoid the submission of long list of documents if it can be avoided. Eliminate clearly irrelevant and marginally pertinent cumulative information. If a long list is submitted, highlight those documents which have been specifically brought to applicant's attention and/or are known to be of most significance. See Penn Yan Boats, Inc. v. Sea Lark Boats, Inc., 359 F. Supp. 948, 175 USPQ 260 (S.D. Fla. 1972), aft 'd, 479 F.2d 1338, 178 USPQ 577 (5th Cir.1973), cert. denied, 414 U.S. 874 (1974). But cf. Molins PLC v. Textron Inc., 48 F.3d 1172, 33 USPQ2d 1823 (Fed. Cir. 1995).
An applicant's duty of disclosure of material and information is not satisfied by presenting a patent examiner with "a mountain of largely irrelevant [material] from which he is presumed to have been able, with his expertise and with adequate time, to have found the critical [material]. It ignores the real-world conditions under which examiners work." Rohm & Haas Co. v. Crystal Chemical Co., 722 F.2d 1556, 1573 [220 USPQ 289] (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984).
Patent applicant has a duty not just to disclose pertinent prior art references but to make a disclosure in such way as not to "bury" it within other disclosures of less relevant prior art; See Golden Valley Microwave Foods Inc. v. Weaver PopcornCo. Inc., 24 USPQ2d 1801 (N.D. Ind. 1992); Molins PLC v. Textron Inc., 26 USPQ2d 1889, at 1899 (D.Del. 1992); Penn Yan Boats, Inc. v. Sea Lark Boats, Inc. et al., 175 USPQ 260, at 272 (S.D.FI. 1972).
In the present case, IDS submitted on 11/8/24 contains 897 documents without any information on the relevance of each document. Applicants is/are required to disclose where in each reference claimed invention is discussed.
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 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.
(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.
Claims 1-5 and 11-18 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Latotzki (US 2017/0015312).
Regarding claims 1 and 14, Latotzki discloses a parking assist system including a system/ method implemented by a system of one or more processors, comprising: one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle and one or more processors configured to (¶60):
obtain an identification of a destination location to which the vehicle is to navigate (¶29 – target point corresponding to the recited information indicative of a destination location to which the vehicle is to navigate);
execute a machine learning model using the generated sensor data to determine a plurality of drivable spaces and a plurality of non-drivable spaces in the real-world environment surrounding the vehicle (¶28 and ¶60-61 – machine vision system corresponding to the recited machine learning model for determining and labeling of the drivable terrain (for example, ‘interlocking pavement type 1, 2, 3 . . . ’, ‘asphalt’, ‘gravel’ and the like) corresponding to the recited drivable spaces and of the non-drivable terrain corresponding to the recited non-drivable spaces in the real-world environment);
generate an occupancy grid comprising a plurality of grid locations each corresponding to a different one of the determined plurality of drivable spaces or the plurality of non-drivable spaces in the real-world environment surrounding the vehicle (¶60-61 – grid elements corresponding to the recited occupancy grid labeled based on drivability of the terrain as well as non-drivable terrain or obstacles (such as ‘man made structure’, ‘nature’, ‘human’, ‘animal’ or ‘vehicle’ or the like)); and
cause the vehicle to navigate based on a path from a current location of the vehicle to the destination location, wherein the path is generated based on the occupancy grid (¶60-61, ¶85-91 and Fig. 4A – path planning to end position in order to permit the car to drive to the end position based on the environmental factors).
Regarding claims 2 and 15, Latotzki further discloses as the vehicle navigates, obtain second sensor data and update the occupancy grid based on the sensor data; and adjust the path based on the updated occupancy grid (¶60-63 – during parking-driving execution corresponding to the recited as the vehicle navigates, the scene elements may be replanned, updated or revised as parts of the prior unknown or roughly detected scene elements may come into sensor range and by that be detected or determined more precisely, or the scene may change during execution).
Regarding claims 3 and 16, Latotzki further discloses update the occupancy grid based on the sensor data by changing grid location corresponding to a drivable space to instead correspond to a non-drivable space (¶60-63 – grid elements corresponding to the recited occupancy grid labeled based on drivability of the terrain as well as non-drivable terrain or obstacles (such as ‘man made structure’, ‘nature’, ‘human’, ‘animal’ or ‘vehicle’ or the like) where the drivability label can be updated based on updated sensor readings. For example, when a neighboring parking lot gets occupied, and by that time the scene is not drivable any more (the free space (FS) changes to occupied space (OS)) ).
