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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/09/2025 has been entered.
This office action is in response to applicant’s arguments/remarks and amendments filed on 12/09/2025. Claims 1, 11, and 16 have been amended. Claim 5 has been cancelled. No Claims have been newly added. Accordingly, claims 1-4, and 6-20 are currently pending.
Response to Arguments
Applicant’s arguments, see applicant’s arguments and remarks, filed on I12/09/2025, with respect to the rejection of claims 1-3, 7, 9-13, and 16-18 under 35 U.S.C. 103 as being unpatentable over Schneider in view of Harmsen have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Schneider, Harmsen, and Iagnemma et al US 2018/0004206 A1 (hence Iagnemma) as detailed below.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 7, 9-13, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schneider et al US 2020/0180610 A1 (hence Schneider) in view of Harmsen et al US 2017/0177937 A1 (hence Harmsen) and Iagnemma et al US 2018/0004206 A1 (hence Iagnemma).
In re claims 1, 11, and 16, Schneider discloses a method for at least partially automated operation of a vehicle based on determined driving maneuvers and traffic situations and teaches the following:
capturing, by a computing system through a sensor of a vehicle traveling through an area, a first image and a second image of the area (Paragraphs 0025)
determining, by a computing system, one or more features of the area based on a first image of the area, the one or more features comprising at least one object captured in the area (Paragraph 0025, and 0029-0030);
classifying, by the computing system, the at least one object as a static object or a dynamic object (Paragraph 0028 “Moving and nonmoving objects in the vehicle surroundings are detected and classified by means of the surroundings sensor system”);
determining, by the computing system, one or more map features of the area based on the one or more features of the area (Paragraph 0029), the one or more map features including at least one of: road segment boundaries, lane markings, crosswalks, or traffic control elements (Paragraph 0030)
and determining, by the computing system, a scenario for the area based on (1) the at least one classified object and (2) the one or more map features (Paragraphs 0025 “classified object is the speed sign #16 in Fig.1, Paragraph 0028 states that “Multiple situation features are evaluated”, and Paragraphs 0029-0032)
and controlling or navigating, by the computing system, the vehicle based on the determined scenario (Paragraphs 0029-0032)
However, Schneider discloses moving and nonmoving objects in the vehicle surroundings are detected and classified by means of the surroundings sensor system (Paragraph 0028), but doesn’t explicitly teach the following:
classifying, by the computing system, the at least one object as a static object or a dynamic object based on changes of the at least one object between the first image and a second image of the area
storing, by the computing system, the determined scenario for the area and the one or more map features both in storage associated with the vehicle and in a map and scenario information database of a transportation management system, the transportation management system being configured to route a fleet of vehicles using the map and scenario information database
Nevertheless, Harmsen discloses a self-contained, low-cost, low-weight guidance system for vehicles (Abstract) and teaches the following:
classifying, by the computing system, the at least one object as a static object or a dynamic object based on changes of the at least one object between the first image and a second image of the area (Fig.18 and Paragraphs 0273-0285)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Schneider reference to include classifying objects as static or dynamic based on changes in images, as taught by Harmsen, with a reasonable expectation of success, in order to assist vehicle navigation and to avoid possible collisions (Harmsen, Abstract).
Nevertheless, Iagnemma discloses affecting functions of a vehicle based on function-related characteristics of its environment (Paragraph 0001) and teaches the following:
storing, by the computing system, the determined scenario for the area and the one or more map features both in storage associated with the vehicle and in a map and scenario information database of a transportation management system, the transportation management system being configured to route a fleet of vehicles using the map and scenario information database (Paragraph 0004 “fleet of autonomous vehicles”, and Paragraph 0095 “Function-related information generated by any of these processes (or any combination of them) can be stored in a database in memory units located on automated vehicles or located on a cloud server and accessed by wireless communication by numerous automated vehicles that have access to that communication network”)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Schneider reference to include saving the scenario on the vehicle and the network, as taught by Iagnemma, with a reasonable expectation of success, in order to use said scenario to modify (affect) the operation or performance of a vehicle such as an autonomous vehicle such as to affect the operation of software processes associated with such operational performance (Iagnemma, Paragraph 0095)
In re claims 2, 12, and 17, Schneider discloses the claimed invention as recited above but doesn’t explicitly teach the following:
wherein classifying the at least one object as a static object or a dynamic object comprises: determining, by the computing system, an object identified in the first image is identified in the second image; and classifying, by the computing system, the object as a static object or a dynamic object based on whether the object moves between the first image and the second image
Nevertheless, Harmsen discloses a self-contained, low-cost, low-weight guidance system for vehicles (Abstract) and teaches the following:
wherein classifying the at least one object as a static object or a dynamic object comprises: determining, by the computing system, an object identified in the first image is identified in the second image; and classifying, by the computing system, the object as a static object or a dynamic object based on whether the object moves between the first image and the second image (Fig.18 and Paragraphs 0273-0285, motivation to combine has been provided above)
In re claims 3, 13, and 18, Harmsen teaches the following:
wherein determining the scenario for the area comprises: generating, by the computing system, a first vector that represents the one or more features; and determining, by the computing system, a level of similarity between the first vector and a second vector that represents the scenario for the area, wherein the scenario for the area is determined based on satisfaction of a threshold level of similarity between the first vector and the second vector (Paragraph 0273 motivation to combine has been provided above)
In re claim 7, Schneider teaches the following:
determining, by the computing system, map features for a map of the area based on the one or more features, wherein the map features include at least one of a road segment length, a road segment quality, a roadway type, information describing traffic lanes, information describing a presence of one or more bike lanes, information describing a presence of one or more crosswalks, or information describing a presence of a zone (Paragraphs 0027-0031)
In re claim 9, Schneider teaches the following:
wherein the first image and the second image are captured at a predefined frequency (Paragraph 0025)
In re claim 10, Schneider teaches the following:
wherein the first image and the second image are captured by a camera of a vehicle as the vehicle travels the area, and wherein the area is unmapped (Fig.1 and Paragraph 0025)
Claim(s) 4, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schneider, Harmsen, Iagnemma, and further in view of Patel et al US 2019/0049948 A1 (hence Patel).
