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 03/19/2025 is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 6 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 6 recites “wherein the historical data comprises past trajectories and weather data “. However, Claim 1 already recites “obtaining historical data related to road users, including past trajectories, road conditions, traffic patterns, and weather data”. Since the definition of historical information in claim 6 is already defined in claim 1, claim 6 is not seen to meaningfully limit the claim beyond what has already been established in claim 1.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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) 1-2, 4-6, 12-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Merwaday (US20230300579A1) in view of Frazzoli (US9645577B1).
Regarding claim 1, Merwaday teaches;
A method comprising:
obtaining historical data related to road users, including past trajectories, [[road conditions]], traffic patterns, [[and weather data]] (taught as storing data in roadside units [RSU], such as traffic statistics, map data, paragraph 0086);
obtaining geo-fence data defining spatial boundaries or zones for monitoring road users (taught as a geo-fence for location aware devices/services, paragraph 0282);
generating probabilistic states for a future time based on the historical and geo- fence data (taught as the data fusion system predicting each object to timestamps at which no measurement is available, paragraph 0140 using a prediction state estimation algorithm including historical data and sensor data, paragraph 0151),
predicting future positions of road users (taught as using a prediction state estimation algorithm including historical data and sensor data, paragraph 0151;
determining spatial proximity data for a feature based on the geo-fence data (taught as vehicle communications including vehicle position/location data, paragraph 0034, and combined with map information of the relevant road sections, paragraph 0036);
generating a prediction based on the probabilistic state and the spatial proximity data, including trajectory, collision risk, or near-miss probabilities (taught as estimating a collision risk with a road user or object, paragraph 0101);
communicating the prediction to a road user or a roadside device (taught as an infrastructure centric roadside usage monitoring system, which receives and processes road information from and to vehicles, paragraph 0030); and
controlling the road user or roadside device based on the prediction, wherein controlling comprises adjusting speed, travel path, or timing to avoid collisions (taught as connecting to infrastructure services, such as traffic light controllers, to control infrastructure based on determined outcomes, paragraph 0116).
However, Merwaday does not explicitly teach; obtaining historical data related to road users, including past trajectories, road conditions, and weather data. (emphasis added).
Frazzoli teaches; obtaining historical data related to road users, including past trajectories, road conditions, and weather data (taught as historical information about driving properties of vehicles along the road section, column 11 lines 20-23, wherein historical information also includes behavior patterns of road users, time, seasonal and weather data, column 1 lines 41-47)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate historical information as taught by Frazzoli int eh system taught by Merwaday in order to improve predictions. Such information, as taught by Frazzoli, can be combined with real time data to form world models to generate control actions and help process things based on available resources , such as asynchronous processing(column 17 lines 44-51). To reiterate, one would incorporate further data into the historical record as information to be referenced, as taught by Frazzoli, into the system taught by Merwaday, which already includes some historical data in prediction scenarios, to further improve prediction of world states.
Regarding claim 2, Merwaday as modified by Frazzoli teaches;
The method of claim 1 (see claim 1 rejection). Merwaday further teaches; wherein the spatial proximity data is based on geo- fence boundaries determined from the geo-fence data (taught as a geo-fence for location aware devices/services, which provides virtual boundaries with location aware devices/services, paragraph 0282).
Regarding claim 4, Merwaday as modified by Frazzoli teaches;
The method of claim 1 (see claim 1 rejection). Merwaday further teaches; wherein generating the prediction comprises generating the prediction based on temporal data (taught as predictions for objects, involving timestamps, based on merged sensor fusion data, paragraph 0140, based on timestamps of measurements of detected objects, paragraph 0139).
Regarding claim 5, Merwaday as modified by Frazzoli teaches;
The method of claim 4 (see claim 4 rejection). Merwaday further teaches; wherein generating the prediction comprises generating the prediction based on the temporal data comprising road conditions and traffic patterns (Taught as the system contributing to sensor fusion with environmental data, paragraph 0138, including information on existing road traffic conditions, paragraph 0040).
Regarding claim 6, Merwaday as modified by Frazzoli teaches;
The method of claim 1 (see claim 1 rejection). However, Merwaday does not explicitly teach; wherein the historical data comprises past trajectories and weather data.
Frazzoli teaches; obtaining historical data related to road users, including past trajectories, road conditions, and weather data (taught as historical information about driving properties of vehicles along the road section, column 11 lines 20-23, wherein historical information also includes behavior patterns of road users, time, seasonal and weather data, column 1 lines 41-47)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate historical information as taught by Frazzoli int eh system taught by Merwaday in order to improve predictions. Such information, as taught by Frazzoli, can be combined with real time data to form world models to generate control actions and help process things based on available resources , such as asynchronous processing(column 17 lines 44-51). To reiterate, one would incorporate further data into the historical record as information to be referenced, as taught by Frazzoli, into the system taught by Merwaday, which already includes some historical data in prediction scenarios, to further improve prediction of world states.
