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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 02/27/2024, 12/05/2024, and 03/13/2025 have been considered by the Examiner.
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 (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(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.
Claims 1-3, 6-8, 10-14, and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Strohbeck (An Extension Proposal for the Collective Perception Service to Avoid Transformation Errors and Include Object Predictions, 2021 IEEE Vehicular Networking Conference).
Regarding claim 1, Strohbeck discloses a method of communication in an intelligent transport system, ITS, comprising, at an originating ITS station, ITS-S, monitoring an area:
transmitting a Collective Perception Message, CPM, comprising predicted data representing a predicted behavior of an object perceived by the originating ITS-S within the monitored area, the CPM further comprising an item of information characterizing the predicted behavior with regard to at least one element of the monitored area (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches).
Regarding claim 2, Strohbeck further discloses wherein the at least one element is different from the perceived object (In the first column of page 41, Strohbeck discloses that reliable and accurate information fusion between an internal environment model of the receiving vehicle and an environment model that was shared via CPM becomes possible, where a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; see also the second column on page 40 where Strohbeck discloses infrastructure environment model generation together with a prediction of the environment model).
Regarding claim 3, Strohbeck further discloses wherein the predicted behavior comprises a predicted position or a predicted trajectory including a set of consecutive predicted positions (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; in the second column of page 42, Strohbeck discloses encoding of up to 10 points per path for the representation of a predicted path from a plurality of multiple predicted paths per object, for example).
Regarding claim 6, Strohbeck further teaches wherein the item of information characterizing the predicted behavior comprises an indication of an existing or a predicted situation causing a possible occurrence of the predicted behavior (In the first column of page 41, Strohbeck discloses that reliable and accurate information fusion between an internal environment model of the receiving vehicle and an environment model that was shared via CPM becomes possible, where a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; see also the second column of page 42 where Strohbeck discloses that each predicted path for the encoding is assigned a probability, i.e. how likely it is that the respective path will be taken).
Regarding claim 7, Strohbeck further discloses determining the predicted behavior (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches).
Regarding claim 8, Strohbeck further discloses wherein determining the predicted behavior comprises accessing knowledge associated with elements of the area (In the first column of page 41, Strohbeck discloses that reliable and accurate information fusion between an internal environment model of the receiving vehicle and an environment model that was shared via CPM becomes possible, where a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; see also the second column on page 40 where Strohbeck discloses infrastructure environment model generation together with a prediction of the environment model).
Regarding claim 10, Strohbeck further discloses determining a state of the perceived object, wherein determining the predicted behavior is carried out as a function of the state of the perceived object, the item of information characterizing the predicted behavior comprising an indication of the state of the perceived object (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; in the second column of page 42, Strohbeck discloses transmission of motion predictions via CPMs, including a maximum of three predicted paths per perceived object with an assigned probability, and encoding of up to 10 points per path for the representation of a predicted path equally spaced in time, for example).
Regarding claim 11, Strohbeck further discloses determining an attitude of the perceived object, wherein determining the predicted behavior is carried out as a function of the attitude of the perceived object, the item of information characterizing the predicted behavior comprising an indication of the attitude of the perceived object (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; in the second column of page 42, Strohbeck discloses transmission of motion predictions via CPMs, including a maximum of three predicted paths per perceived object with an assigned probability, and encoding of up to 10 points per path for the representation of a predicted path equally spaced in time, for example).
Regarding claim 12, Strohbeck discloses a method of communication in an intelligent transport system, ITS, comprising, at a receiving ITS station, ITS-S:
receiving a Collective Perception Message, CPM, from an originating ITS-S, the received CPM comprising predicted data representing a predicted behavior of an object perceived by the originating ITS-S within a monitored area, the CPM further comprising an item of information characterizing the predicted behavior with regard to at least one element of the monitored area (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches), and
analyzing the received predicted behavior and the item of information characterizing the received predicted behavior to determine whether the received predicted behavior is to be considered (In the second column of page 40, Strohbeck discloses that using information exchange via the CPS, connected vehicles can enlarge their effective field of view and/or improve the accuracy of their environment model via information fusion methods).
Regarding claim 13, Strohbeck further discloses wherein the at least one element is different from the perceived object (In the first column of page 41, Strohbeck discloses that reliable and accurate information fusion between an internal environment model of the receiving vehicle and an environment model that was shared via CPM becomes possible, where a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; see also the second column on page 40 where Strohbeck discloses infrastructure environment model generation together with a prediction of the environment model).
