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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/21/2026 is being considered by the examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-4, and 6-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang (US20200124423A1) in view of Liu (US20230144288A1).
Regarding claim 1, Jiang teaches;
A computing system (taught as an autonomous vehicle network, paragraph 0019) comprising:
a communication interface to communicate, over one or more networks, with a plurality of human-driven vehicles operating throughout a region (taught as a network, element 102, which includes multiple autonomous vehicles, paragraph 0020, which also operate in a manual mode, paragraph 0020);
one or more processors (taught as processor, such as element 1501, paragraph 0060);
a memory storing instructions that, when executed by the one or more processors (taught as a memory, such as element 1503, paragraph 0060), cause the computing system to:
receive, over the one or more networks, sensor data from a subset of the plurality of human-driven vehicles that have moved through an intersection in the region, the intersection including multiple lanes (taught as lane information, including road segments, with reference points to all lanes of road segments of interest, paragraph 0046);
based on the sensor data, dynamically determine a plurality of trajectories of the subset of human-driven vehicles through the intersection (taught as a server, including a data analytics system, collecting driving statistics from either autonomous vehicles or regular vehicles driven by human drivers, including driving commands, speed, accelerations, directions etc., paragraph 0030), and trajectories from a number of vehicles, paragraph 0031), the set of trajectories indicating a temporal sequence of motion of the plurality of human-driven vehicles passing through the intersection (taught as collecting trajectories of vehicles for a period of time for analysis, paragraph 0050, and using the current driving environment at a point in time to predict vehicle behavior, paragraph 0038);
superimpose each trajectory of the plurality of trajectories to a corresponding lane of the multiple lanes of the intersection (taught as projecting the trajectories onto a navigation map based on, for example, GPS coordinates, paragraph 0051; using GPS coordinates would, in the case of multiple lanes and trajectories corresponding to different lanes, be imposed onto each lane based on the coordinates);
based on each trajectory of the plurality of trajectories and the corresponding lane on which each trajectory is superimposed, (i) classify, by executing at least one of an artificial neural network or a machine learning model (taught as the data analytics system including a machine learning engine that generates or trains a set of rules, algorithms or models, paragraph 0032), driving behaviors of the subset of human-driven vehicles for each lane of the intersection (taught as generating/training a set of rules based on the driving statistics, paragraph 0032, metadata for objects [and their trajectories, paragraph 0039], including intersections based on trajectories, paragraph 0053) and (ii) identify a specific location or area in one or more of the multiple lanes of the intersection where a corresponding specific driving behavior occurs (taught as identifying information based on perception data, paragraph 0037, such as a left turn only lane, paragraph 0038, and storing information regarding the object describing the object, paragraph 0039);
determine a classification of the intersection [interpreted to mean identifying features of the intersection] based on the classified driving behaviors for each lane of the intersection, and the specific location or area in each of the one or more lanes of the intersection where corresponding driving behaviors occur (taught as determining intersection presence/features based on sensed data of trajectories; for example, determining an intersection presence if some trajectories turn left while others go straight or right, or the presence of a stop or pedestrian crosswalk based on other trajectory behaviors, as exemplified in paragraph 0053; further area/sub lane resolution is indicated in, for example, designating no lane changeable area, such as in paragraph 0052)
based on the classification, label an autonomy map to include pass- through information comprising a set of right-of-way rules for autonomous vehicles or semi- autonomous vehicles driving through the intersection (taught as generating and updating HD map label data for uploading onto the autonomous driving vehicles, paragraph 0054, based on the lane configurations detected based on trajectories detected, paragraph 0053, wherein lane configuration information “includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc.” using perception information (paragraph 0036)) the set of right-of-way rules indicating a priority amongst multiple competing pathways of vehicles traveling through the intersection (taught as indicating left-turn only or right-turn only lane configurations, paragraph 0038 and traffic light or stop sign/line information, which can reasonably, by one of ordinary skill in the art, be recognized as an asymmetric intersection if one direction has a stop and another does not, for example);
wherein dynamically determining the plurality of trajectories includes detecting, in real-time, changes in the set of trajectories (taught as the perception and planning system obtaining real-time traffic and environmental information, paragraph 0029, which would indicate knowledge of current trajectories in consideration for planning);
and update the autonomy map based on the changed classification (taught as updating a map based on the analysis of the trajectory information, paragraph 0032); and
distribute the updated autonomy map to control at least one of autonomous vehicles or semi-autonomous vehicles operating in a region of the intersection (taught as the generated HD map being uploaded and used for autonomous driving in real-time, paragraph 0054 [a server/database communicates with a plurality of vehicles to obtain information and provide maps, e.g as seen in the system shown in Fig 4]).
However, Jiang does not explicitly teach;
based on the changes to the plurality of trajectories, determine a change to the classification of the intersection, and update the autonomy map based on the changed classification.