Regarding claim 12, Merwaday as modified by Frazzoli teaches;
The method of claim 1 (see claim 1 rejection). Merwaday further teaches; wherein controlling the road user or roadside device based on the prediction comprises controlling the road user by changing a speed or travel path (taught as the roadside unit controlling ongoing vehicular and pedestrian traffic, paragraph 0086, such as reducing vehicle speeds, paragraph 0116).
Regarding claim 13, Merwaday as modified by Frazzoli teaches;
The method of claim 1 (see claim 1 rejection). Merwaday further teaches; wherein controlling the road user or roadside device based on the prediction comprises controlling the roadside device by changing a timing (taught as controlling a vehicle based on the prediction probabilities, including trajectory control and vehicle operations like steering, braking, acceleration etc. paragraph 0162).
Regarding claims 14-18, it has been determined that no further limitations exist apart from those previously addressed in claims 1-6. Therefore, claims 14-20 are rejected under the same rationales as claims 1-6, wherein claims 14-15 correspond to claims 1-2, claim 17 correspond to claim 4, and claim 18 corresponds to claim 6. claim 19 corresponds to claim 8, and claim 20 corresponds to claim 11.
Claim(s) 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Merwaday (US20230300579A1) as modified by Frazzoli (US9645577B1), and further in view of Reschka (US20200310450A1).
Regarding claim 3, Merwaday as modified by Frazzoli teaches;
The method of claim 2 (see claim 2 rejection). However, Merwaday does not explicitly teach; wherein the geo-fence boundaries comprise at least one of lane boundaries and sidewalks.
Reschka teaches; wherein the geo-fence boundaries comprise at least one of lane boundaries and sidewalks (taught as road segments including adjacent sidewalks and other lane features, paragraph 0037, wherein road segments are used to segment a map to provide a smaller map section for, for example, comparison to map data, paraph 0038, which includes positional information about cross walks, parking zones etc. paragraph 0025).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to define an area by lane boundaries of sidewalks as suggested by Reschka in the system taught by Merwaday in order to improve mapping data. As suggested by Reschka, such features create unique features/attributes to help differentiate them (paragraph 0037), which would help better localize features in a map relative. Furthermore, the exact location of the boundary for geo-fence data can be considered a matter of routine optimization, and obvious to choose the sidewalk/lane boundary as that is where traffic considerations would usually end.
Regarding claim 16, it has been determined that no further limitations exist apart from those previously addressed in claim 3. Therefore, claim 16 is rejected under the same rationale as claim 3.
Claim(s) 7-11 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Merwaday (US20230300579A1) as modified by Frazzoli (US9645577B1) and further in view of Newman (US20220289177A1).
Regarding claim 7, Merwaday as modified by Frazzoli teaches;
The method of claim 1 (see claim 1 rejection). However, Merwaday does not explicitly teach; wherein generating a prediction comprises generating a near-miss prediction.
Newman teaches; wherein generating a prediction comprises generating a near-miss prediction (taught as future position projections determining near-miss counts, paragraph 0019).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to predict/account for near-misses as taught by Newman in the system taught by Merwaday in order to improve collision avoidance/predictions. As suggested by Newman, such accommodation of near-misses should be accounted for, to err on the side of safety, similarly to projected collisions for the purposes of determining imminency (paragraph 0019).
Regarding claim 8, Merwaday as modified by Frazzoli and Newman teaches;
The method of claim 7 (see claim 7 rejection). However, Merwaday does not explicitly teach; wherein the near-miss prediction comprises determining a time-to-collision threshold for a plurality of road users.
Newman teaches; wherein the near-miss prediction comprises determining a time-to-collision threshold for a plurality of road users (taught as determining a projected collision time between vehicles, based on a kinematic model, paragraph 0036).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to predict/account for near-misses as taught by Newman in the system taught by Merwaday in order to improve collision avoidance/predictions. As suggested by Newman, such accommodation of near-misses should be accounted for, to err on the side of safety, similarly to projected collisions for the purposes of determining imminency (paragraph 0019). Time to collision additionally helps determine the time limit to act and force a best action to be selected (paragraph 0049).
Regarding claim 9, Merwaday as modified by Frazzoli and Newman teaches;
The method of claim 8 (see claim 8 rejection). However, Merwaday does not explicitly teach; wherein generating the near-miss prediction by comparing distances between road user using a predicted probabilistic trajectory and a minimum safe distance, and performing an operation when the distances are below a distance threshold.
