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 .
Claim Objections
Claims 2, 11 and 18 are objected to because of the following informalities: ”the static objects” in lines 1-2 respectively should read “the one or more static objects”. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: ”the database” in lines 6, 8 and 10 should read “the one or more databases”. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: ”the database” in lines 7, 9 and 11 should read “the one or more databases”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 recites the limitation "the one or more databases" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation "the at least one image sensor" and “the vehicle” in lines 4-5. There is insufficient antecedent basis for these limitations in the claim.
Claim 17 recites the limitation "the at least one image sensor" and “the vehicle” in lines 5-6. There is insufficient antecedent basis for these limitations in the claim.
Claim Rejections - 35 USC § 103
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 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 1-4, 6-8, 10-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kabkab (US20230075493) hereafter Kabkab in view of Kabkab (single reference 103 as the claimed limitations are shown/disclose in multiple figs/embodiments).
1. Regarding claim 1, Kabkab discloses an automotive vehicle (figs 1-2, 10A, para 0035 discloses an automotive vehicle), comprising:
a vehicle navigation system (fig 2 element 220, para 0042 shows and discloses a vehicle navigation system);
a database comprising map data corresponding to road level latitude and longitude coordinates for a plurality of roadway features (paras 0042-0044 shows and discloses a database (memory 206 storing map data 210 (fig 2 shows database 210 with maps/Roadgraphs) comprising map data corresponding to road level latitude and longitude coordinates for a plurality of roadway features (para 0042-0043 disclose For instance, the map information may include one or more roadgraphs, graph networks or road networks of information such as roads, lanes, intersections and the connections between these features which may be represented by road segments. Each feature in the map may also be stored as graph data and may be associated with information such as a geographic location (i.e latitude, longitude, altitude etc.) and whether or not it is linked to other related features, for example, signage (e.g., a stop, yield or turn sign) or road markings (e.g., stop lines or crosswalks) may be linked to a road and an intersection, etc. In some examples, the associated data may include grid-based indices of a road network to allow for efficient lookup of certain road network features. meeting the claim limitations, examiner notes that the specifics of a plurality of roadway features are no required by the current claim));
at least one image sensor (fig 2 element 232, paras 0045-0046 shows and discloses cameras meeting the limitations of at least one image sensor); and
one or more processors in communication with the vehicle navigation system, the database, and the at least one image sensor (fig 2, para 0037, 0115, shows one or more processors 204 in communication with the vehicle navigation system 220, the database 210, and the at least one image sensor 232), the one or more processors (paras 0037, 0038) being configured to:
detect, based on image data from the at least one image sensor, positions of one or more static objects in proximity of the vehicle (figs 4A, 5B, paras 0008-0009, 0022, 0045, 0047, 0068-0070 disclose detect, based on image data from the at least one image sensor, positions of one or more static objects such as buildings, trees, signage, crosswalks or stop lines on the roadway, the presence of parked vehicles on a side of the roadway, etc. in proximity of the vehicle 100, examiner notes that the specifics of one or more static objects are not required by the current claim. Due to the recital of or only one is required to be met);
obtain road and traffic sign metadata from the database (paras 0042-0048, 0097 discloses the computing devices 202 using the maps (database 201) for navigating the vehicle using (obtain) the maps which includes information (metadata) about the roads, lanes, signs etc meeting the claims limitations, examiner notes that the specifics of road and traffic sign metadata are not required by the current claim);
perform traffic sign recognition by matching of the one or more static objects with road and traffic sign data from the database using an AI-based system (figs 4A, 5B, 8-9C, paras 0025, 0041-0045, 0096-0100, 0106, 0122 shows and discloses “Sign-object association associates (matches) the sign (i.e the identified/recognized the one or more static signs) real world sign with existing mapped signs (i.e the database) using AI (neural networks) based classifiers/systems meeting the above claims limitations, examiner notes that the specifics of matching are not required by the current claim); and
output results of matching the one or more static objects with road and traffic sign data from the database (paras 0039, 0041-0045, 0097-0101, 0104, 0112, 0122 discloses relaying/output the information to the vehicle (i.e autonomous vehicle 100) to stop accordingly (para 0104) based on the matching (association) as seen in the prior step meeting the claim limitations, examiner notes that the specifics of output are not required by the current claim). Before the effective filing date of the invention was made, different figs/embodiments in Kabkab are combinable. The suggestion/motivation would be a quick and real-time (i.e fast) system (para 0104).
2. Regarding claim 2, Kabkab discloses the automotive vehicle of claim 1 wherein the static objects are traffic signs (figs 4A-4B, 5B, 9A-9C and para 0022 disclose static objects are traffic signs).