Liu teaches; based on the changes to the plurality of trajectories, determine a change to the classification of the intersection, and update the autonomy map based on the changed classification (taught as updating road network topology timely, paragraph 0122, detecting a traffic anomaly based on the intersections, Fig 5 S501-502, determining candidate information, S505, and removing invalid information, S507; essentially, based on detecting changes/anomalies from trajectory information, update the map).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update a map as taught by Liu in the system taught by Jiang in order to improve relevance and provide accurate information. Liu suggests autonomous detection of traffic restriction information improves on traditional human labor based updates in cost, efficiency and accuracy (paragraph 0003). One of ordinary skill in the art would readily recognize that such updates would mesh with Jiang’s method of determining intersection information; while not explicitly updating based on changes in trajectory information, Jiang does teach the determining intersection information based on trajectories (paragraph 0053). In combination, by applying the more definitive recognition of a change/anomaly in an intersection as taught by Liu, Jiang would provide a more accurate, up to date map information to distribute.
Regarding claim 3, Jiang as modified by Liu teaches;
The computing system of claim 1 (see claim 1 rejection). Jiang further teaches; wherein the computing system classifies the intersection using at least one of a heuristic approach or a learning-based approach (taught as the data analytics using a machine learning engine, element 122, to generate/train rules, algorithms and models for analyzing collected data such as behavior data, paragraph 0032) based on a temporal and aggregated distribution of the plurality of trajectories represented by the map data (taught as analyzing trajectories, based on a period of time, paragraph 0050, into subsets, such as for determining number of lanes, paragraph 0051, lane width, paragraph 0052, and intersection data etc. paragraph 0053).
Regarding claim 4, Jiang as modified by Liu teaches;
The computing system of claim 1 (see claim 1 rejection). Jiang further teaches; wherein the instructions, when executed by the one or more processors, cause the computing system to classify the intersection by determining a crossing-type for a segment of each lane of the intersection based on the plurality of trajectories (taught as determining, based on trajectory data, the number of lanes, paragraph 0051, lane widths and divider information, paragraph 0052, and intersection information such as turning and stopping etc. paragraph 0053).
Regarding claim 6, Jiang as modified by Liu teaches;
The computing system of claim 1 (see claim 1 rejection). Jiang further teaches; wherein the set of right-of-way rules identify a region-specific norm of human drivers passing through the intersection (taught as trajectory data corresponding to a period of time, where the vehicle [vehicles can be autonomous or human driven, paragraph 0030] data collected based on vehicle perception data, paragraph 0050, for example, determining an intersection presence if some trajectories turn left while others go straight or right, or the presence of a stop or pedestrian crosswalk based on other trajectory behaviors, as exemplified in paragraph 0053).
Regarding claim 7, Jiang as modified by Liu teaches;
The computing system of claim 1 (see claim 1 rejection). Jiang further teaches; wherein the computing system executes a learning-based approach to process the plurality of trajectories (taught as the data analytics using a machine learning engine, element 122, to generate/train rules, algorithms and models for analyzing collected data such as behavior data, paragraph 0032) and classify the driving behavior of each human-driven vehicle through the intersection (taught as analyzing the collected data to determine driving behaviors of the vehicles, paragraph 0056).
Regarding claim 8, Jiang as modified by Liu teaches;
The computing system of claim 1 (see claim 1 rejection). Jiang further teaches; wherein the pass-through information comprises a set of labels indicating traffic signals and/or signage that control the intersection (taught as determining, based on the trajectory analysis, stop lines/signs at an intersection location, paragraph 0053).
Regarding claims 9, 11-12, 14-17, 19-20, it has been determined that no further limitations exist apart from those previously addressed in claims 1, 3-4 and 6-8. Therefore, claims 9, 11-12, 14-17, and 19-20 are rejected under the same rationale as claims 1, 3-4 and 6-8, where claims 9, 11-12 and 14-16 correspond to claims 1-4 and 6-8 respectively, and claims 17, 19-20 correspond to claims 1, 3-4 respectively.
Claim(s) 5, 13, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang (US20200124423A1) in view of Liu (US20230144288A1) and further in view of Levin (US20220341747A1).
Regarding claim 5, Jiang as modified by Liu teaches;
The computing system of claim 4 (see claim 4 rejection). Jiang further teaches; wherein the crossing-type for the lane segment of each lane of the intersection corresponds to a sign-controlled crossing-type (taught as determining an intersection has a stop line/sign, paragraph 0053), a signal-controlled crossing type (taught as detecting traffic light signals, paragraph 0035), an all-way stop crossing type (taught as detecting stop signs, paragraph 0035, which extrapolates to an all way stop if all branches of the intersection contain a stop sign), and a priority road crossing type (taught as detecting stop signs and yield signs, paragraph 0036; yielding/give way signs establish right of way/priority and appears to correspond to the intent of the specification that indicate two-way stops in paragraph 0014).