Regarding claims 4 and 17, Latotzki further discloses update the occupancy grid by executing the machine learning model using the obtained second sensor data to determine a second plurality of drivable spaces and a second plurality of non-drivable spaces in the real-world environment surrounding the vehicle (¶60-61 – each grid element corresponding to the recited plurality of occupancy grids labeled based on drivability of the terrain as well as non-drivable terrain or obstacles (such as ‘man made structure’, ‘nature’, ‘human’, ‘animal’ or ‘vehicle’ or the like)).
Regarding claims 5 and 18, Latotzki further discloses generate the occupancy grid by inserting, into each of the plurality of grid locations, an indication of whether the grid location corresponds to a drivable space or a non-drivable space (¶60-61 – grid elements corresponding to the recited occupancy grid labeled based on drivability of the terrain as well as non-drivable terrain or obstacles (such as ‘man made structure’, ‘nature’, ‘human’, ‘animal’ or ‘vehicle’ or the like)).
Regarding claim 11, Latotzki further discloses wherein the one or more sensors comprise one or more of a camera, a radar, a lidar, or an ultrasonic sensor (¶60).
Regarding claim 12, Latotzki further discloses wherein the path is generated based on one or more cost metrics associated with the determinations of the plurality of drivable spaces and the plurality of non-drivable spaces (¶60-61 and ¶81-83 – path planning utilizing cost functions based on avoiding curbs/obstacles which are identified in the grid as drivability values in order to select a path).
Regarding claim 13, Latotzki further discloses wherein a plurality of paths are generated, and wherein the path is selected according to the cost metrics and a cost function which assigns costs to the paths (¶60-61 and ¶81-83 – path planning utilizing cost functions based on avoiding curbs/obstacles which are identified in the grid as drivability values in order to select a path).
Claim Rejections - 35 USC § 103
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, 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.
Claims 6 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Latotzki (US 2017/0015312), as applied to claims 5 and 18 above, in view of Hennessy et al. (US 2012/0179322).
Regarding claims 6 and 19, Latotzki further discloses different factors applied to classifying terrain as drivable or not drivable but does not limit the classifications to a binary value.
However, Hennessy discloses an autonomous vehicle navigation system including wherein the indication of whether the grid location corresponds to a drivable space or a non-drivable space is a binary value indicating whether grid location corresponds to an occupied space or a non-occupied space (¶92 - Each cell stores the probability of being occupied or free; in the present instance a binary representation is used.).
The combination the environmentally assessed park assist system of Latotzki with the binary occupancy grid based navigation system of Hennessy fully discloses the elements as claimed.
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the environmentally assessed park assist system of Latotzki with the binary occupancy grid based navigation system of Hennessy in order to simplify the search space allowing for convenient path planning (Hennessy - ¶95).
Claims 7-10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Latotzki (US 2017/0015312), as applied to claims 1 and 14 above, in view of Goldstein et al. (US 2020/0250196).
Regarding claims 7 and 20, Latotzki does not disclose the utilization of a mobile device location however Goldstein discloses a rideshare system including wherein the destination location is selected via a user interface of an application configured to execute on a mobile device (¶45 – pickup location can be selected by a user via an interface on the user device corresponding to the recited mobile device. The combination of the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein fully discloses the elements as claimed).
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein in order to prevent negative user experiences associated with ridesharing systems (Goldstein - ¶1).
Regarding claim 8, Latotzki does not disclose the utilization of a mobile device location however Goldstein further discloses wherein the destination location is based on a global positioning system location associated with a mobile device (¶40 – location data provided from the user device corresponding to the recited global positioning system associated with the mobile device for identifying the location of the user. The combination of the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein fully discloses the elements as claimed).
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein in order to prevent negative user experiences associated with ridesharing systems (Goldstein - ¶1).
Regarding claim 9, Latotzki does not disclose the utilization of a mobile device location however Goldstein further discloses wherein during navigation the destination location is updated based on the location associated with the mobile device (¶40 – location data provided from the user device corresponding to the recited global positioning system associated with the mobile device for identifying the location of the user where the data showing the user is moving towards a rendezvous location corresponding to the recited updating the location associated with the mobile device. The combination of the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein fully discloses the elements as claimed).
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein in order to prevent negative user experiences associated with ridesharing systems (Goldstein - ¶1).