3. Regarding claim 3, Kabkab discloses the automotive vehicle of claim 1 wherein road and traffic sign metadata comprise one or more of road markers, traffic signs, (figs 4A-4B, 5B, 9A-9C, paras 0042-0048, 0097 discloses the computing devices 202 using the maps (database 201) for navigating the vehicle using (obtain) the maps which includes information (metadata) about the roads, lanes, signs etc meeting the claims limitations, examiner notes that due to the recital of or only one is required to be met).
4. Regarding claim 4, Kabkab discloses the automotive vehicle of claim 1 wherein the one or more processors are configured to detect the positions of one or more static objects in proximity of the vehicle from the image data by performing object detection and using a pose estimation model to extract traffic sign image features, and wherein the one or more processors are configured to perform traffic sign recognition by matching the traffic sign image features extracted from the image data with traffic sign image features extracted from the road and traffic sign data (figs 4A-4B, 5B, 9B-9C and paras 0039, 0044-0045, 0047, 0097, 0100 wherein the one or more processors are configured to detect the positions of one or more static objects in proximity of the vehicle from the image data by performing object detection and using a pose estimation model to extract traffic sign image features, and wherein the one or more processors are configured to perform traffic sign recognition by matching the traffic sign image features extracted from the image data with traffic sign image features extracted from the road and traffic sign data).
5. Regarding claim 6, Kabkab discloses the automotive vehicle of claim 1 wherein the one or more processors are configured to: estimate a current location of the vehicle; and update the estimate of the current location of the vehicle based on results of matching the one or more static objects with known static objects from the database (figs 4A-4B, 5B, 9B-9C and paras 0039, 0041-0043, 0048, 0055 shows and disclose wherein the one or more processors are configured to: estimate a current location of the vehicle; and update the estimate of the current location of the vehicle based on results of matching the one or more static objects with known static objects from the database).
6. Regarding claim 7, Kabkab discloses the automotive vehicle of claim 1 wherein the output comprises, for a detected sign, a longitude, a latitude, and one or more of a height (paras 0043-0045 wherein the output comprises, for a detected sign, a longitude, a latitude, and one or more of a height (i.e altitude)), .
7. Regarding claim 8, Kabkab disclose the automotive vehicle of claim 1 wherein the one or more processors are configured to execute a map matching pipeline with an AI-model (paras figs 2, 4A-4B, 5B, 7A and paras 0076, 0090, 0103-0115 shows and discloses wherein the one or more processors are configured to execute a map matching pipeline with an AI-model (neural network) for localization of the vehicle).
8. Regarding claim 10, Kabkab discloses a method for performing traffic sign recognition (figs 1-2, 10A, 11 and paras 0003-0004 discloses a method for performing traffic sign recognition), the method comprising:
storing one (paras 0042-0044 shows and discloses a database (memory 206 storing map data 210 (fig 2 shows database 210 with maps/Roadgraphs, due to the recital of one or more only one is required to be met) comprising map data corresponding to road level latitude and longitude coordinates for a plurality of roadway features (para 0042-0043 disclose For instance, the map information may include one or more roadgraphs, graph networks or road networks of information such as roads, lanes, intersections and the connections between these features which may be represented by road segments. Each feature in the map may also be stored as graph data and may be associated with information such as a geographic location (i.e latitude, longitude, altitude etc.) and whether or not it is linked to other related features, for example, signage (e.g., a stop, yield or turn sign) or road markings (e.g., stop lines or crosswalks) may be linked to a road and an intersection, etc (i.e a plurality of features including road and traffic sign data). In some examples, the associated data may include grid-based indices of a road network to allow for efficient lookup of certain road network features. meeting the claim limitations));
detecting, based on image data from the at least one image sensor (fig 2 element 232, paras 0045-0046 shows and discloses cameras meeting the limitations of at least one image sensor), positions of one or more static objects in proximity of the vehicle (figs 4A, 5B, paras 0008-0009, 0022, 0045, 0047, 0068-0070 disclose detect, based on image data from the at least one image sensor, positions of one or more static objects such as buildings, trees, signage, crosswalks or stop lines on the roadway, the presence of parked vehicles on a side of the roadway, etc. in proximity of the vehicle 100, examiner notes that the specifics of one or more static objects are not required by the current claim. Due to the recital of or only one is required to be met);
obtaining road and traffic sign metadata from the database (paras 0042-0048, 0097 discloses the computing devices 202 using the maps (database 201) for navigating the vehicle using (obtain) the maps which includes information (metadata) about the roads, lanes, signs etc meeting the claims limitations, examiner notes that the specifics of road and traffic sign metadata are not required by the current claim);
perform traffic sign recognition by matching the one or more static objects with road and traffic sign data from the database using an AI-based system (figs 4A, 5B, 8-9C, paras 0025, 0041-0045, 0096-0100, 0106, 0122 shows and discloses “Sign-object association associates (matches) the sign (i.e the identified/recognized the one or more static signs) real world sign with existing mapped signs (i.e the database) using AI (neural networks) based classifiers/systems meeting the above claims limitations, examiner notes that the specifics of matching are not required by the current claim); and
output based on results of matching the one or more static objects with road and traffic sign data from the database (paras 0039, 0041-0045, 0097-0101, 0104, 0112, 0122 discloses relaying/output the information to the vehicle (i.e autonomous vehicle 100) to stop accordingly (para 0104) based on the matching (association) as seen in the prior step meeting the claim limitations, examiner notes that the specifics of output are not required by the current claim). Before the effective filing date of the invention was made, different figs/embodiments in Kabkab are combinable. The suggestion/motivation would be a quick and real-time (i.e fast) system/method (para 0104).