Regarding claim 14, Strohbeck further discloses determining a predicted behavior of the perceived object, the determined predicted behavior or the received predicted behavior being considered as a result of the analyzing (In the second column of page 40, Strohbeck discloses that using information exchange via the CPS, connected vehicles can enlarge their effective field of view and/or improve the accuracy of their environment model via information fusion methods; in the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches).
Regarding claim 17, Strohbeck further teaches wherein the item of information characterizing the predicted behavior comprises an indication of an existing or a predicted situation causing a possible occurrence of the predicted behavior (In the first column of page 41, Strohbeck discloses that reliable and accurate information fusion between an internal environment model of the receiving vehicle and an environment model that was shared via CPM becomes possible, where a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; see also the second column of page 42 where Strohbeck discloses that each predicted path for the encoding is assigned a probability, i.e. how likely it is that the respective path will be taken).
Regarding claim 18, Strohbeck further discloses wherein the item of information characterizing the predicted behavior comprises a state or an attitude of the perceived object (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches; in the second column of page 42, Strohbeck discloses transmission of motion predictions via CPMs, including a maximum of three predicted paths per perceived object with an assigned probability, and encoding of up to 10 points per path for the representation of a predicted path equally spaced in time, for example).
Regarding claim 19, Strohbeck discloses a non-transitory computer-readable storage medium storing instructions of a computer program for implementing a method of communication in an intelligent transport system, ITS, comprising, at an originating ITS station, ITS-S, monitoring an area:
transmitting a Collective Perception Message, CPM, comprising predicted data representing a predicted behavior of an object perceived by the originating ITS-S within the monitored area, the CPM further comprising an item of information characterizing the predicted behavior with regard to at least one element of the monitored area (In the first column of page 41, Strohbeck discloses a possible extension of the CPM which adds the possibility to encode path predictions for PerceivedObjects, where such predictions are commonly required by automated vehicles during motion planning, and where a stationary intelligent transportation system (ITS) station like an RSU is not limited to detect objects via connected sensors, but it can also predict the future trajectories of the respective road users using e.g. modern deep learning approaches).
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.
Claims 4-5, 9, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Strohbeck (An Extension Proposal for the Collective Perception Service to Avoid Transformation Errors and Include Object Predictions, 2021 IEEE Vehicular Networking Conference),
in view of ETSI (Intelligent Transport Systems (ITS); Vulnerable Road Users (VRU) awareness; Part 1: Use Cases definition; Release 2, 2021-04).
Regarding claim 4, although in the second column of page 42 Strohbeck discloses that covariance matrices can be used to further assess the accuracy of the predicted path, which is important, e.g. for calculating risks of collision, Strohbeck does not explicitly disclose wherein the item of information characterizing the predicted behavior comprises a danger level associated with an occurrence of the predicted behavior.
However, ETSI teaches wherein the item of information characterizing the predicted behavior comprises a danger level associated with an occurrence of the predicted behavior (In section 6.3.2.4, ETSI teaches that the V-ITS-S makes a risk assessment, based on the VRU standard messages received and on behavioral models of the VRU, and identifies potential collision with a TTC less than 5 seconds, and where the VRU-St assesses the level of risk based on context perception, e.g. presence of other road users transmitting C-ITS messages and/or participation to traffic with increased risk of collision).
ETSI is considered to be analogous to the claimed invention in that they both pertain to determining a danger level for predicted behavior of traffic participants for use with ITS communications. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of ETSI with the method as disclosed by Strohbeck where doing so advantageously improves the accuracy of operations by increasing understanding of potential dangers or risks with the predicted behaviors, for example.
Regarding claim 5, although in the second column of page 42 Strohbeck discloses that each predicted path for the encoding is assigned a probability, i.e. how likely it is that the respective path will be taken, and where covariance matrices can be used to further assess the accuracy of the predicted path, which is important, e.g. for calculating risks of collision, Strohbeck does not explicitly disclose wherein the item of information characterizing the predicted behavior comprises a difficulty level representing a difficulty for predicting the behavior of the perceived object.