Regarding claim 10, Latotzki does not disclose the utilization of a mobile device location however Goldstein further discloses wherein navigation is aborted in response to information indicating lack of user input to an application configured to execute on a mobile device (¶40 – if the user does not show up to the rendezvous location and does not acknowledge potentially changed rendezvous location or a notification that the transport is arriving, the service configuration can automatically cancel the request corresponding to the recited information indicating lack of user input to an application causing navigation to be aborted. The combination of the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein fully discloses the elements as claimed).
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the environmentally assessed park assist system of Latotzki with the ridesharing pickup system of Goldstein in order to prevent negative user experiences associated with ridesharing systems (Goldstein - ¶1).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 8-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 9, 13, 14, 19, and 22 of U.S. Patent No. US 11,567,514. Although the claims at issue are not identical, they are not patentably distinct from each other as follows:
Present application
US 18/941,837
Parent Patent
US 11,567,514
(Parallel to Method claim 14)
1. A system, comprising: one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle and one or more processors configured to: obtain an identification of a destination location to which the vehicle is to navigate;
execute a machine learning model using the generated sensor data to determine a plurality of drivable spaces and a plurality of non-drivable spaces in the real-world environment surrounding the vehicle;
generate an occupancy grid comprising a plurality of grid locations each corresponding to a different one of the determined plurality of drivable spaces or the plurality of non-drivable spaces in the real-world environment surrounding the vehicle;
and cause the vehicle to navigate based on a path from a current location of the vehicle to the destination location, wherein the path is generated based on the occupancy grid.
(Parallel to Method claim 22)
1. A system, comprising: one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle;
a processor configured to: receive an identification of a geographical location associated with a target specified by a user remote from the vehicle;
receive the sensor data from the one or more sensors;
utilize a machine learning model trained by training data to generate a representation of the real-world environment surrounding the vehicle, the representation of the real-world environment surrounding the vehicle generated based on a drivable space predicted by the machine learning model based on the sensor data, wherein the training data is prepared before the sensor data is generated and the drivable space is used for calculating one or more cost metrics associated with the cost of traversing through respective locations in the real-world environment surrounding the vehicle, wherein the representation of the real-world environment surrounding the vehicle comprises an occupancy grid which includes one or more drivability values at each grid location of a plurality of grid locations which form respective portions of the occupancy grid, wherein each grid location corresponds to a location in the real-world environment, wherein a drivability value at a grid location represents a probability that a corresponding location in the real-world environment is drivable and is selected from a range of drivability values, and wherein the one or more cost metrics is calculated based on the drivability values;
calculate at least a portion of a path to a target location corresponding to the received geographical location using the representation of the real-world environment surrounding the vehicle;
and provide at least one command to automatically navigate the vehicle based on the path and updated sensor data from at least a portion of the one or more sensors of the vehicle, wherein as the vehicle automatically navigates, the processor is configured to obtain new sensor data and update the drivability values included in the occupancy grid, and wherein based on the drivability values the processor is configured to adjust the path;
and a memory coupled to the processor and configured to provide the processor with instructions.
8. The system of claim 1, wherein the destination location is based on a global positioning system location associated with a mobile device.
3. The system of claim 1, wherein the geographical location is based on a global positioning system location detected by a mobile device of the user.
9. The system of claim 8, wherein during navigation the destination location is updated based on the location associated with the mobile device.
2. The system of claim 1, wherein the target specified by the user is a dynamically updated current location of a mobile device of the user.
10. The system of claim 1, wherein navigation is aborted in response to information indicating lack of user input to an application configured to execute on a mobile device.
9. The system of claim 1, wherein the automatic navigation of the vehicle is aborted in response to a determination that a heartbeat signal has not been received from a device of the user, wherein the heartbeat signal is provided based on the user providing user input to an application executing on the device.
11. The system of claim 1, wherein the one or more sensors comprise one or more of a camera, a radar, a lidar, or an ultrasonic sensor.
13. The system of claim 1, wherein the one or more sensors of the vehicle include a plurality of cameras.
14. The system of claim 1, wherein the representation of the at least portion of the real-world environment is generated using auxiliary sensor data obtained using one or more of the following: an ultrasonic sensor or a radar sensor.
12. The system of claim 1, wherein the path is generated based on one or more cost metrics associated with the determinations of the plurality of drivable spaces and the plurality of non-drivable spaces.
13. The system of claim 12, wherein a plurality of paths are generated, and wherein the path is selected according to the cost metrics and a cost function which assigns costs to the paths.