However, Jiang does not explicitly teach; and a no control [an intersection with no signals or signs, paragraph 0014] crossing type.
Levin teaches; a no control [an intersection with no signals or signs, paragraph 0014] crossing type (taught as using sensor data with known rules and expectations where stop signs and traffic lights are likely to be located, and upon determining there are none, determining the uncontrolled intersection, paragraph 0014).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to detect and classify an uncontrolled intersection as taught by Levin in the system taught by Jiang in order to improve safety. As taught by Levin, uncontrolled intersections create challenging conditions that require extra attention and caution (paragraph 0003), and thus should be accounted for in navigation and control. Using such information
Regarding claim 13 and 21, it has been determined that no further limitations exist apart from those previously addressed in claim 5. Therefore, claims 13 and 21 are rejected under the same rationale as claim 5.
Claim(s) 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang (US20200124423A1) as modified by Liu (US20230144288A1) and further in view of Omari (US20210166145A1).
Regarding claim 22, Jiang as modified by Liu teaches;
The computing system of claim 1 (see claim 1 rejection). Jiang further teaches; wherein dynamically determining the plurality of trajectories includes determining, for each trajectory of the plurality of trajectories, sensor data that is indicative of a corresponding driving behavior of the respective human-driven vehicle as the human-driven vehicle passes the intersection (taught as the decision module detecting and saving object metadata including speed, direction and turning angle, paragraph 0039; this is not explicitly human-driven vehicles only, but inclusive of all vehicles, and as the specification does not appear to explicitly derive whether a vehicle is human-driven or autonomous, would account for the scope of the claim).
However, Jiang does not explicitly teach; the sensor data including acceleration data.
Omari teaches the sensor data including acceleration data (taught as a perception module tracking vehicles, their trajectories and acceleration etc. paragraph 0058).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate acceleration data in the trajectory prediction system as suggested by Omari in the system taught by Jiang in order to improve predictions. As taught by Omari, the pose information derived from the perception data may be used to help determine what objects are expected in an environment (paragraph 0058) and generate predictions of the future environment (paragraph 0060). Furthermore, Jiang does suggest driving statistics including information indicating vehicle accelerations, decelerations (paragraph 0030), and thus would be obvious to use to further analyze driver behavior and trajectory planning/estimation.
Regarding claim 23, it has been determined that no further limitations exist apart from those previously addressed in claim 22. Therefore, claim 23 is rejected under the same rationale as claim 22.
Response to Arguments
Applicant argues on pages 11-13 of the remarks that the amended claims overcome the rejection of independent claim 1 over Jiang and Liu, especially regarding;
“superimpose each trajectory…to a corresponding lane of the multiple lanes of the intersection”,
“classify[ing]…driving behaviors of the subset of human-driven vehicles for each lane of the intersection” and
“identify[ing] a specific location or area in one or more of the multiple lanes of the intersection where a corresponding specific driving behavior occurs”.
The examiner respectfully disagrees. Jiang does suggest the superimposing trajectories in the form of projecting multiple trajectories (paragraph 0051); multiple trajectories being projected on the same map would effectively be superimposed as claimed. Jiang also suggests detection of multiple lanes (paragraph 0051) and sub-lane resolution with GPS data to distinguish specific areas within a lane segment/intersection for different behaviors (paragraphs 0052-0053), and thus would cover the lane designation/behavior based on the location and driving behavior as claimed.
Applicant argues on page 12 that Jiang cannot accomplish the results of the claimed invention, at least without perception data.
The examiner tentatively agrees that Jiang uses perception data to classify intersections; however, the claims do not specify that distinguishing the crossing-type occurs without perception data to separate, for example, stop sign and stop lights. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argues on pages 13-14 that Jiang does not sufficiently address the claimed material of claims 4-5, such that Jiang cannot distinguish between red light/stopping lines or a stop sign.
The examiner agrees that Jiang does not explicitly address the entire list of classifications, especially regarding the no control intersection, and withdraws the previous rejection. However, a new rejection in light of Levin is provided above. The examiner also notes Liu does identify a similar no control scenario in that expected controls are missing.
Applicant argues on pages 14-15 that, at least based on their dependency on allowable material, dependent claims 9-16 and 17-21 are also allowable.
Based on the above arguments and rejections, this argument is rendered moot.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For further determination of intersection rules/features; US20150134233A1 and US20230186646A1
For further trajectory use to analyze driving behavior and traffic rules; US20180066957A1 and US20200134325A1
For further updates to maps based on detected changes; US20210406559A1
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/GABRIEL ANFINRUD/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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