Newman teaches; wherein generating the near-miss prediction by comparing distances between road user using a predicted probabilistic trajectory and a minimum safe distance, and performing an operation when the distances are below a distance threshold (Taught as a separation distance between future positions of the vehicles, and comparing to a predetermined distance limit to trigger action, paragraph 0024).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to predict/account for near-misses as taught by Newman in the system taught by Merwaday in order to improve collision avoidance/predictions. As suggested by Newman, such accommodation of near-misses should be accounted for, to err on the side of safety, similarly to projected collisions for the purposes of determining imminency (paragraph 0019). Time to collision additionally helps determine the time limit to act and force a best action to be selected (paragraph 0049).
Regarding claim 10, Merwaday as modified by Frazzoli and Newman teaches;
The method of claim 7 (see claim 7 rejection). However, Merwaday does not explicitly teach; wherein the near-miss prediction comprises determining a time-to-collision threshold at a plurality of time steps based on the probabilistic state and road user velocities.
Newman teaches; wherein the near-miss prediction comprises determining a time-to-collision threshold at a plurality of time steps based on the probabilistic state and road user velocities (taught as continually updating the kinematic model with sensor data, paragraph 0050).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate an iterative/updating process as taught by Newman in the system taught by Merwaday in order to improve predictions. Such updates/iterations allow for updates from sensor data to continually reassess the situation, imminency of collision etc., as taught by Newman (paragraph 0050).
Regarding claim 11, Merwaday as modified by Frazzoli and Newman teaches;
The method of claim 9 (see claim 1 rejection). However, Merwaday does not explicitly teach; wherein determining the time-to-collision threshold comprises dynamically adjusting the time-to-collision threshold dynamically based on updated weather and road conditions.
Newman teaches; wherein determining the time-to-collision threshold comprises dynamically adjusting the time-to-collision threshold dynamically based on updated weather and road conditions (taught as the kinetic model including environmental factors, such as rain or ice on the roadway, paragraph 0108).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to consider current weather/road conditions as taught by Newman in the system taught by Merwaday in order to further improve predictions. Such considerations obviously influence the kinetics of vehicles (e.g. friction), and thus influence the predicted trajectories, vectors etc. of any vehicle traveling in the vicinity.
Regarding claims 19-20, it has been determined that no further limitations exist apart from those previously addressed in claims 1-11.therefore, claims 19-20 are rejected under the same rationales as claims 8 and 11 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For further collision and trajectory predictions related to claim 1 (without the specific use of geo-fence/infrastructure); US320210004611A1.
A method comprising:
obtaining historical data related to road users, including past trajectories, road conditions, traffic patterns, and weather data; (taught as using several snapshots of the history of the environment to generate predictions, paragraph 0015, wherein the characteristics in the environment includes things like elements in the environment [e.g. traffic lights, lanes, and other infrastructure], time, states of entities in the environment, season, weather conditions, light levels etc. paragraph 0059)
[[obtaining geo-fence data defining spatial boundaries or zones for monitoring road users; ]]
generating probabilistic states for a future time based on the historical and geo- fence data (taught as generating prediction probabilities, which predict the future, paragraph 0015, or where objects would be in the future, paragraph 0016),
predicting future positions of road users (taught as determining prediction probabilities for objects in the environment, paragraph 0051);
determining spatial proximity data for a feature based on the geo-fence data (taught as map data associated with the environment, including positions of elements, paragraph 0034);
generating a prediction based on the probabilistic state and the spatial proximity data (taught as generating prediction probabilities associated with objects in the environment, paragraph 0063), including trajectory (taught as predicted trajectories, paragraph 0063), collision risk (taught as evaluating candidate actions [including predictions probabilities] to determine likelihood of collisions, paragraph 0029), or near-miss probabilities (taught as evaluating candidate actions [including prediction probabilities] to determine likelihood of near-collisions, paragraph 0029);
communicating the prediction to a road user or a roadside device (taught as communicating, such as audibly or with indicator lights, to pedestrians or other nearby vehicles, paragraph 0085, or to remote computing devices or remote services, paragraph 0086); and
controlling the road user or roadside device based on the prediction, wherein controlling comprises adjusting speed, travel path, or timing to avoid collisions (taught as controlling a vehicle based on the prediction probabilities, including trajectory control and vehicle operations like steering, braking, acceleration etc. paragraph 0162).
For further monitoring of a traffic space with infrastructure, using historical data to predict trajectories and collisions particularly related to claim 14; US20170287332A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL ANFINRUD whose telephone number is (571)270-3401. The examiner can normally be reached M-F 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at (571)270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GABRIEL ANFINRUD/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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