However, ETSI teaches wherein the item of information characterizing the predicted behavior comprises a difficulty level representing a difficulty for predicting the behavior of the perceived object (In section 7.2, ETSI teaches that when a predicted behaviour is indicated, it will be necessary also to provide the level of confidence associated to this prediction, where the level of confidence can be related to movement parameters (e.g. the mobile object velocity), but also to knowledge that may be acquired (via learning) of regular VRU behaviours).
ETSI is considered to be analogous to the claimed invention in that they both pertain to determining a difficulty associated with predicting traffic participant behaviour in an ITS communication setting. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of ETSI with the method as disclosed by Strohbeck, where doing so advantageously improves accuracy by denoting how confident the predictions are to correctly reflecting future behaviours of the object, thereby improving accuracy of operations which are performed in response to the determinations for example.
Regarding claim 9, Strohbeck does not explicitly disclose wherein the knowledge associated with elements of the area comprises observed past trajectories, vehicle timetables, school timetables, and/or local events.
However, ETSI teaches wherein the knowledge associated with elements of the area comprises observed past trajectories, vehicle timetables, school timetables, and/or local events (In section 7.9, ETSI teaches that behaviour prediction can be learned progressively by observing the VRU trajectories and velocity evolutions in different situations).
ETSI is considered to be analogous to the claimed invention in that they both pertain to predicting traffic participant behavior based on learning from past observed trajectories and behaviors. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of ETSI with the method as disclosed by Strohbeck, where doing so can advantageously improve the contextual accuracy of the behavior predictions by basing them on observed behaviors for example, thereby improving determinations made utilizing the predicted behaviors.
Regarding claim 15, although in the second column of page 42 Strohbeck discloses that covariance matrices can be used to further assess the accuracy of the predicted path, which is important, e.g. for calculating risks of collision, Strohbeck does not explicitly disclose wherein the item of information characterizing the predicted behavior comprises a danger level associated with an occurrence of the predicted behavior.
However, ETSI teaches wherein the item of information characterizing the predicted behavior comprises a danger level associated with an occurrence of the predicted behavior (In section 6.3.2.4, ETSI teaches that the V-ITS-S makes a risk assessment, based on the VRU standard messages received and on behavioral models of the VRU, and identifies potential collision with a TTC less than 5 seconds, and where the VRU-St assesses the level of risk based on context perception, e.g. presence of other road users transmitting C-ITS messages and/or participation to traffic with increased risk of collision).
ETSI is considered to be analogous to the claimed invention in that they both pertain to determining a danger level for predicted behavior of traffic participants for use with ITS communications. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of ETSI with the method as disclosed by Strohbeck where doing so advantageously improves the accuracy of operations by increasing understanding of potential dangers or risks with the predicted behaviors, for example.
Regarding claim 16, although in the second column of page 42 Strohbeck discloses that each predicted path for the encoding is assigned a probability, i.e. how likely it is that the respective path will be taken, and where covariance matrices can be used to further assess the accuracy of the predicted path, which is important, e.g. for calculating risks of collision, Strohbeck does not explicitly disclose wherein the item of information characterizing the predicted behavior comprises a difficulty level representing a difficulty for predicting the behavior of the perceived object.
However, ETSI teaches wherein the item of information characterizing the predicted behavior comprises a difficulty level representing a difficulty for predicting the behavior of the perceived object (In section 7.2, ETSI teaches that when a predicted behaviour is indicated, it will be necessary also to provide the level of confidence associated to this prediction, where the level of confidence can be related to movement parameters (e.g. the mobile object velocity), but also to knowledge that may be acquired (via learning) of regular VRU behaviours).
ETSI is considered to be analogous to the claimed invention in that they both pertain to determining a difficulty associated with predicting traffic participant behaviour in an ITS communication setting. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of ETSI with the method as disclosed by Strohbeck, where doing so advantageously improves accuracy by denoting how confident the predictions are to correctly reflecting future behaviours of the object, thereby improving accuracy of operations which are performed in response to the determinations for example.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fujita (US 12,293,662 B2) teaches a wireless roadside device, traffic communication system, and traffic communication method.
Alfano (US 2022/0068120 A1) teaches a method and apparatus for providing road user alerts.
Doig (US 2019/0339082 A1) teaches a method and system for hybrid collective perception and map crowdsourcing.
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/HARRISON HEFLIN/ Examiner, Art Unit 3665
/FREDERICK M BRUSHABER/ Primary Examiner, Art Unit 3665