In re claims 4, 14, and 19, Schneider discloses the claimed invention as recited above but doesn’t explicitly teach the following:
determining, by the computing system, an identification code associated with the scenario; and communicating, by the computing system, the identification code and contextual information with a transportation management system, wherein the contextual information includes at least one of a calendar date, a day of week, a time of day, weather data, and location data
Nevertheless, Patel discloses operating a vehicle by switching between an autonomous control system within the vehicle and a remote operator (Abstract) and teaches the following:
determining, by the computing system, an identification code associated with the scenario; and communicating, by the computing system, the identification code and contextual information with a transportation management system, wherein the contextual information includes at least one of a calendar date, a day of week, a time of day, weather data, and location data (Paragraphs 0018, 0027-0028, 0033, and 0042-0044)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Schneider reference with the feature of communicating current maneuvering parameters between the autonomous vehicle and the teleoperation center, as taught by Patel, with a reasonable expectation of success, in order to monitor the environment of the vehicle and take precautionary measures during a handover process between the autonomous vehicle and the teleoperation center (Patel, Paragraph 0009).
Claim(s) 6, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schneider, Harmsen, Iagnemma, and further in view of Viswanathan US 2020/0201890 A1 (hence Viswanathan).
In re claim 6, Schneider discloses the claimed invention as recited above but doesn’t explicitly teach the following:
generating, by the computing system, a label for the area based on the one or more features; and training, by the computing system, a machine learning model based on labeled training data including the label for the area, wherein the scenario for the area is determined based on the machine learning model
Nevertheless, Viswanathan discloses a method, apparatus and computer program product are provided for constructing a high definition map from crowd sourced data using semantic attributes to bootstrap map construction (Abstract) and teaches the following:
generating, by the computing system, a label for the area based on the one or more features; and training, by the computing system, a machine learning model based on labeled training data including the label for the area, wherein the scenario for the area is determined based on the machine learning model (Paragraph 0048)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Schneider reference with the feature of labeling the images with identified features, with a reasonable expectation of success, in order to facilitate categorization of future map image data (Viswanathan, Paragraph 0048).
In re claims 15, and 20, Viswanathan teaches the following:
generating, by the computing system, a label for the area based on the one or more features; and applying, by the computing system, the label to a map associated with the area (Paragraph 0048, the motivation to combine has been recited above)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schneider, Harmsen, Iagnemma, and further in view of Zhang et al US 2022/0163348 A1 (hence Zhang).
In re claim 8, Schneider discloses the claimed invention as recited above but doesn’t explicitly teach the following:
wherein determining the scenario for the area comprises: matching, by the computing system, the one or more features with features associated with the scenario for the area; and determining, by the computing system, the one or more features matches a threshold number of the features associated with the scenario
Nevertheless, Zhang discloses acquiring azimuth angle data collected by a first sensor and acceleration data collected by a second sensor respectively and judging whether a turning event occurs at a current moment based on the azimuth angle data and the acceleration data (Abstract), and teaches the following:
wherein determining the scenario for the area comprises: matching, by the computing system, the one or more features with features associated with the scenario for the area; and determining, by the computing system, the one or more features matches a threshold number of the features associated with the scenario (Paragraphs 0027 and 0031)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Schneider reference to include identifying and positioning the positional change caused by a turning action, as taught by Zhang, with a reasonable expectation of success, so that a motion trajectory displayed on a map reflects the actual movement more truly (Zhang, Paragraph 0005).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI KHATIB whose telephone number is (571)270-1165. The examiner can normally be reached M-F: 9:00am-5:30pm.
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/RAMI KHATIB/Primary Examiner, Art Unit 3669