9. Claim 11 is s corresponding method claim of claim 2. See the corresponding explanation of claim 2.
10. Claim 12 is s corresponding method claim of claim 3. See the corresponding explanation of claim 3.
11. Claim 13 is s corresponding method claim of claim 4. See the corresponding explanation of claim 4.
12. Claim 15 is s corresponding method claim of claim 6. See the corresponding explanation of claim 6.
13. Claim 16 is s corresponding method claim of claim 7. See the corresponding explanation of claim 7.
14. Claim 17 is a corresponding non-transitory, computer-readable medium claim of claim 10. See the corresponding explanation of claim 10. Figs 2, 10A and para 0035-0037 shows and discloses memory 206 storing instructions executable by the one or more processors 204.
15. Claim 18 is a corresponding non-transitory, computer-readable medium claim of claim 3. See the corresponding explanation of claim 3.
16. Claim 19 is a corresponding non-transitory, computer-readable medium claim of claim 4. See the corresponding explanation of claim 4.
17. Claim 20 is a corresponding non-transitory, computer-readable medium claim of claim 6. See the corresponding explanation of claim 6.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kabkab in view of CN112767475B hereafter CN7475B.
18. Regarding claim 5, Kabkab discloses the automotive vehicle of claim 4 wherein the one or more processors are configured to match the traffic sign image features extracted from the image data with the traffic sign image features extracted from the road and traffic sign data using a model (figs 4A-4B, 5B-5C, 9B-9C and paras 0066, 0098-0106 shows and discloses wherein the one or more processors are configured to match the traffic sign image features extracted from the image data with the traffic sign image features extracted from the road and traffic sign data using a model(s))
CN7475B discloses a pyramid attention network (PAN) (page 1 discloses a pyramid attention network). Before the effective filing date of the invention was made, Kabkab and CN7475B are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be a lightweight and maximum efficiency system (page 1). Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of CN7475B in the vehicle of Kabkab to obtain the invention as specified in claim 5.
19. Claim 14 is s corresponding method claim of claim 5. See the explanation of claim 5.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kabkab in view of NPL8 (HD Map Generation from Vehicle Fleet Data for Highly Automated Driving on Highways, Christopher Doer et al., IEEE, 2020, Pages 2014-2020) hereafter NPL8.
20. Regarding claim 9 as best understood by the examiner, Kabkab discloses the automotive vehicle of claim 1 with the database (fig 1-2 element 210 maps/radiographs database). Kabkab is silent and however fails to disclose wherein the
NPL8 discloses wherein the (fig 1, page 2014 col 1, page 2020 (table 1) discloses SD maps and HD maps databases that includes the urban roads and the highways meeting the claim limitations). Before the effective filing of the invention was made, Kabkab and NPL8 are combinable because they are form the same filed of endeavor and are analogous art of vehicle data processing. The suggestion/motivation would be an advanced, efficient and accurate system (page 2020 col 1). Therefore it would be obvious and within one of ordinary skill in the art to have recognized the advantages of NPL8 in the system of Kabkab to obtain the invention as specified in claim 9.
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAYESH PATEL whose telephone number is (571)270-1227. The examiner can normally be reached IFW Mon-FRI.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at 571-270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAYESH A PATEL/Primary Examiner, Art Unit 2677
/JAYESH PATEL/
Primary Examiner
Art Unit 2677
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