19. The system of claim 1, wherein the occupancy grid includes a plurality of cost metrics associated with cost of traversing through the plurality of grid locations.
Claims 1, 7-14, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 12,164,310. Although the claims at issue are not identical, they are not patentably distinct from each other as follows:
Present application
US 18/941,837
Parent Patent
US 12,164,310
(Parallel to Method claim 14)
1. A system, comprising: one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle and one or more processors configured to:
obtain an identification of a destination location to which the vehicle is to navigate;
execute a machine learning model using the generated sensor data to determine a plurality of drivable spaces and a plurality of non-drivable spaces in the real-world environment surrounding the vehicle;
generate an occupancy grid comprising a plurality of grid locations each corresponding to a different one of the determined plurality of drivable spaces or the plurality of non-drivable spaces in the real-world environment surrounding the vehicle;
and cause the vehicle to navigate based on a path from a current location of the vehicle to the destination location, wherein the path is generated based on the occupancy grid.
(Parallel to Method Claim 11 and Computer Readable Medium Claim 20)
1. A system, comprising: one or more sensors configured to generate sensor data by capturing a real-world environment surrounding a vehicle and one or more processors configured to:
obtain information indicative of a location to which the vehicle is to navigate;
generate a representation of the real-world environment surrounding the vehicle based on a machine learning model and the sensor data, by:
determining a plurality of drivable spaces and a plurality of non-drivable spaces surrounding the vehicle; and
generating an occupancy grid comprising a plurality of grid locations each corresponding to a different one of the determined plurality of drivable spaces or the plurality of non-drivable spaces,
wherein the occupancy grid includes one or more drivability values at each grid location of a plurality of grid locations, and wherein a drivability value at a grid location represents a numerical probability that a corresponding location in the real-world environment is drivable and is selected from a plurality of drivability values within a range of numerical drivability values;
cause the vehicle to navigate based on a path associated with navigating to the location, wherein the path is calculated based on the representation of the real-world environment;
as the vehicle navigates, obtain sensor data and update the drivability values included in the occupancy grid; and
adjust the path based on at least one updated drivability value.
7/20. The system of claim 1, wherein the destination location is selected via a user interface of an application configured to execute on a mobile device.
2/12. The system of claim 1, wherein the location is selected via a user interface of an application configured to execute on a mobile device.
8. The system of claim 1, wherein the destination location is based on a global positioning system location associated with a mobile device.
3/13. The system of claim 1, wherein the location is based on a global positioning system location associated with a mobile device.
9. The system of claim 8, wherein during navigation the destination location is updated based on the location associated with the mobile device.
4/14. The system of claim 3, wherein during navigation the location is updated based on the location associated with the mobile device.
10. The system of claim 1, wherein navigation is aborted in response to information indicating lack of user input to an application configured to execute on a mobile device.
5/15. The system of claim 1, wherein navigation is aborted in response to information indicating lack of user input to an application configured to execute on a mobile device.
11. The system of claim 1, wherein the one or more sensors comprise one or more of a camera, a radar, a lidar, or an ultrasonic sensor.
6/16. The system of claim 1, wherein the one or more sensors consist of cameras.
7/17. The system of claim 1, wherein the one or more sensors comprise one or more of a camera, a radar, or an ultrasonic sensor.
8/18. The system of claim 7, wherein the one or more sensors comprise a lidar.
12. The system of claim 1, wherein the path is generated based on one or more cost metrics associated with the determinations of the plurality of drivable spaces and the plurality of non-drivable spaces.
9/19. The system of claim 1, wherein the path is calculated based on one or more cost metrics associated with the drivability values.
13. The system of claim 12, wherein a plurality of paths are generated, and wherein the path is selected according to the cost metrics and a cost function which assigns costs to the paths.
10. The system of claim 9, wherein a plurality of paths are calculated, and wherein the path is selected according to the cost metrics and a cost function which assigns costs to the paths.
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Will et al. (US 2013/0080359) discloses a vehicle guidance assist system including identifying and classifying terrain utilizing binary classifiers (¶55).
Albaghajati et al. (US 2017/0199525) discloses a robotic system for mapping local terrain and determining whether an area is passable or unpassable (¶96-100).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew J Reda whose telephone number is (408)918-7573. The examiner can normally be reached on Monday - Friday 7-4 ET.
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/MATTHEW J. REDA/Primary Examiner, Art Unit 3665